ICML

2021–2025 確定版

著者一覧 · 国際会議 · 大学ランキング

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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
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian Mcauley, Zichao Yang, Eric P. Xing, Zhiting Hu
Machine Learning
日本大学: 東京大学
2024 · ICML
Unified Training of Universal Time Series Forecasting Transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, Han Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Uniformly Stable Algorithms for Adversarial Training and Beyond
Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unifying Image Processing as Visual Prompting Question Answering
Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Hyunouk Ko, Xiaoming Huo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Universal Gradient Methods for Stochastic Convex Optimization
Anton Rodomanov, Ali Kavis, Yongtao Wu, Kimon Antonakopoulos, Volkan Cevher
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, Samuel L Smith
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
Jiashun Liu, Jianye Hao, Yi Ma, Shuyin Xia
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training
Jinxia Yang, Bing Su, Xin Zhao, Ji-Rong Wen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs
Sara Ahmadian, Edith Cohen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images
Bin Pu, Xingguo Lv, Jiewen Yang, He Guannan, Xingbo Dong, Yiqun Lin, Li Shengli, Tan Ying, Liu Fei, Ming Chen, Zhe Jin, Kenli Li, Xiaomeng Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Episode Generation for Graph Meta-learning
Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
Hiren Madhu, Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity
Ali Behrouz, Parsa Delavari, Farnoosh Hashemi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
Zhongzhi Yu, Zheng Wang, Yonggan Fu, Huihong Shi, Khalid Shaikh, Yingyan Celine Lin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
Zhankun Luo, Abolfazl Hashemi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unveiling the Dynamics of Information Interplay in Supervised Learning
Kun Song, Zhiquan Tan, Bochao Zou, Huimin Ma, Weiran Huang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Unveiling the Potential of AI for Nanomaterial Morphology Prediction
Ivan Dubrovsky, Andrei Dmitrenko, Aleksei Dmitrenko, Nikita Serov, Vladimir Vinogradov
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Using AI Uncertainty Quantification to Improve Human Decision-Making
Laura Marusich, Jonathan Bakdash, Yan Zhou, Murat Kantarcioglu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Using Left and Right Brains Together: Towards Vision and Language Planning
Jun Cen, Chenfei Wu, Xiao Liu, Shengming Yin, Yixuan Pei, Jinglong Yang, Qifeng Chen, Nan Duan, Jianguo Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
S Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vague Prototype-Oriented Diffusion Model for Multi-Class Anomaly Detection
Yuxin Li, Yaoxuan Feng, Bo Chen, Wenchao Chen, Yubiao Wang, Xinyue Hu, Baolin Sun, Chunhui Qu, Mingyuan Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Value-Evolutionary-Based Reinforcement Learning
Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Fazl Barez
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vanilla Bayesian Optimization Performs Great in High Dimensions
Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Yash Patel, Declan Mcnamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Variational Learning is Effective for Large Deep Networks
Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Bazan Clement Emile Marcel Raoul, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
Machine Learning
日本大学: 東京科学大学
2024 · ICML
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Variational Schrödinger Diffusion Models
Wei Deng, Weijian Luo, Yixin Tan, Marin Biloš, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Zhen Qin, Weigao Sun, Dong Li, Xuyang Shen, Weixuan Sun, Yiran Zhong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment
Haokun Gui, Xiucheng Li, Xinyang Chen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Verification of Machine Unlearning is Fragile
Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Verifying message-passing neural networks via topology-based bounds tightening
Christopher Hojny, Shiqiang Zhang, Juan S Campos, Ruth Misener
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
Yang Jin, Zhicheng Sun, Kun Xu, Kun Xu, Liwei Chen, Hao Jiang, Quzhe Huang, Chengru Song, Yuliang Liu, Di Zhang, Yang Song, Kun Gai, Yadong Mu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition
Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong-Li Lee, Wynne Hsu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VideoPoet: A Large Language Model for Zero-Shot Video Generation
Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A Ross, Bryan Seybold, Lu Jiang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VideoPrism: A Foundational Visual Encoder for Video Understanding
Long Zhao, Nitesh Bharadwaj Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J. Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, Rachel Hornung, Florian Schroff, Ming-Hsuan Yang, David A Ross, Huisheng Wang, Hartwig Adam, Mikhail Sirotenko, Ting Liu, Boqing Gong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Viewing Transformers Through the Lens of Long Convolutions Layers
Itamar Zimerman, Lior Wolf
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception
Zhaoliang Wan, Yonggen Ling, Senlin Yi, Lu Qi, Wang Wei Lee, Minglei Lu, Sicheng Yang, Xiao Teng, Peng Lu, Xu Yang, Ming-Hsuan Yang, Hui Cheng
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vision Transformers as Probabilistic Expansion from Learngene
Qiufeng Wang, Xu Yang, Haokun Chen, Xin Geng
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, Min Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Visual Representation Learning with Stochastic Frame Prediction
Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach
Yancheng Wang, Ping Li, Yingzhen Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
Jinhao Li, Haopeng Li, Sarah Monazam Erfani, Lei Feng, James Bailey, Feng Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions
Yongqiang Cai
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
Pengying Wu, Yao Mu, Bingxian Wu, Yi Hou, Ji Ma, Shanghang Zhang, Chang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Rame, Nino Vieillard, Leonard Hussenot, Robert Dadashi-Tazehozi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
WAVES: Benchmarking the Robustness of Image Watermarks
Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer
Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh Ap, Vaibhav Rajan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer
Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe’Er
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Watermark Stealing in Large Language Models
Nikola Jovanović, Robin Staab, Martin Vechev
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeffrey Wu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation
Pei Liu, Luping Ji
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
Xing Han Lu, Zdeněk Kasner, Siva Reddy
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weighted distance nearest neighbor condensing
Lee-Ad Gottlieb, Timor Sharabi, Roi Weiss
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Snir Hordan, Tal Amir, Nadav Dym
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Weisfeiler-Leman at the margin: When more expressivity matters
Billy Joe Franks, Christopher Morris, Ameya Velingker, Floris Geerts
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks
Xingwu Chen, Difan Zou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Xisen Jin, Xiang Ren
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng, Olga Russakovsky
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis
Waı̈ss Azizian, Franck Iutzeler, Jerome Malick, Panayotis Mertikopoulos
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Aaditya K Singh, Ted Moskovitz, Felix Hill, Stephanie C.Y. Chan, Andrew M Saxe
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
Zhening Li, Gabriel Poesia, Armando Solar-Lezama
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
Haoran You, Yichao Fu, Zheng Wang, Amir Yazdanbakhsh, Yingyan Celine Lin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When Representations Align: Universality in Representation Learning Dynamics
Loek Van Rossem, Andrew M Saxe
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When Will Gradient Regularization Be Harmful?
Yang Zhao, Hao Zhang, Xiuyuan Hu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Xuefeng Du, Yiyou Sun, Yixuan Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
When is Transfer Learning Possible?
My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri, Gokul Swamy, Geoffrey J. Gordon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
Yuxiao Wen, Arthur Jacot
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models
Xavier Suau, Pieter Delobelle, Katherine Metcalf, Armand Joulin, Nicholas Apostoloff, Luca Zappella, Pau Rodriguez
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Jin Hwa Lee, Stefano Sarao Mannelli, Andrew M Saxe
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition
Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Why do Variational Autoencoders Really Promote Disentanglement?
Pratik Bhowal, Achint Soni, Sirisha Rambhatla
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Winner-takes-all learners are geometry-aware conditional density estimators
Victor Letzelter, David Perera, Cédric Rommel, Mathieu Fontaine, Slim Essid, Gaël Richard, Patrick Perez
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vazquez, Nicolas Chapados, Alexandre Lacoste
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Wukong: Towards a Scaling Law for Large-Scale Recommendation
Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation
Yiwei Ma, Zhekai Lin, Jiayi Ji, Yijun Fan, Xiaoshuai Sun, Rongrong Ji
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Che Liu, Zhongwei Wan, Cheng Ouyang, Anand Shah, Wenjia Bai, Rossella Arcucci
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Zero-Shot Reinforcement Learning via Function Encoders
Tyler Ingebrand, Amy Zhang, Ufuk Topcu
Machine Learning
日本大学: 関与なし（対象大学）