1.4k citations · 1.8k across the 20 of their papers we have counts for
22 papers
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to lea…
Deep Axial Hypercomplex Networks
Nazmul Shahadat, Anthony S. Maida
Over the past decade, deep hypercomplex-inspired networks have enhanced feature extraction for image classification by enabling weight sharing across input channels. Recent works m…
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication
Nazmul Shahadat, Anthony S. Maida
Recently, many deep networks have introduced hypercomplex and related calculations into their architectures. In regard to convolutional networks for classification, these enhanceme…
Deep Residual Axial Networks
Nazmul Shahadat, Anthony S. Maida
While convolutional neural networks (CNNs) demonstrate outstanding performance on computer vision tasks, their computational costs remain high. Several techniques are used to reduc…
Vision-Based American Sign Language Classification Approach via Deep Learning
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) i…
LiteLSTM Architecture for Deep Recurrent Neural Networks
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power fo…
Improving Axial-Attention Network Classification via Cross-Channel Weight Sharing
Nazmul Shahadat, Anthony S. Maida
In recent years, hypercomplex-inspired neural networks (HCNNs) have been used to improve deep learning architectures due to their ability to enable channel-based weight sharing, tr…
Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction
Md Fazle Rabby, Yazhou Tu, Md Imran Hossen +3
Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep…
Generalizing Complex/Hyper-complex Convolutions to Vector Map Convolutions
Chase J Gaudet, Anthony S Maida
We show that the core reasons that complex and hypercomplex valued neural networks offer improvements over their real-valued counterparts is the weight sharing mechanism and treati…
Hierarchical Predictive Coding Models in a Deep-Learning Framework
Matin Hosseini, Anthony Maida
Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscienc…
Inception-inspired LSTM for Next-frame Video Prediction
Matin Hosseini, Anthony S. Maida, Majid Hosseini +1
The problem of video frame prediction has received much interest due to its relevance to many computer vision applications such as autonomous vehicles or robotics. Supervised metho…
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that rep…
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi
Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that us…
Deep Learning in Spiking Neural Networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh +2
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural ne…
Deep Quaternion Networks
Chase Gaudet, Anthony Maida
The field of deep learning has seen significant advancement in recent years. However, much of the existing work has been focused on real-valued numbers. Recent work has shown that…
BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
Amirhossein Tavanaei, Anthony S. Maida
The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has s…
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
Amirhossein Tavanaei, Anthony Maida
Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised…
Representation Learning using Event-based STDP
Amirhossein Tavanaei, Timothee Masquelier, Anthony Maida
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a ch…
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Amirhossein Tavanaei, Anthony S. Maida
Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is sti…
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
Amirhossein Tavanaei, Anthony S Maida
It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. Th…
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
Amirhossein Tavanaei, Timothee Masquelier, Anthony S Maida
The final version of this paper has been published in IEEEXplore available at http://ieeexplore.ieee.org/document/7727213. Please cite this paper as: Amirhossein Tavanaei, Timothee…
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals
Amirhossein Tavanaei, Anthony S Maida
Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynami…