7 papers
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…
LANCE: Low Rank Activation Compression for Efficient On-Device Continual Learning
Marco Paul E. Apolinario, Kaushik Roy
On-device learning is essential for personalization, privacy, and long-term adaptation in resource-constrained environments. Achieving this requires efficient learning, both fine-t…
Feedback Alignment Meets Low-Rank Manifolds: A Structured Recipe for Local Learning
Arani Roy, Marco P. Apolinario, Shristi Das Biswas +1
Training deep neural networks (DNNs) with backpropagation (BP) achieves state-of-the-art accuracy but requires global error propagation and full parameterization, leading to substa…
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning
Marco Paul E. Apolinario, Sakshi Choudhary, Kaushik Roy
Continual learning (CL) - the ability to progressively acquire and integrate new concepts - is essential to intelligent systems to adapt to dynamic environments. However, deep neur…
TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy, Charlotte Frenkel
The demand for low-power inference and training of deep neural networks (DNNs) on edge devices has intensified the need for algorithms that are both scalable and energy-efficient.…
LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Marco Paul E. Apolinario, Arani Roy, Kaushik Roy
Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-dev…