activity
20172021
most citedBio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

39 citations · 53 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG20214 cited

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…

cs.NE20202 cited

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…

cs.CV20208 cited

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…

cs.CV2019

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…

cs.LG2018

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…

cs.LG2018

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…