Summary of Crackess: a Self-prompting Crack Segmentation System For Edge Devices, by Yingchu Wang et al.
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devicesby Yingchu Wang, Ji He, Shijie YuFirst…
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devicesby Yingchu Wang, Ji He, Shijie YuFirst…
Sequential Compression Layers for Efficient Federated Learning in Foundational Modelsby Navyansh Mahla, Sunny Gupta, Amit…
Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantizationby Dongwei Wang, Huanrui YangFirst…
Mining Limited Data Sufficiently: A BERT-inspired Approach for CSI Time Series Application in Wireless Communication…
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantizationby Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangmin…
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Modelsby Neel Jain, Aditya…
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backboneby Max…
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networksby Junhe Zhang, Wanli…
1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval (LeSeR) for Regulatory Question Answeringby Jebish Purbey,…
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Modelsby Fan Wang, Juyong Jiang, Chansung Park, Sunghun…