2 citations · 2 across the 6 of their papers we have counts for
6 papers
In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
Benjamin Smith, Levin Kuhlmann, Kaushik Roy +1
Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological lea…
MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
Ankita Awasthi, Marco Apolinario, Kaushik Roy
In Online Continual Learning (OCL), a neural network sequentially learns from a non-stationary data stream in a single-pass with access only to a limited memory replay buffer. This…
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression
Utkarsh Saxena, Gobinda Saha, Sakshi Choudhary +1
Large language models (LLMs) represent a groundbreaking advancement in the domain of natural language processing due to their impressive reasoning abilities. Recently, there has be…
SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustness
Sangamesh Kodge, Deepak Ravikumar, Gobinda Saha +1
Label corruption, where training samples are mislabeled due to non-expert annotation or adversarial attacks, significantly degrades model performance. Acquiring large, perfectly la…
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi +1
Deep Neural Nets (DNNs) have become a pervasive tool for solving many emerging problems. However, they tend to overfit to and memorize the training set. Memorization is of keen int…
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting
Sangamesh Kodge, Gobinda Saha, Kaushik Roy
Machine unlearning is a prominent and challenging field, driven by regulatory demands for user data deletion and heightened privacy awareness. Existing approaches involve retrainin…