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Research & Development
REP-Net: A Modular Framework for Advanced Time Series Forecasting
TLDR: REP-Net is a novel time series forecasting architecture that decomposes the forecasting pipeline into three distinct, modular stages: Representation, Memory, and Projection. This...
Research & Development
Bridging Probability and Logic: A New Look at Rational Beliefs and Their Evolution
TLDR: This paper explores the Lockean thesis, which defines beliefs based on probabilistic confidence. It addresses the issue that these belief sets are often...
Research & Development
Advancing Speech Emotion Recognition with Spectral Learning and Attention
TLDR: A new Speech Emotion Recognition (SER) framework uses Mel-Frequency Cepstral Coefficients (MFCCs) and a 1D Convolutional Neural Network (CNN) with channel and spatial...
Research & Development
Decoding Brain Signals: How EEG-VJEPA Advances EEG Analysis
TLDR: EEG-VJEPA is a novel self-supervised AI model that adapts a video analysis architecture (V-JEPA) to interpret brain signals (EEG). By treating EEG as...
Research & Development
LoSiA: Optimizing LLM Fine-Tuning with Dynamic Subnet Localization
TLDR: LoSiA is a novel Parameter-Efficient Fine-Tuning (PEFT) method that dynamically identifies and optimizes critical sub-networks within large language models. Unlike traditional low-rank methods,...
Research & Development
LTMSformer: Enhancing Multi-Agent Trajectory Prediction with Local Trends and Motion State Encoding
TLDR: LTMSformer is a new lightweight framework for multi-agent trajectory prediction in autonomous driving. It introduces a Local Trend-Aware Attention mechanism to capture short-term...