Summary of Can Active Label Correction Improve Llm-based Modular Ai Systems?, by Karan Taneja and Ashok Goel
Can Active Label Correction Improve LLM-based Modular AI Systems?by Karan Taneja, Ashok GoelFirst submitted to…
Can Active Label Correction Improve LLM-based Modular AI Systems?by Karan Taneja, Ashok GoelFirst submitted to…
Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable…
Correlated Quantization for Faster Nonconvex Distributed Optimizationby Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter RichtárikFirst…
Inconsistency-Based Data-Centric Active Open-Set Annotationby Ruiyu Mao, Ouyang Xu, Yunhui GuoFirst submitted to arxiv on:…
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detectionby Sicong Huang,…
Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradientsby Sebastian Johann WetzelFirst submitted to arxiv…
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysisby YongKyung Oh, Dong-Young…
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structureby Haoyu Chu, Yuto Miyatake, Wenjun Cui,…
AdaFed: Fair Federated Learning via Adaptive Common Descent Directionby Shayan Mohajer Hamidi, En-Hui YangFirst submitted…
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecastingby Shubao Zhao, Ming…