ICML

Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation

Mingyu Kang, Yong Suk Choi

机构 * Department of Artificial Intelligence, University of Hanyang, Seoul, Korea（人工智能系，翰阳大学，韩国首尔） ； Department of Computer Science, University of Hanyang, Seoul, Korea（计算机科学系，翰阳大学，韩国首尔） 纠错

Comments ICML 2025

Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems

Jiachen Guo, Xiaoyu Xie, Chanwook Park, Hantao Zhang, Matthew Politis, Gino Domel, Thomas J. R. Hughes, Wing Kam Liu

Journal ref Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:21138-21162, 2025

The dark side of the forces: assessing non-conservative force models for atomistic machine learning

Filippo Bigi, Marcel Langer, Michele Ceriotti

机构 * Laboratory of Computational Science and Modeling（计算科学与建模实验室） 纠错

Comments ICML 2025 (Oral)

Retraining-Free Merging of Sparse MoE via Hierarchical Clustering

I-Chun Chen, Hsu-Shen Liu, Wei-Fang Sun, Chen-Hao Chao, Yen-Chang Hsu, Chun-Yi Lee

机构 * National Tsing Hua University（国立清华大学） ； National Taiwan University（国立台湾大学） ； University of Toronto（多伦多大学） ； NVIDIA AI Technology Center (NVAITC)（NVIDIA AI技术中心） ； Samsung Research America（三星美国研究院） 纠错

Comments Code: https://github.com/wazenmai/HC-SMoE. Accepted by ICML 2025

Invertible generative models for inverse problems: mitigating representation error and dataset bias

Muhammad Asim, Mara Daniels, Oscar Leong, Ali Ahmed, Paul Hand

机构 * itu（信息科技大学） ； rice（里士满大学） ； neumcs（东北大学） 纠错

Comments Camera ready version for ICML 2020, paper 2655

Deep Learning on Real-World Graphs

Emanuele Rossi

Comments The thesis was submitted for the degree of Doctor of Philosophy in Computing at Imperial College London (February 2024), under the supervision of Prof. Michael M. Bronstein. It includes work published at ICML, ICLR, NeurIPS, and the Learning on Graphs Conference