Deep Learning in Neural Networks: An Overview

Abstract

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

88 pages, 888 references

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• Discriminative Regularization for Generative Models
• Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
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• Deep learning approach to control of prosthetic hands with electromyography signals
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• Machine Learning for Vehicular Networks
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• Understanding and Improving Fast Adversarial Training
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• Addressing Overfitting on Pointcloud Classification using Atrous XCRF
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• Artificial intelligence and machine learning applications for cultured meat
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• The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars
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• Defining Explainable AI for Requirements Analysis
• Setting up experimental Bell test with reinforcement learning
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• Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
• On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
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• Application of machine learning in the determination of impact parameter in the ${}^{132}$Sn+${}^{124}$Sn system
• How higher goals are constructed and collapse under stress: a hierarchical Bayesian control systems perspective
• Arcades: A deep model for adaptive decision making in voice controlled smart-home
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• Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
• Identifying safe intersection design through unsupervised feature extraction from satellite imagery
• Connections Between Adaptive Control and Optimization in Machine Learning
• Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks
• Compressing deep neural networks by matrix product operators
• Identifying the elastic isotropy of architectured materials based on deep learning method
• Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
• Applying Deep Learning to Basketball Trajectories
• Kandinsky Patterns
• Visual pathways from the perspective of cost functions and multi-task deep neural networks
• Laplacian Smoothing Gradient Descent
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• Reinforcement-learning-based control of convectively-unstable flows
• Asteroseismic determination of fundamental parameters of sun-like stars using multi-layered neural networks
• Deepr: A Convolutional Net for Medical Records
• Encrypted Internet traffic classification using a supervised Spiking Neural Network
• iNNvestigate neural networks!
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• Machine learning reveals complex behaviours in optically trapped particles
• A Framework for Parallel and Distributed Training of Neural Networks
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• CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi
• NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains
• Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
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• Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks
• Richness of Deep Echo State Network Dynamics
• Bayesian graph convolutional neural networks via tempered MCMC
• RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks
• Cross-Domain Few-Shot Learning by Representation Fusion
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• ProbAct: A Probabilistic Activation Function for Deep Neural Networks
• Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
• Controllable Data Generation by Deep Learning: A Review
• A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
• Transport-based analysis, modeling, and learning from signal and data distributions
• Probabilistic Circuits for Autonomous Learning: A simulation study
• Data-driven Stabilization of SISO Feedback Linearizable Systems
• A multiscale neural network based on hierarchical matrices
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• Machine-learning assisted quantum control in random environment
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• Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
• Constraining the Thermal Properties of Planetary Surfaces using Machine Learning: Application to Airless Bodies
• Binary classification of proteins by a Machine Learning approach
• Neural Language Modeling by Jointly Learning Syntax and Lexicon
• Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
• Deep Learning for Scene Classification: A Survey
• Artificial neural network subgrid models of 2-D compressible magnetohydrodynamic turbulence
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• The SP theory of intelligence: distinctive features and advantages
• A Review on Machine Learning for Neutrino Experiments
• Correspondence between neuroevolution and gradient descent
• Robust and Efficient Single-Pixel Image Classificationwith Nonlinear Optics
• Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation
• Advances in Machine Learning for the Behavioral Sciences
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• AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks
• Deep learning techniques applied to the physics of extensive air showers
• Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• A deep learning pipeline for identification of motor units in musculoskeletal ultrasound
• Deep Learning in Bioinformatics
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Steganalysis via a Convolutional Neural Network using Large Convolution Filters for Embedding Process with Same Stego Key
• SPP-CNN: An Efficient Framework for Network Robustness Prediction
• DeepHealth: Review and challenges of artificial intelligence in health informatics
• The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
• MetaGrad: Multiple Learning Rates in Online Learning
• Meta-Learning Mean Functions for Gaussian Processes
• A semi-supervised approach to dark matter searches in direct detection data with machine learning
• Physical Accuracy of Deep Neural Networks for 2D and 3D Multi-Mineral Segmentation of Rock micro-CT Images
• Crossing the principle-practice gap in AI ethics with ethical problem-solving
• Eye-Movement behavior identification for AD diagnosis
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• On Random Matrices Arising in Deep Neural Networks. Gaussian Case
• Deep Learning for Apple Diseases: Classification and Identification
• Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
• CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space
• HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
• From handcrafted to deep local features
• A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
• Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
• 1-Dimensional polynomial neural networks for audio signal related problems
• Bayesian Neural Networks for Fast SUSY Predictions
• Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
• RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search
• Training Agents using Upside-Down Reinforcement Learning
• Applying Transfer Learning To Deep Learned Models For EEG Analysis
• On the rate of convergence of fully connected very deep neural network regression estimates
• Machine Learning technique for isotopic determination of radioisotopes using HPGe $\gamma$-ray spectra
• Detecting Chiral Magnetic Effect via Deep Learning
• Mixed Precision Fermi-Operator Expansion on Tensor Cores From a Machine Learning Perspective
• Stochastic Training of Residual Networks: a Differential Equation Viewpoint
• Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations
• Can neural quantum states learn volume-law ground states?
• Galaxy merger challenge: A comparison study between machine learning-based detection methods
• Learning to Play Othello with Deep Neural Networks
• iRoPro: An interactive Robot Programming Framework
• Quantifying the Alignment of Graph and Features in Deep Learning
• General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation
• Deep Learning in Mining Biological Data
• Geology prediction based on operation data of TBM: comparison between deep neural network and statistical learning methods
• Machine learning reconstruction of depth-dependent thermal conductivity profile from pump-probe thermoreflectance signals
• Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
• Data augmentation and feature selection for automatic model recommendation in computational physics
• Australia's long-term electricity demand forecasting using deep neural networks
• Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets
• BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
• The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting
• The Neutron Star Outer Crust Equation of State: A Machine Learning approach
• Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
• Spike Event Based Learning in Neural Networks
• Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data
• Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks
• Soft-Root-Sign Activation Function
• Towards a universal mechanism for successful deep learning
• Judging a Book By its Cover
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• Sperm Detection and Tracking in Phase-Contrast Microscopy Image Sequences using Deep Learning and Modified CSR-DCF
• Aedes-AI: Neural Network Models of Mosquito Abundance
• Power-Enhanced Residual Network for Function Approximation and Physics-Informed Inverse Problems
• A Novel Approach Based Deep RNN Using Hybrid NARX-LSTM Model For Solar Power Forecasting
• A review of machine learning in processing remote sensing data for mineral exploration
• ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
• Solving for high dimensional committor functions using artificial neural networks
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• A Time Series Approach To Player Churn and Conversion in Videogames
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• Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part II
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• Random batch methods (RBM) for interacting particle systems
• Solutions to problems with deep learning
• Deep Stochastic Configuration Networks with Universal Approximation Property
• Online Distillation with Continual Learning for Cyclic Domain Shifts
• Deep Learning in Information Security
• Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
• Multi-task neural networks by learned contextual inputs
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• Local Propagation in Constraint-based Neural Network
• Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods
• Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning
• Deep learning of transition probability densities for stochastic asset models with applications in option pricing
• DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
• At-Scale Evaluation of Weight Clustering to Enable Energy-Efficient Object Detection
• Emulating complex networks with a single delay differential equation
• The 'Paris-end' of town? Urban typology through machine learning
• Reduced-order Deep Learning for Flow Dynamics
• Using stigmergy to incorporate the time into artificial neural networks
• The Future of Data Analysis in the Neurosciences
• Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks
• Generative adversarial networks for generation and classification of physical rehabilitation movement episodes
• Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
• A Cloud-Based Energy Management Strategy for Hybrid Electric City Bus Considering Real-Time Passenger Load Prediction
• Multi-scale aggregation of phase information for reducing computational cost of CNN based DOA estimation
• Vision System Prototype for Inspection and Monitoring with a Smart Camera
• Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks
• Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
• Tricks from Deep Learning
• Data Collection versus Data Estimation: A Fundamental Trade-off in Dynamic Networks
• On the rate of convergence of a neural network regression estimate learned by gradient descent
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
• Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown
• Interpolating between boolean and extremely high noisy patterns through Minimal Dense Associative Memories
• Fitting random cash management models to data
• Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection
• Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
• DELTA: A DEep learning based Language Technology plAtform
• A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
• SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper
• A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization
• AIXIjs: A Software Demo for General Reinforcement Learning
• Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
• Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its Realisation in the SP Computer Model
• MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
• Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture
• An Integrated Deep Learning and Dynamic Programming Method for Predicting Tumor Suppressor Genes, Oncogenes, and Fusion from PDB Structures
• Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry
• Sparse-View Spectral CT Reconstruction Using Deep Learning
• AutoSourceID-FeatureExtractor. Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
• RNN-DAS: A New Deep Learning Approach for Detection and Real-Time Monitoring of Volcano-Tectonic Events Using Distributed Acoustic Sensing
• Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Conduction
• Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms
• Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
• Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
• Orthogonality of Diffractive Deep Neural Networks
• Noisy Computations during Inference: Harmful or Helpful?
• Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines
• CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
• Deep Online Convex Optimization with Gated Games
• Machine Learning and Social Robotics for Detecting Early Signs of Dementia
• Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games
• Arabic aspect based sentiment analysis using bidirectional GRU based models
• Deep Learning in Robotics: A Review of Recent Research
• Array Languages Make Neural Networks Fast
• Multi-label Ranking: Mining Multi-label and Label Ranking Data
• Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
• On Geometric Structure of Activation Spaces in Neural Networks
• Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
• Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems
• A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks
• Classifying textual data: shallow, deep and ensemble methods
• CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images
• Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces
• Numerically Recovering the Critical Points of a Deep Linear Autoencoder
• Short-Term Traffic Flow Prediction Using Variational LSTM Networks
• Toward the Starting Line: A Systems Engineering Approach to Strong AI
• Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization
• Recent Deep Semi-supervised Learning Approaches and Related Works
• Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods
• A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems
• Towards a Cognitive Computational Neuroscience of Auditory Phantom Perceptions
• Rectified Factor Networks
• One Big Net For Everything
• Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
• Numerical approaches for investigating quasiconvexity in the context of Morrey's conjecture
• Sketchy Empirical Natural Gradient Methods for Deep Learning
• Prediction of Prokaryotic and Eukaryotic Promoters Using Convolutional Deep Learning Neural Networks
• A comparable study of modeling units for end-to-end Mandarin speech recognition
• Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images
• Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
• Entity Matching by Pool-based Active Learning
• Factorized Adversarial Networks for Unsupervised Domain Adaptation
• Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback
• Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)
• Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
• Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
• Classification via local manifold approximation
• Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC
• Large Scale Artificial Neural Network Training Using Multi-GPUs
• ExAIS: Executable AI Semantics
• On the Spectrum of Random Features Maps of High Dimensional Data
• New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture
• Visualizing Multidimensional Linear Programming Problems
• The MIT Supercloud Workload Classification Challenge
• Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques
• Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
• Intelligent Recognition of Time Stamp Characters in Solar Scanned Images from Film
• Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
• Motif Identification using CNN-based Pairwise Subsequence Alignment Score Prediction
• Translating Diffusion, Wavelets, and Regularisation into Residual Networks
• Deep Dimension Reduction for Supervised Representation Learning
• Towards a theory of machine learning
• Randomized Overdrive Neural Networks
• Computational Eco-Systems for Handwritten Digits Recognition
• C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
• Advanced deep architecture pruning using single filter performance
• Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF
• Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction
• Using stigmergy as a computational memory in the design of recurrent neural networks
• Detecting Dead Weights and Units in Neural Networks
• Auto-Classifier: A Robust Defect Detector Based on an AutoML Head
• A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
• CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications
• DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
• Open DNN Box by Power Side-Channel Attack
• Efficient Graphics Representation with Differentiable Indirection
• Deep vs. Diverse Architectures for Classification Problems
• Real-time interactive sequence generation and control with Recurrent Neural Network ensembles
• A representer theorem for deep neural networks
• Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model
• Introduction to the SP theory of intelligence
• A Personalized Affective Memory Neural Model for Improving Emotion Recognition
• Semi-supervised Learning with Sparse Autoencoders in Phone Classification
• On Expressivity and Trainability of Quadratic Networks
• Finding trainable sparse networks through Neural Tangent Transfer
• A Study of Checkpointing in Large Scale Training of Deep Neural Networks
• Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
• Semantic Segmentation of Skin Lesions using a Small Data Set
• Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
• Predicting Natural Hazards with Neuronal Networks
• What are the most important statistical ideas of the past 50 years?
• A non iterative method of separation of points by planes in n dimensions and its application
• A Model for Auto-Programming for General Purposes
• Towards Building Deep Networks with Bayesian Factor Graphs
• Spectral and Dynamic Consequences of Network Specialization
• Neural Pruning via Growing Regularization
• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
• MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions
• A new role for circuit expansion for learning in neural networks
• End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands
• Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data
• Prediction intervals for Deep Neural Networks
• Ensembles of Spiking Neural Networks
• Tunable Quantum Neural Networks for Boolean Functions
• Large-scale image analysis using docker sandboxing
• MC-LSTM: Mass-Conserving LSTM
• Hierarchical Expert Networks for Meta-Learning
• DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future
• Learning by Design: Structuring and Documenting the Human Choices in Machine Learning Development
• Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning
• Knowledge transfer in deep block-modular neural networks
• Eigenvalue Distribution of Large Random Matrices Arising in Deep Neural Networks: Orthogonal Case
• An energy-based deep splitting method for the nonlinear filtering problem
• Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)
• Neural Greedy Pursuit for Feature Selection
• Do Kernel and Neural Embeddings Help in Training and Generalization?
• Learning biological neuronal networks with artificial neural networks: neural oscillations
• A Mathematical Framework for Learning Probability Distributions
• Learning Numeracy: Binary Arithmetic with Neural Turing Machines
• Trained quantum neural networks are Gaussian processes
• Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition
• Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
• Learning from power system data stream: phasor-detective approach
• R-process heating implementation in hydrodynamic simulations with neural networks
• Deep Unfolding of a Proximal Interior Point Method for Image Restoration
• Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
• Control of dynamical systems with neural networks
• Prediction of Industrial Process Parameters using Artificial Intelligence Algorithms
• A model of cortical cognitive function using hierarchical interactions of gating matrices in internal agents coding relational representations
• DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder
• PhD Dissertation: Generalized Independent Components Analysis Over Finite Alphabets
• FRUGAL: Unlocking SSL for Software Analytics
• Piecewise Linear Units Improve Deep Neural Networks
• Deep Global Model Reduction Learning in Porous Media Flow Simulation
• Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging
• Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective
• Reduced order modeling of dynamical systems using artificial neural networks applied to water circulation
• Time series analysis with dynamic law exploration
• Efficient UAV Physical Layer Security based on Deep Learning and Artificial Noise
• An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
• Exploiting Contextual Information with Deep Neural Networks
• Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning
• Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation
• A Double-Layered Framework for Distributed Coordination in Solving Linear Equations
• Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations
• Decision Explanation and Feature Importance for Invertible Networks
• Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting
• Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
• Differential Dynamic Programming for time-delayed systems
• Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance
• Evolving Self-supervised Neural Networks: Autonomous Intelligence from Evolved Self-teaching
• M-GWAP: An Online and Multimodal Game With A Purpose in WordPress for Mental States Annotation
• Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side
• Deep Online Convex Optimization by Putting Forecaster to Sleep
• Computational Cost Reduction in Learned Transform Classifications
• Machine learning approach for the search of resonances with topological features at the Large Hadron Collider
• What's in the box? Explaining the black-box model through an evaluation of its interpretable features
• Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
• EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
• Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network
• Smaller Models, Better Generalization
• Training Stacked Denoising Autoencoders for Representation Learning
• Training Feedforward Neural Networks with Standard Logistic Activations is Feasible
• An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning
• Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques
• Semantics, Representations and Grammars for Deep Learning
• ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid
• Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video
• Google Auto ML versus Apple Create ML for Histopathologic Cancer Diagnosis; Which Algorithms Are Better?
• Deep Gaussian Mixture Models
• Surprising properties of dropout in deep networks
• Estimating Nonlinear Dynamics with the ConvNet Smoother
• An efficient optimization based microstructure reconstruction approach with multiple loss functions
• Adversarial Robustness Guarantees for Random Deep Neural Networks
• Learning Based on CC1 and CC4 Neural Networks
• A relativistic extension of Hopfield neural networks via the mechanical analogy
• On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics
• Implicit Feedback Deep Collaborative Filtering Product Recommendation System
• Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data
• Identification of Indian Languages using Ghost-VLAD pooling
• Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
• Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey
• CAT STREET: Chronicle Archive of Tokyo Street-fashion
• Cognitive Deep Machine Can Train Itself
• NCoder -- A Quantum Field Theory approach to encoding data
• A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
• Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
• Reducing catastrophic forgetting when evolving neural networks
• Variational Information Bottleneck for Effective Low-resource Audio Classification
• Master's Thesis : Deep Learning for Visual Recognition
• Tensor Switching Networks
• Deep learning of parameterized equations with applications to uncertainty quantification
• Music Generation with Temporal Structure Augmentation
• Analysis of structure and dynamics in three-neuron motifs
• Convolutional Support Vector Machine
• Hybrid Piezoelectric-Magnetic Neurons: A Proposal for Energy-Efficient Machine Learning
• Tabular Image: a method to convert tabular data to images for convolutional neural networks
• Flexible Operator Embeddings via Deep Learning
• Evolving neural networks to follow trajectories of arbitrary complexity
• Dynamic Key-Value Memory Networks for Knowledge Tracing
• Impact of Training Dataset Size on Neural Answer Selection Models
• Dissipative learning of a quantum classifier
• Machine Learning Promoting Extreme Simplification of Spectroscopy Equipment
• Recovering gaps in the gamma-ray logging method
• HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization
• Convergence Analysis of a Cooperative Diffusion Gauss-Newton Strategy
• Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
• Recent Advances in Convolutional Neural Network Acceleration
• Retro Drug Design: From Target Properties to Molecular Structures
• A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting
• Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
• Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents
• BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
• A Unified Framework of Deep Neural Networks by Capsules
• A Deep-learning-based Joint Inference for Secure Spatial Modulation Receiver
• Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform
• CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
• MLR (Memory, Learning and Recognition): A General Cognitive Model -- applied to Intelligent Robots and Systems Control
• Role of Delay in Brain Dynamics
• Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey
• What does LIME really see in images?
• QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
• Dynamic Weight Alignment for Temporal Convolutional Neural Networks
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
• Attention-Based End-to-End Speech Recognition on Voice Search
• Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
• Multi-layer Kernel Ridge Regression for One-class Classification
• Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations
• Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
• Random Polytope Descriptors
• A multiscale neural network based on hierarchical nested bases
• A Brief Survey of Associations Between Meta-Learning and General AI
• Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
• Scalable Multiagent Coordination with Distributed Online Open Loop Planning
• Time-Reversal Symmetric ODE Network
• Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions
• AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings
• General Backpropagation Algorithm for Training Second-order Neural Networks
• Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
• Problems in AI research and how the SP System may help to solve them
• Training Recurrent Neural Networks against Noisy Computations during Inference
• Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
• Recovering Localized Adversarial Attacks
• Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible to Various Temporal Codes
• Nonparametric signal separation in very-high-energy gamma ray observations with probabilistic neural networks
• Introduction to a novel T2 relaxation analysis method SAME-ECOS: Spectrum Analysis for Multiple Exponentials via Experimental Condition Oriented Simulation
• Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
• Soft-Deep Boltzmann Machines
• Solving Sinhala Language Arithmetic Problems using Neural Networks
• Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
• Discriminative Learning of Similarity and Group Equivariant Representations
• Semantic Segmentation for Urban Planning Maps based on U-Net
• Machines hear better when they have ears
• Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks
• Neural Networks for cosmological model selection and feature importance using Cosmic Microwave Background data
• Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer
• Exoplanet Cartography using Convolutional Neural Networks
• What Can Be Predicted from Six Seconds of Driver Glances?
• An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks
• Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
• Analysis of the rate of convergence of neural network regression estimates which are easy to implement
• Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural Networks
• DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction
• Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information
• MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders
• A Deep Generative Approach to Conditional Sampling
• Estimating the Robustness of Public Transport Systems Using Machine Learning
• Bifurcation Spiking Neural Network
• Continual Learning for Affective Computing
• Deep learning bank distress from news and numerical financial data
• Backward Gradient Normalization in Deep Neural Networks
• Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows
• Auto-tuning of Deep Neural Networks by Conflicting Layer Removal
• Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep Neural Networks
• Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
• Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
• Learning without gradient descent encoded by the dynamics of a neurobiological model
• ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
• A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs
• QUDOS: Quorum-Based Cloud-Edge Distributed DNNs for Security Enhanced Industry 4.0
• Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems
• Multi-task Learning for Financial Forecasting
• Analyzing GPU Tensor Core Potential for Fast Reductions
• A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural Network
• Deep Octonion Networks
• Deep Learning: Our Miraculous Year 1990-1991
• Stochastic optimization methods for the simultaneous control of parameter-dependent systems
• Towards a Cybernetic Foundation for Natural Resource Governance
• Generalized Convolution and Efficient Language Recognition
• Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
• Parameter-Based Value Functions
• Seismic Inversion by Newtonian Machine Learning
• An Analytic Layer-wise Deep Learning Framework with Applications to Robotics
• Multi-layered tensor networks for image classification
• Multi-level neural networks for PDEs with uncertain parameters
• Instance Explainable Temporal Network For Multivariate Timeseries
• Vehicle Detection in Deep Learning
• Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
• PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification
• Integration of TensorFlow based Acoustic Model with Kaldi WFST Decoder
• A shallow residual neural network to predict the visual cortex response
• Recursive Least Squares Based Refinement Network for the Rollout Trajectory Prediction Methods
• Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
• How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning
• Phasic dopamine release identification using ensemble of AlexNet
• Search in Imperfect Information Games
• Sensitivity Analysis of Lift and Drag Coefficients for Flow over Elliptical Cylinders of Arbitrary Aspect Ratio and Angle of Attack using Neural Network
• Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networks
• Minimal Neural Network Models for Permutation Invariant Agents
• Learning event representation: As sparse as possible, but not sparser
• Towards Robust Pattern Recognition: A Review
• Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems
• Big Data For Development: Applications and Techniques
• A generic and fast C++ optimization framework
• Happiness Pursuit: Personality Learning in a Society of Agents
• Lightweight Neural Networks
• A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models
• Binary output layer of feedforward neural networks for solving multi-class classification problems
• Globally Optimal Object Tracking with Fully Convolutional Networks
• Geometry matters: Exploring language examples at the decision boundary
• The Wilson Machine for Image Modeling
• Identification of Neuronal Polarity by Node-Based Machine Learning
• Temperature-Based Deep Boltzmann Machines
• Density Estimation via Binless Multidimensional Integration
• Two-level monotonic multistage recommender systems
• Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network
• Constraining Galaxy-Halo Connection Using Machine Learning
• ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
• TTR-Based Reward for Reinforcement Learning with Implicit Model Priors
• Experiential Robot Learning with Accelerated Neuroevolution
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music
• Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices
• Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
• Frequency Disentangled Residual Network
• Tiny language models
• Adaptive Eigenspace Segmentation
• Data-Rate Driven Transmission Strategy for Deep Learning Based Communication Systems
• Computational Graph Approach for Detection of Composite Human Activities
• Digital video microscopy enhanced by deep learning
• Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity prediction
• Spectrally Consistent UNet for High Fidelity Image Transformations
• Robust Optimization for Deep Regression
• Non-iterative recomputation of dense layers for performance improvement of DCNN
• Pushing the boundaries of parallel Deep Learning -- A practical approach
• Deep Learning in Science
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• Graceful Forgetting II. Data as a Process
• Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network
• New approaches for faint source detection in hard X-ray surveys
• Shallow Transits -- Deep Learning II: Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
• SDN Flow Entry Management Using Reinforcement Learning
• Identification of Coronal Holes on AIA/SDO images using unsupervised Machine Learning
• Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks
• Learning to Win, Lose and Cooperate through Reward Signal Evolution
• Improving Compositionality of Neural Networks by Decoding Representations to Inputs
• Drivers' Manoeuvre Modelling and Prediction for Safe HRI
• Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks
• MMDF2018 Workshop Report
• An induction proof of the backpropagation algorithm in matrix notation
• Robust Trimmed k-means
• Zoom, Enhance! Measuring Surveillance GAN Up-sampling
• A Generative Model for Multi-Dialect Representation
• Deep Convolutional Neural Networks Predict Elasticity Tensors and their Bounds in Homogenization
• Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding
• Active Perception with Neural Networks
• Deep Neural Network for Analysis of DNA Methylation Data
• Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose
• Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
• Enhancing Clinical Information Extraction with Transferred Contextual Embeddings
• Convolutional Deep Exponential Families
• Frugal Machine Learning
• Explainable, automated urban interventions to improve pedestrian and vehicle safety
• Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography
• Long Short-Term Memory Neuron Equalizer
• Multi-Perspective Neural Architecture for Recommendation System
• DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins
• Artificial Intelligence and its impact on the Fourth Industrial Revolution: A Review
• Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks
• On tuning deep learning models: a data mining perspective
• Towards Distortion-Predictable Embedding of Neural Networks
• AI-Supported Assessment of Load Safety
• A deep network approach to multitemporal cloud detection
• Assessing The Importance Of Colours For CNNs In Object Recognition
• The Foundations of Deep Learning with a Path Towards General Intelligence
• Hierarchical Recurrent Attention Networks for Structured Online Maps
• Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
• A design of human-like robust AI machines in object identification
• Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
• Understanding in Artificial Intelligence
• Reformulating van Rijsbergen's $F_{\beta }$ metric for weighted binary cross-entropy
• DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues
• Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning
• Network Learning with Local Propagation
• A PAC-Bayes oracle inequality for sparse neural networks
• Fault Diagnosis of Rotary Machines using Deep Convolutional Neural Network with three axis signal input
• Reducing Parameter Space for Neural Network Training
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks
• Reliable counting of weakly labeled concepts by a single spiking neuron model
• Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks
• Representation Learning in Partially Observable Environments using Sensorimotor Prediction
• Physical discovery in representation learning via conditioning on prior knowledge: applications for ferroelectric domain dynamics
• Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images
• Cellular automata can classify data by inducing trajectory phase coexistence
• Modeling the Nonsmoothness of Modern Neural Networks
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• Finite Difference Nets: A Deep Recurrent Framework for Solving Evolution PDEs
• Planning with Expectation Models for Control
• Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets
• PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
• Predicting the Porosity Formed in Freeze Casting by Artificial Neural Network
• Deep Hierarchical Classification for Category Prediction in E-commerce System
• BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
• Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks
• Bridging Cognitive Programs and Machine Learning
• On the Existence of Universal Lottery Tickets
• Multimodal fusion for sea level anomaly forecasting
• No one-hidden-layer neural network can represent multivariable functions
• An affective computational model for machine consciousness
• DeepProcess: Supporting business process execution using a MANN-based recommender system
• RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning
• Deep Fiducial Inference
• Data-driven modelling and characterisation of task completion sequences in online courses
• A Novel Co-design Peta-scale Heterogeneous Cluster for Deep Learning Training
• Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
• Deep Controllable Backlight Dimming
• Online Spatiotemporal Action Detection and Prediction via Causal Representations
• Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations
• Empirical Strategy for Stretching Probability Distribution in Neural-network-based Regression
• A Deep Hybrid Model for Recommendation Systems
• An Ultra Lightweight CNN for Low Resource Circuit Component Recognition
• How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text
• MFPC-Net: Multi-fidelity Physics-Constrained Neural Process
• Nearly Optimal Learning using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization with Lipschitz Loss
• The Separation Capacity of Random Neural Networks
• Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector
• Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals
• Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases
• A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component
• Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
• Dealing with overdispersion in multivariate count data
• Dynamic Time Warp Convolutional Networks
• Review of Quantitative Systems Pharmacological Modeling in Thrombosis
• Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks
• Weight Friction: A Simple Method to Overcome Catastrophic Forgetting and Enable Continual Learning
• The many faces of deep learning
• An Outer-approximation Guided Optimization Approach for Constrained Neural Network Inverse Problems
• Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
• Deep learning of nanopore sensing signals using a bi-path network
• On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience
• A Logarithmic Barrier Method For Proximal Policy Optimization
• Learning formation energy of inorganic compounds using matrix variate deep Gaussian process
• Image Synthesis and Style Transfer
• Reducing state updates via Gaussian-gated LSTMs
• Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees
• TUNet: Incorporating segmentation maps to improve classification
• A real-time hourly ozone prediction system using deep convolutional neural network
• Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
• Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning
• Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms
• Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition
• A block-random algorithm for learning on distributed, heterogeneous data
• Predicting publication productivity for authors: Shallow or deep architecture?
• Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search
• Deep recommender engine based on efficient product embeddings neural pipeline
• Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities
• Stability of the Decoupled Extended Kalman Filter Learning Algorithm in LSTM-Based Online Learning
• Deep graph embedding for prioritizing synergistic anticancer drug combinations
• Quality-aware Unpaired Image-to-Image Translation
• Network Horizon Dynamics I: Qualitative Aspects
• Nearest-Neighbor Neural Networks for Geostatistics
• PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
• Low-Rank Deep Convolutional Neural Network for Multi-Task Learning
• Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)
• Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction
• GaDei: On Scale-up Training As A Service For Deep Learning
• The Fourier Discrepancy Function
• Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
• Aortic Pressure Forecasting with Deep Sequence Learning
• An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks
• Research on Bi-mode Biometrics Based on Deep Learning
• RoboCup 2D Soccer Simulation League: Evaluation Challenges
• Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features
• A Symmetric Loss Perspective of Reliable Machine Learning
• Learning Robust Representations for Computer Vision
• Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation
• Infinite-dimensional Folded-in-time Deep Neural Networks
• Locally Imposing Function for Generalized Constraint Neural Networks - A Study on Equality Constraints
• Unsupervised Foveal Vision Neural Networks with Top-Down Attention
• Towards Machine Intelligence
• PCANet: An energy perspective
• Spiking neurons with short-term synaptic plasticity form superior generative networks
• Inferring Mesoscale Models of Neural Computation
• Separation of Water and Fat Magnetic Resonance Imaging Signals Using Deep Learning with Convolutional Neural Networks
• Understanding Information Processing in Human Brain by Interpreting Machine Learning Models
• Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
• (Yet) Another Theoretical Model of Thinking
• A Greedy Algorithm for Quantizing Neural Networks
• BigGAN-based Bayesian reconstruction of natural images from human brain activity
• Block-term Tensor Neural Networks
• Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel
• The SWAG Algorithm; a Mathematical Approach that Outperforms Traditional Deep Learning. Theory and Implementation
• Single-Nodal Spontaneous Symmetry Breaking in NLP Models
• Kalman Filter Modifier for Neural Networks in Non-stationary Environments
• DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph
• Uncertainty-Aware Neural Networks for Fuzzy Dark Matter Model Selection from \texorpdfstring{$x_{\mathrm{HI}}$}{x_HI} Measurements
• A Generalized Meta-loss function for regression and classification using privileged information
• Experience and Prediction: A Metric of Hardness for a Novel Litmus Test
• Characteristic time of transition from write error to retention error in voltage-controlled magnetoresistive random-access memory
• A Survey of Brain Inspired Technologies for Engineering
• Parameter Inference of Black Hole Images using Deep Learning in Visibility Space