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07/11/2023
Memorization Through the Lens of Curvature of Loss Function Around Samples
Neural networks are overparametrized and easily overfit the datasets the...
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01/21/2022
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data
Advances in Federated Learning and an abundance of user data have enable...
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12/20/2021
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift
Over the past decade, deep neural networks have proven to be adept in im...
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04/26/2021
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks
Spiking Neural Networks (SNNs) are a promising alternative to traditiona...
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03/17/2021
Gradient Projection Memory for Continual Learning
The ability to learn continually without forgetting the past tasks is a ...
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12/15/2020
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
Deep neural networks have found widespread adoption in solving complex t...
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10/05/2020
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
Spiking Neural Networks (SNNs) offer a promising alternative to traditio...
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08/04/2020
TREND: Transferability based Robust ENsemble Design
Deep Learning models hold state-of-the-art performance in many fields, b...
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01/23/2020
Structured Compression and Sharing of Representational Space for Continual Learning
Humans are skilled at learning adaptively and efficiently throughout the...
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06/04/2019
PCA-driven Hybrid network design for enabling Intelligence at the Edge
The recent advent of IOT has increased the demand for enabling AI-based ...
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12/15/2018
