Vishwas Sathish
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View article: PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals Open
This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, leveraging Power-In-Band (PIB) and signal coherence as disting…
View article: LLeMpower: Understanding Disparities in the Control and Access of Large Language Models
LLeMpower: Understanding Disparities in the Control and Access of Large Language Models Open
Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing…
View article: Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning
Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning Open
There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active pre…
View article: Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning Open
Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework fo…
View article: Graph Embedding Based Hybrid Social Recommendation System
Graph Embedding Based Hybrid Social Recommendation System Open
Item recommendation tasks are a widely studied topic. Recent developments in deep learning and spectral methods paved a path towards efficient graph embedding techniques. But little research has been done on applying these graph embedding …