27 citations · 37 across the 3 of their papers we have counts for
8 papers
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks
Sayeed Shafayet Chowdhury, Isha Garg, Kaushik Roy
Spiking Neural Networks (SNNs) are a promising alternative to traditional deep learning methods since they perform event-driven information processing. However, a major drawback of…
Gradient Projection Memory for Continual Learning
Gobinda Saha, Isha Garg, Kaushik Roy
The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial…
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
Deepak Ravikumar, Sangamesh Kodge, Isha Garg +1
Deep neural networks have found widespread adoption in solving complex tasks ranging from image recognition to natural language processing. However, these networks make confident m…
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
Isha Garg, Sayeed Shafayet Chowdhury, Kaushik Roy
Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven informa…
TREND: Transferability based Robust ENsemble Design
Deepak Ravikumar, Sangamesh Kodge, Isha Garg +1
Deep Learning models hold state-of-the-art performance in many fields, but their vulnerability to adversarial examples poses threat to their ubiquitous deployment in practical sett…
SPACE: Structured Compression and Sharing of Representational Space for Continual Learning
Gobinda Saha, Isha Garg, Aayush Ankit +1
Humans learn adaptively and efficiently throughout their lives. However, incrementally learning tasks causes artificial neural networks to overwrite relevant information learned ab…