ICLR Papers
6,124 papers found • Page 9 of 123
Block-Attention for Efficient Prefilling
Dongyang Ma, Yan Wang, Tian Lan
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Marianne Arriola, Aaron Gokaslan, Justin Chiu et al.
Block Verification Accelerates Speculative Decoding
Ziteng Sun, Uri Mendlovic, Yaniv Leviathan et al.
BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
Yunhan Zhao, Xiang Zheng, Lin Luo et al.
BodyGen: Advancing Towards Efficient Embodiment Co-Design
Haofei Lu, Zhe Wu, Junliang Xing et al.
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
Yu Heng Hung, Kai-Jie Lin, Yu-Heng Lin et al.
Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions
Xiaoran Jiao, Weian Mao, Wengong Jin et al.
Boltzmann priors for Implicit Transfer Operators
Juan Viguera Diez, Mathias Schreiner, Ola Engkvist et al.
Boltzmann Semantic Score: A Semantic Metric for Evaluating Large Vision Models Using Large Language Models
Ali Khajegili Mirabadi, Katherine Rich, Hossein Farahani et al.
BOND: Aligning LLMs with Best-of-N Distillation
Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot-Desenonges et al.
BoneMet: An Open Large-Scale Multi-Modal Murine Dataset for Breast Cancer Bone Metastasis Diagnosis and Prognosis
Tiankuo Chu, Fudong Lin, Shubo Wang et al.
Bonsai: Gradient-free Graph Condensation for Node Classification
Mridul Gupta, Samyak Jain, Vansh Ramani et al.
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Tiansheng Huang, Sihao Hu, Fatih Ilhan et al.
Boosting Latent Diffusion with Perceptual Objectives
Tariq Berrada, Pietro Astolfi, Melissa Hall et al.
Boosting Methods for Interval-censored Data with Regression and Classification
Yuan Bian, Grace Yi, Wenqing He
Boosting Multiple Views for pretrained-based Continual Learning
Quyen Tran, Tung Lam Tran, Khanh Doan et al.
Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems
Fu Luo, Xi Lin, Yaoxin Wu et al.
Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games
Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu et al.
Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors
Chen Ma, Xinjie Xu, Shuyu Cheng et al.
Boosting the visual interpretability of CLIP via adversarial fine-tuning
Shizhan Gong, Haoyu LEI, Qi Dou et al.
Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation
Sheng-Feng Yu, Jia-Jiun Yao, Wei-Chen Chiu
Bootstrapped Model Predictive Control
Yuhang Wang, Hanwei Guo, Sizhe Wang et al.
Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
Zun Wang, Jialu Li, Yicong Hong et al.
Bootstrapping Language Models with DPO Implicit Rewards
Changyu Chen, Zichen Liu, Chao Du et al.
Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation
Peiwen Sun, Sitong Cheng, Xiangtai Li et al.
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations
David Dalton, Alan Lazarus, Hao Gao et al.
Bounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions
Yoshiaki Kitazawa
BP-Modified Local Loss for Efficient Training of Deep Neural Networks
REN Lianhai, Qianxiao Li
BRAID: Input-driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
Parsa Vahidi, Omid G. Sani, Maryam Shanechi
BrainACTIV: Identifying visuo-semantic properties driving cortical selectivity using diffusion-based image manipulation
Diego García Cerdas, Christina Sartzetaki, Magnus Petersen et al.
Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration
Chen Jiang, Jiahui An, Yating Liu et al.
Brain-inspired $L_p$-Convolution benefits large kernels and aligns better with visual cortex
Jea Kwon, Sungjun Lim, Kyungwoo Song et al.
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
Andrew Luo, Jacob Yeung, Rushikesh Zawar et al.
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Jiaxing Xu, Yongqiang Chen, Xia Dong et al.
BrainUICL: An Unsupervised Individual Continual Learning Framework for EEG Applications
Yangxuan Zhou, Sha Zhao, Jiquan Wang et al.
Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses
David Glukhov, Ziwen Han, I Shumailov et al.
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
xin zhang, Jiawei Du, Ping Liu et al.
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
Wei Liu, Zhiying Deng, Zhongyu Niu et al.
Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate
Yexiang Liu, Jie Cao, Zekun Li et al.
Breaking Neural Network Scaling Laws with Modularity
Akhilan Boopathy, Sunshine Jiang, William Yue et al.
Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids
Tianyuan Jin, Qin Zhang, Dongruo Zhou
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Lukas Miklautz, Timo Klein, Kevin Sidak et al.
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
Yuxin Jiang, Bo Huang, Yufei Wang et al.
Bridging Compressed Image Latents and Multimodal Large Language Models
Chia-Hao Kao, Cheng Chien, Yu-Jen Tseng et al.
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Yanming Liu, Xinyue Peng, Jiannan Cao et al.
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach
Zechen Bai, Tianjun Xiao, Tong He et al.
Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
Chen Xu, Yuxin Li, Wenjie Wang et al.
Bridging the Data Provenance Gap Across Text, Speech, and Video
Shayne Longpre, Nikhil Singh, Manuel Cherep et al.
Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo
Jun Xia, Sizhe Liu, Jingbo Zhou et al.
Bridging the Gap Between f-divergences and Bayes Hilbert Spaces
Linus Lach, Alexander Fottner, Yarema Okhrin