ICML Spotlight Papers
406 papers found • Page 2 of 9
Elucidating the Design Space of Multimodal Protein Language Models
Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang et al.
Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective
Seungwook Han, Jinyeop Song, Jeff Gore et al.
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation
Alessandro Palma, Sergei Rybakov, Leon Hetzel et al.
Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss
Bo-Han Lai, Pin-Han Huang, Bo-Han Kung et al.
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
Zheyang Xiong, Jack Cai, John Cooper et al.
Exogenous Isomorphism for Counterfactual Identifiability
Yikang Chen, Dehui du
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
Chi-Ning Chou, Hang Le, Yichen Wang et al.
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
Fabiola Ricci, Lorenzo Bardone, Sebastian Goldt
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
Terje Mildner, Oliver Hamelijnck, Paris Giampouras et al.
FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
Yichen Li, Yuying Wang, Haozhao Wang et al.
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian et al.
Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator
YuXin Li, Felix Dangel, Derek Tam et al.
FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials
Seung Lee, Hojoon Kim, Yutack Park et al.
Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency
Georg Bökman, David Nordström, Fredrik Kahl
FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Gwanhyeong Koo, Sunjae Yoon, Younghwan Lee et al.
From Language Models over Tokens to Language Models over Characters
Tim Vieira, Benjamin LeBrun, Mario Giulianelli et al.
From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
Etowah Adams, Liam Bai, Minji Lee et al.
Functional Alignment Can Mislead: Examining Model Stitching
Damian Smith, Harvey Mannering, Antonia Marcu
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
James Rowbottom, Georg Maierhofer, Teo Deveney et al.
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Guibin Zhang, Yanwei Yue, Xiangguo Sun et al.
Generalized Random Forests Using Fixed-Point Trees
David Fleischer, David A Stephens, Archer Yang
Geometric Hyena Networks for Large-scale Equivariant Learning
Artem Moskalev, Mangal Prakash, Junjie Xu et al.
Geometric Representation Condition Improves Equivariant Molecule Generation
Zian Li, Cai Zhou, Xiyuan Wang et al.
GMAIL: Generative Modality Alignment for generated Image Learning
Shentong Mo, Sukmin Yun
Graph Adaptive Autoregressive Moving Average Models
Moshe Eliasof, Alessio Gravina, Andrea Ceni et al.
Graph Diffusion for Robust Multi-Agent Coordination
Xianghua Zeng, Hang Su, Zhengyi Wang et al.
Great Models Think Alike and this Undermines AI Oversight
Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina et al.
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
Tianwei Lin, Wenqiao Zhang, Sijing Li et al.
Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It
Marvin F, da Silva, Felix Dangel, Sageev Oore
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee, Youngdo Lee, Takuma Seno et al.
Identifying Causal Direction via Variational Bayesian Compression
Quang-Duy Tran, Bao Duong, Phuoc Nguyen et al.
Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Mark Schoene, Babak Rahmani, Heiner Kremer et al.
Improving Consistency Models with Generator-Augmented Flows
Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos et al.
Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices
Junhao Dong, Piotr Koniusz, Yifei Zhang et al.
Independence Tests for Language Models
Sally Zhu, Ahmed Ahmed, Rohith Kuditipudi et al.
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
Yuanhong Zhang, Muyao Yuan, Weizhan Zhang et al.
Instance Correlation Graph-based Naive Bayes
Chengyuan Li, Liangxiao Jiang, Wenjun Zhang et al.
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency
Zexu Sun, Qiyu Han, Hao Yang et al.
Investigating Non-Transitivity in LLM-as-a-Judge
Yi Xu, Laura Ruis, Tim Rocktäschel et al.
Is Complex Query Answering Really Complex?
Cosimo Gregucci, Bo Xiong, Daniel Hernández et al.
Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations
Lucy Farnik, Tim Lawson, Conor Houghton et al.
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
Ken Ziyu Liu, Christopher A. Choquette Choo, Matthew Jagielski et al.
Large Language Model-driven Large Neighborhood Search for Large-Scale MILP Problems
Huigen Ye, Hua Xu, An Yan et al.
Latent Diffusion Planning for Imitation Learning
Amber Xie, Oleh Rybkin, Dorsa Sadigh et al.
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models
Saketh Bachu, Erfan Shayegani, Rohit Lal et al.
Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures
Dongzhe Zheng, Wenjie Mei
Learning Parametric Distributions from Samples and Preferences
Marc Jourdan, Gizem Yüce, Nicolas Flammarion
Learning Safety Constraints for Large Language Models
Xin Chen, Yarden As, Andreas Krause
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
Connor Schenck, Isaac Reid, Mithun Jacob et al.
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
Yu Sun, Xinhao Li, Karan Dalal et al.