ICML

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Machine Learning

2024 · 2,610 papers

このEditionに反映済みのmain full research papersのみ掲載しています。著者リンクは日本の大学への著者別所属根拠がある場合に限ります。

2024 · ICML
Differentially Private Domain Adaptation with Theoretical Guarantees
Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially Private Post-Processing for Fair Regression
Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially Private Representation Learning via Image Captioning
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially Private Sum-Product Networks
Xenia Heilmann, Mattia Cerrato, Ernst Althaus
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially Private Worst-group Risk Minimization
Xinyu Zhou, Raef Bassily
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Differentially private exact recovery for stochastic block models
Dung Nguyen, Anil Kumar Vullikanti
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation
Kartik Sharma, Srijan Kumar, Rakshit Trivedi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Language Models Are Versatile Protein Learners
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Model-Augmented Behavioral Cloning
Shang-Fu Chen, Hsiang-Chun Wang, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance
Mingyuan Bai, Wei Huang, Tenghui Li, Andong Wang, Junbin Gao, Cesar F Caiafa, Qibin Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Models Encode the Intrinsic Dimension of Data Manifolds
Jan Pawel Stanczuk, Georgios Batzolis, Teo Deveney, Carola-Bibiane Schönlieb
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, Zhiyang Xun
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Rejection Sampling
Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, Il-Chul Moon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering
Jie Wen, Shijie Deng, Waikeung Wong, Guoqing Chao, Chao Huang, Lunke Fei, Yong Xu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Directly Denoising Diffusion Models
Dan Zhang, Jingjing Wang, Feng Luo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dirichlet Flow Matching with Applications to DNA Sequence Design
Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discounted Adaptive Online Learning: Towards Better Regularization
Zhiyu Zhang, David Bombara, Heng Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
Dyah Adila, Shuai Zhang, Boran Han, Bernie Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discovering Environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discovering Features with Synergistic Interactions in Multiple Views
Chohee Kim, Mihaela Van Der Schaar, Changhee Lee
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discovering Mixtures of Structural Causal Models from Time Series Data
Sumanth Varambally, Yian Ma, Rose Yu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
Takayuki Osa, Tatsuya Harada
Machine Learning
日本大学: 東京大学
2024 · ICML
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
Rui Wang, Elyssa Hofgard, Han Gao, Robin Walters, Tess Smidt
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Aaron Lou, Chenlin Meng, Stefano Ermon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Discrete Latent Perspective Learning for Segmentation and Detection
Deyi Ji, Feng Zhao, Lanyun Zhu, Wenwei Jin, Hongtao Lu, Jieping Ye
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disentangled 3D Scene Generation with Layout Learning
Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A Efros, Aleksander Holynski
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet
Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, Wenwu Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, Wenwu Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disentanglement Learning via Topology
Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disguised Copyright Infringement of Latent Diffusion Models
Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Disparate Impact on Group Accuracy of Linearization for Private Inference
Saswat Das, Marco Romanelli, Ferdinando Fioretto
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal, Basura Fernando, Cheston Tan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DistiLLM: Towards Streamlined Distillation for Large Language Models
Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
Zheng Xiong, Risto Vuorio, Jacob Beck, Matthieu Zimmer, Kun Shao, Shimon Whiteson
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distinguishing the Knowable from the Unknowable with Language Models
Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, Benjamin L. Edelman
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distributed Bilevel Optimization with Communication Compression
Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu, Bin Wang, Kun Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
Caixing Wang, Ziliang Shen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
Jintong Gao, He Zhao, Dan Dan Guo, Hongyuan Zha
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distributional Bellman Operators over Mean Embeddings
Li Kevin Wenliang, Gregoire Deletang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Distributionally Robust Data Valuation
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Ditto: Quantization-aware Secure Inference of Transformers upon MPC
Haoqi Wu, Wenjing Fang, Yancheng Zheng, Junming Ma, Jin Tan, Lei Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diversified Batch Selection for Training Acceleration
Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor Tsang, Yanfeng Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Shijie Lian, Ziyi Zhang, Hua Li, Wenjie Li, Laurence Tianruo Yang, Sam Kwong, Runmin Cong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Efficient Transformers Really Save Computation?
Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell, Bernhard Schölkopf, Abulhair Saparov, Mrinmaya Sachan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates
Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Keyon Vafa, Ashesh Rambachan, Sendhil Mullainathan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen Mckeown
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Topological Characteristics Help in Knowledge Distillation?
Jungeun Kim, Junwon You, Dongjin Lee, Ha Young Kim, Jae-Hun Jung
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Do Transformer World Models Give Better Policy Gradients?
Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D’Oro, Pierre-Luc Bacon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DoRA: Weight-Decomposed Low-Rank Adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Does Label Smoothing Help Deep Partial Label Learning?
Xiuwen Gong, Nitin Bisht, Guandong Xu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Domain Generalisation via Imprecise Learning
Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Domain-wise Data Acquisition to Improve Performance under Distribution Shift
Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget
Florian E. Dorner, Moritz Hardt
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Don’t be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan, Berend Zwartsenberg, Frank Wood
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Don’t trust your eyes: on the (un)reliability of feature visualizations
Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent)
Zongxin Yang, Guikun Chen, Xiaodi Li, Wenguan Wang, Yi Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Double Momentum Method for Lower-Level Constrained Bilevel Optimization
Wanli Shi, Yi Chang, Bin Gu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods
Hao Di, Haishan Ye, Xiangyu Chang, Guang Dai, Ivor Tsang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor Tsang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements
Kyuwon Kim, Donghwan Kim
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
Hany Hamed, Subin Kim, Dongyeong Kim, Jaesik Yoon, Sungjin Ahn
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Drug Discovery with Dynamic Goal-aware Fragments
Seul Lee, Seanie Lee, Kenji Kawaguchi, Sung Ju Hwang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DsDm: Model-Aware Dataset Selection with Datamodels
Logan Engstrom, Axel Feldmann, Aleksander Madry
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dual Operating Modes of In-Context Learning
Ziqian Lin, Kangwook Lee
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
Kaibo He, Chenhui Zuo, Chengtian Ma, Yanan Sui
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Sam Reifenstein, Timothee Leleu, Yoshihisa Yamamoto
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
Ron Dorfman, Naseem Amin Yehya, Kfir Yehuda Levy
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Correlation Clustering in Sublinear Update Time
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Evaluation of Large Language Models by Meta Probing Agents
Kaijie Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu, Xing Xie
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Facility Location in High Dimensional Euclidean Spaces
Sayan Bhattacharya, Gramoz Goranci, Shaofeng H.-C. Jiang, Yi Qian, Yubo Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference
Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, Edoardo Ponti
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Metric Embedding into lp Space
Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Dariusz Rafal Kowalski, Jan Olkowski, Max Springer
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Spectral Clustering with Provable Approximation Guarantee
Steinar Laenen, He Sun
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Dynamic Survival Analysis with Controlled Latent States
Linus Bleistein, Van Tuan Nguyen, Adeline Fermanian, Agathe Guilloux
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
Foteini Strati, Sara Mcallister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
E$^2$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation
Yifan Gong, Zheng Zhan, Qing Jin, Yanyu Li, Yerlan Idelbayev, Xian Liu, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, Chenyan Xiong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization
Zhibin Gu, Zhendong Li, Songhe Feng
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis
Jungil Kong, Junmo Lee, Jeongmin Kim, Beomjeong Kim, Jihoon Park, Dohee Kong, Changheon Lee, Sangjin Kim
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models
Limin Liu, Shuai He, Anlong Ming, Rui Xie, Huadong Ma
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
Chung-Yiu Yau, Hoi To Wai, Parameswaran Raman, Soumajyoti Sarkar, Mingyi Hong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers
Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, Rongrong Ji
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ESM All-Atom: Multi-Scale Protein Language Model for Unified Molecular Modeling
Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection
Hongyu Liu, Runmin Cong, Hua Li, Qianqian Xu, Qingming Huang, Wei Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
Massimo Bini, Karsten Roth, Zeynep Akata, Anna Khoreva
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
Sunil Hwang, Jaehong Yoon, Youngwan Lee, Sung Ju Hwang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Early Time Classification with Accumulated Accuracy Gap Control
Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
Jiewei Zhang, Song Guo, Peiran Dong, Jie Zhang, Ziming Liu, Yue Yu, Xiao-Ming Wu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Editing Partially Observable Networks via Graph Diffusion Models
Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
Machine Learning
日本大学: 関与なし（対象大学）