5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
25 papers · 2 filters
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi +1
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an ex…
Randomly Projected Additive Gaussian Processes for Regression
Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson
Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle…
The State of Knowledge Distillation for Classification
Fabian Ruffy, Karanbir Chahal
We survey various knowledge distillation (KD) strategies for simple classification tasks and implement a set of techniques that claim state-of-the-art accuracy. Our experiments usi…
The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)
Ernest Davis
Lample and Charton (2019) describe a system that uses deep learning technology to compute symbolic, indefinite integrals, and to find symbolic solutions to first- and second-order…
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection
Urwa Muaz, Stanislav Sobolevsky
Unsupervised anomaly detection from high dimensional data like mobility networks is a challenging task. Study of different approaches of feature engineering from such high dimensio…
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
Sebastian Schelter, Yuxuan He, Jatin Khilnani +1
The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that crit…
Learning GANs and Ensembles Using Discrepancy
Ben Adlam, Corinna Cortes, Mehryar Mohri +1
Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions. The choice of that divergence is therefore critical. We argue that…
Function-Space Distributions over Kernels
Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey +2
Gaussian processes are flexible function approximators, with inductive biases controlled by a covariance kernel. Learning the kernel is the key to representation learning and stron…
Generalized Inner Loop Meta-Learning
Edward Grefenstette, Brandon Amos, Denis Yarats +6
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimiza…
Hamiltonian Graph Networks with ODE Integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer +1
We introduce an approach for imposing physically informed inductive biases in learned simulation models. We combine graph networks with a differentiable ordinary differential equat…
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
Owen Marschall, Kyunghyun Cho, Cristina Savin
We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN). The framework or…
Latent Multi-Criteria Ratings for Recommendations
Pan Li, Alexander Tuzhilin
Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However…
Modeling Severe Traffic Accidents With Spatial And Temporal Features
Devashish Khulbe, Soumya Sourav
We present an approach to estimate the severity of traffic related accidents in aggregated (area-level) and disaggregated (point level) data. Exploring spatial features, we measure…
Gradient Dynamics of Shallow Univariate ReLU Networks
Francis Williams, Matthew Trager, Claudio Silva +3
We present a theoretical and empirical study of the gradient dynamics of overparameterized shallow ReLU networks with one-dimensional input, solving least-squares interpolation. We…
Stability of Graph Scattering Transforms
Fernando Gama, Joan Bruna, Alejandro Ribeiro
Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of…
On the Expressive Power of Deep Polynomial Neural Networks
Joe Kileel, Matthew Trager, Joan Bruna
We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial neural network defines…
Variational Bayes: A report on approaches and applications
Manikanta Srikar Yellapragada, Chandra Prakash Konkimalla
Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models of…
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco +4
Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bay…
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo +1
It remains a puzzle that why deep neural networks (DNNs), with more parameters than samples, often generalize well. An attempt of understanding this puzzle is to discover implicit…
A COLD Approach to Generating Optimal Samples
Omar Mahmood, José Miguel Hernández-Lobato
Optimising discrete data for a desired characteristic using gradient-based methods involves projecting the data into a continuous latent space and carrying out optimisation in this…
Task-Driven Data Verification via Gradient Descent
Siavash Golkar, Kyunghyun Cho
We introduce a novel algorithm for the detection of possible sample corruption such as mislabeled samples in a training dataset given a small clean validation set. We use a set of…
Advancing GraphSAGE with A Data-Driven Node Sampling
Jihun Oh, Kyunghyun Cho, Joan Bruna
As an efficient and scalable graph neural network, GraphSAGE has enabled an inductive capability for inferring unseen nodes or graphs by aggregating subsampled local neighborhoods…
Generative Image Translation for Data Augmentation of Bone Lesion Pathology
Anant Gupta, Srivas Venkatesh, Sumit Chopra +1
Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we a…
Disentangling Video with Independent Prediction
William F. Whitney, Rob Fergus
We propose an unsupervised variational model for disentangling video into independent factors, i.e. each factor's future can be predicted from its past without considering the othe…