arXivDaily arXiv每日学术速递 周一至周五更新

期刊&会议

International Conference on Machine Learning · 会议 · Machine Learning

2025-12-09 至 2025-12-09 共收录 4
2512.07289 2025-12-09 cond-mat.mtrl-sci cs.LG

Equivariant Diffusion for Crystal Structure Prediction

等价扩散用于晶体结构预测

Peijia Lin, Pin Chen, Rui Jiao, Qing Mo, Jianhuan Cen, Wenbing Huang, Yang Liu, Dan Huang, Yutong Lu

机构 * School of Computer Science Engineering, Sun Yat-sen University, Guangzhou, China National Supercomputer Center in Guangzhou, China Dept. of Comp. Sci. \& Tech., Institute for AI, Tsinghua University, Beijing, China Institute for AIR, Tsinghua University, Beijing, China Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China Beijing Key Laboratory of Big Data Management Analysis Methods, Beijing, China

AI总结 EquiCSP通过等价扩散模型解决晶体结构预测中的对称性问题,提升生成结构的准确性并加快训练收敛速度。

Comments ICML 2024

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2502.03930 2025-12-09 eess.AS cs.AI cs.CL cs.LG cs.SD

DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation

DiTAR:基于扩散变换器的语音生成自回归建模

Dongya Jia, Zhuo Chen, Jiawei Chen, Chenpeng Du, Jian Wu, Jian Cong, Xiaobin Zhuang, Chumin Li, Zhen Wei, Yuping Wang, Yuxuan Wang

机构 * ByteDance Seed(字节跳动种子)

AI总结 DiTAR通过结合语言模型和扩散变换器,提出一种基于补丁的自回归框架,有效提升连续语音生成的效率与质量。

Comments ByteDance Seed template, ICML 2025

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2410.02483 2025-12-09 cs.CV

Event-Customized Image Generation

事件定制图像生成

Zhen Wang, Yilei Jiang, Dong Zheng, Jun Xiao, Long Chen

机构 * Zhejiang University, Hangzhou, China(浙江大学) The Hong Kong University of Science and Technology(香港科技大学)

AI总结 本文提出FreeEvent方法,通过引入实体切换和事件转移路径,实现事件定制化图像生成,提升复杂场景下的定制化能力。

Journal ref ICML 2025

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2512.06886 2025-12-09 cs.CV

Balanced Learning for Domain Adaptive Semantic Segmentation

领域自适应语义分割中的平衡学习

Wangkai Li, Rui Sun, Bohao Liao, Zhaoyang Li, Tianzhu Zhang

机构 * MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition(脑启发智能感知与认知关键实验室) University of Science and Technology of China(中国科学技术大学) Deep Space Exploration Laboratory(深空探测实验室)

AI总结 BLDA通过分析logits分布和引入共享锚定分布,有效缓解领域自适应语义分割中的类别偏倚问题,提升模型在欠预测类别上的性能。

Comments Accepted by International Conference on Machine Learning (ICML 2025)

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