research
∙
01/12/2023
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Long short-term memory (LSTM) is one of the robust recurrent neural netw...
share
research
∙
01/11/2023
Deep Residual Axial Networks
While residual networks (ResNets) demonstrate outstanding performance on...
research
∙
01/11/2023
Deep Axial Hypercomplex Networks
Over the past decade, deep hypercomplex-inspired networks have enhanced ...
research
∙
01/11/2023
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication
Recently, many deep networks have introduced hypercomplex and related ca...
research
∙
04/08/2022
Vision-Based American Sign Language Classification Approach via Deep Learning
Hearing-impaired is the disability of partial or total hearing loss that...
research
∙
01/27/2022
LiteLSTM Architecture for Deep Recurrent Neural Networks
Long short-term memory (LSTM) is a robust recurrent neural network archi...
research
∙
10/04/2021
Adding Quaternion Representations to Attention Networks for Classification
This paper introduces a novel modification to axial-attention networks t...
research
∙
01/18/2021
Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction
Blood glucose (BG) management is crucial for type-1 diabetes patients re...
research
∙
09/09/2020
Generalizing Complex/Hyper-complex Convolutions to Vector Map Convolutions
We show that the core reasons that complex and hypercomplex valued neura...
research
∙
12/18/2018
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series clas...
research
∙
10/16/2018
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
Spatiotemporal sequence prediction is an important problem in deep learn...
research
∙
04/22/2018
Deep Learning in Spiking Neural Networks
Deep learning approaches have shown remarkable performance in many areas...
research
∙
11/12/2017
BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
The problem of training spiking neural networks (SNNs) is a necessary pr...
research
∙
11/09/2016
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Hierarchical feature discovery using non-spiking convolutional neural ne...
research
∙
06/03/2016
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
The final version of this paper has been published in IEEEXplore availab...
research
∙
06/02/2016
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
It is of some interest to understand how statistically based mechanisms ...
research
∙
06/02/2016
