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Found 19 papers, 5 papers with code
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data.
Deep Residual Axial Networks
The axial CNNs are predicated on the assumption that the dataset supports approximately separable convolution operations with little or no loss of training accuracy.
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication
Recently, many deep networks have introduced hypercomplex and related calculations into their architectures.
Deep Axial Hypercomplex Networks
We conduct experiments on CIFAR benchmarks, SVHN, and Tiny ImageNet datasets and achieve better performance with fewer trainable parameters and FLOPS.
Vision-Based American Sign Language Classification Approach via Deep Learning
Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society.
LiteLSTM Architecture for Deep Recurrent Neural Networks
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data.
Improving Axial-Attention Network Classification via Cross-Channel Weight Sharing
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, treat colors as a single entity, and improve representational coherence within the layers.
Removing Dimensional Restrictions on Complex/Hyper-complex Convolutions
It has been shown that the core reasons that complex and hypercomplex valued neural networks offer improvements over their real-valued counterparts is the fact that aspects of their algebra forces treating multi-dimensional data as a single entity (forced local relationship encoding) with an added benefit of reducing parameter count via weight sharing.
Generalizing Complex/Hyper-complex Convolutions to Vector Map Convolutions
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 treating multidimensional data as a single entity.
Inception-inspired LSTM for Next-frame Video Prediction
The proposed Inception LSTM methods are compared with convolutional LSTM when applied using PredNet predictive coding framework for both the KITTI and KTH data sets.
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series.
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
Our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time.
Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction
Spatiotemporal sequence prediction is an important problem in deep learning.
Deep Learning in Spiking Neural Networks
In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP.
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Kernels for the convolutional layer are trained using local learning.
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery.
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals
Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks.
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule.
