4 papers
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…
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…
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…