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Interrupting encoder training in diffusion models enables more efficient generative AI

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as ...
IEEE

GURNet: A Gated U-Shaped Encoder-Decoder Predictor for Reversible Data Hiding

Abstract: The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual ...
GitHub

Brain-to-Text '25: Neural Speech Decoding

Deep learning models for decoding intracortical neural activity during attempted speech into text. This repository contains our team's implementation for the COMP 433 Fall 2025 course project, ...
IEEE

Deep Neural Encoder-Decoder Model to Relate fMRI Brain Activity with Naturalistic Stimuli

Abstract: We propose an end-to-end deep neural encoder-decoder model to encode and decode brain activity in response to naturalistic stimuli using functional magnetic resonance imaging (fMRI) data.
GitHub

GitHub - SVNSAIRAVIKIRAN/DAEIN: Deep learning framework for energy consumption forecasting and anomaly detection using a Dual Attention Encoder-Decoder architecture with an ...

Deep learning framework for energy consumption forecasting and anomaly detection using a Dual Attention Encoder-Decoder architecture with an interactive analytics dashboard. - SVNSAIRAVIKIRAN/DAEIN ...
Machine Design

Reduce Encoder Cost While Gaining Functionality with 44-Turn Sensors

How to substantially reduce encoder cost while gaining functionality with 44-Turn multi-turn rotary position sensors—retaining count and position during power loss. New technology reduces encoder ...