联邦学习 x NeurIPS'2023(中) 联邦学习 x NeurIPS'2023 (下) 联邦学习 x NeurIPS'2023 Workshop 创作声明: 本篇内容为 @白小鱼 整理,意在促进对联邦学习领域的学习与交流。 论文集基于小鱼手工筛选与核对后收录于 Awesome-FL 项目,论文元数据的获取和整理基于开源软件 Zotero ,论文摘要的翻译基于 zotero-pd...
https://openreview.net/group?id=NeurIPS.cc/2024/Workshop/FM4Science 重要日期(AoE 时间): 摘要提交截止日期:2024年9月10日 论文提交截止日期:2024年9月13日 审稿截止日期:2024年10月11日 录取/拒稿通知日期:2024年10月14日 Workshop研讨会日期:2024年12月14日或15日 主办方 Wuyang Chen Assistant Profess...
本届会议中,英飞智药与北京大学合作的蛋白质生成模型论文被NeurIPS2021机器学习与结构生物学Workshop接收。Workshop于2021年12月13日美东时间上午九时线上召开,论文第一作者张书豪在poster session对此项研究进行了报告。 NeurIPS是学术界、工业界...
Two InstaDeep Tunis-based AI researchers will present at NeurIPS 2023 NAML: North African Machine Learning workshop on multi-script handwriting recognition and what happens Multi agent Reinforcement Learning is built on economic theories. The pair’s different research interests illustrate the brea...
NeurIPS 2022 Workshop on Causality for Real-world Impact This workshop was held at NeurIPS on 2nd December 2023 Causality has a long history, providing it with many principled approaches to identify a causal effect [1-3] (or even distill cause from effect [4]). However, these approaches ar...
http://bing.comImplicit Behavioral Cloning -- talk at NeurIPS 2021 Deep RL Workshop字幕版之后会放出,敬请持续关注欢迎加入人工智能机器学习群:556910946,公众号: AI基地,会有视频,资料放送。公众号中输入视频地址或视频ID就可以自助查询对应的字幕版本, 视频播放
AI Art Gallery NeurIPS Workshop on Machine Learning for Creativity and Design 2020Highlights Art Music Design Poetry 2019 Highlights Music Art Design Paper Demos 2018 Highlights Music Art Design 2017 Art Music Paper demos AboutMal Som @errthangisalive This piece was trained on myths and ...
About Code for SBI-RAG published at 4th MATH-AI Workshop at NeurIPS'24 Resources Readme Activity Stars 0 stars Watchers 1 watching Forks 0 forks Report repository Releases No releases published Packages No packages published Languages Jupyter Notebook 97.4% Python 2.6% ...
Conference : International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 Url:https://openreview.net/forum?id=XSfsvBoc8M Abstract: Personalized federated learning (PFL) aims at learning personalized models for users in a federated setup. We focus on...
Extensive experiments show that our safety attack method can significantly compromise the LLM's safety alignment (e.g., reduce safety rate by 70%), which can not be effectively defended by existing defense methods (at most 4% absolute improvement), while our safety defense method can significantl...