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Found 10 papers, 0 papers with code
Mem-elements based Neuromorphic Hardware for Neural Network Application
The model, implemented through a hybrid Python and PyTorch approach, accounts for various non-idealities, achieving exceptional training accuracies of 90. 02% and 91. 03% for the CIFAR-10 dataset with memristive and memcapacitive crossbar arrays on an 8-layer VGG network.
Analysis and Fully Memristor-based Reservoir Computing for Temporal Data Classification
Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals.
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning
This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks.
Robust Representation Learning with Self-Distillation for Domain Generalization
Despite the recent success of deep neural networks, there remains a need for effective methods to enhance domain generalization using vision transformers.
Semi-Supervised Super-Resolution
The proposed semi-supervised technique can be used as a plug-and-play module with any supervised GAN-based Super-Resolution method to enhance its performance.
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis
While medical image segmentation is an important task for computer aided diagnosis, the high expertise requirement for pixelwise manual annotations makes it a challenging and time consuming task.
The Curious Case of Convex Neural Networks
In this paper, we investigate a constrained formulation of neural networks where the output is a convex function of the input.
Adversarial Incremental Learning
Although deep learning performs really well in a wide variety of tasks, it still suffers from catastrophic forgetting -- the tendency of neural networks to forget previously learned information upon learning new tasks where previous data is not available.
Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth
Later on, in this paper we show how this technique can help to save bandwidth usage to upto three times while transmitting raw colored videos.
