IEEE

Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds

Abstract: Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. With ...
IEEE

V-DCRNN: Virtual Network-Based Diffusion Convolutional Recurrent Neural Network for Estimating Unobserved Traffic Data

Abstract: Several studies have analyzed traffic patterns using Vehicle Detector (VD) and Global Positioning System (GPS) data. VD records the speed of vehicles passing through detectors, GPS data ...
EDN

Why memory swizzling is hidden tax on AI compute

Memory swizzling is the quiet tax that every hierarchical-memory accelerator pays. It is fundamental to how GPUs, TPUs, NPUs, ...
eLife

High-Fidelity Neural Speech Reconstruction through an Efficient Acoustic-Linguistic Dual-Pathway Framework

This study presents a valuable advance in reconstructing naturalistic speech from intracranial ECoG data using a dual-pathway model. The evidence supporting the claims of the authors is solid, ...
techxplore

Guided learning helps previously 'untrainable' neural networks learn effectively

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a ...
psypost.org

Deep neural recordings suggest the brain adapts to weight-loss drugs over time

New research offers a rare glimpse into how popular weight-loss medications affect electrical activity deep within the human brain. A case study involving a patient with obesity and binge-eating ...