17 citations · 26 across the 8 of their papers we have counts for
10 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…
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…
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…
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.…
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…
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…
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly…
HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities
Shristi Das Biswas, Adarsh Kosta, Chamika Liyanagedera +2
Event cameras detect changes in per-pixel intensity to generate asynchronous `event streams'. They offer great potential for accurate semantic map retrieval in real-time autonomous…
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena +1
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge de…
Estimation of 2D Velocity Model using Acoustic Signals and Convolutional Neural Networks
Marco Apolinario, Samuel Huaman Bustamante, Giorgio Morales +2
The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also…