Poster Papers
24,624 papers found • Page 439 of 493
Conference
Position: Tensor Networks are a Valuable Asset for Green AI
Eva Memmel, Clara Menzen, Jetze Schuurmans et al.
Position: The Causal Revolution Needs Scientific Pragmatism
Joshua Loftus
Position: The Platonic Representation Hypothesis
Minyoung Huh, Brian Cheung, Tongzhou Wang et al.
Position: The Reasonable Person Standard for AI
Sunayana Rane
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein et al.
Position: Towards Implicit Prompt For Text-To-Image Models
Yue Yang, Yuqi Lin, Hong Liu et al.
Position: Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, an liu, Zijun Liu et al.
Position: TrustLLM: Trustworthiness in Large Language Models
Yue Huang, Lichao Sun, Haoran Wang et al.
Position: Video as the New Language for Real-World Decision Making
Sherry Yang, Jacob C Walker, Jack Parker-Holder et al.
Position: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yi-Fan Zhang, Wei Chen et al.
Position: Why Tabular Foundation Models Should Be a Research Priority
Boris van Breugel, M van der Schaar
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger et al.
Position: Will we run out of data? Limits of LLM scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla et al.
Positive and Unlabeled Learning with Controlled Probability Boundary Fence
Changchun Li, Yuanchao Dai, Lei Feng et al.
Positive Concave Deep Equilibrium Models
Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante
Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation
Lin Long, Haobo Wang, Zhijie Jiang et al.
Posterior Distillation Sampling
Juil Koo, Chanho Park, Minhyuk Sung
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama et al.
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger et al.
PosterLlama: Bridging Design Ability of Langauge Model to Content-Aware Layout Generation
Jaejung Seol, Seojun Kim, Jaejun Yoo
Post-hoc Part-Prototype Networks
Andong Tan, Fengtao ZHOU, Hao Chen
Post-training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models
Siao Tang, Xin Wang, Hong Chen et al.
PostureHMR: Posture Transformation for 3D Human Mesh Recovery
Yu-Pei Song, Xiao WU, Zhaoquan Yuan et al.
Potential Based Diffusion Motion Planning
Yunhao Luo, Chen Sun, Josh Tenenbaum et al.
Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
Fanyue Wei, Wei Zeng, Zhenyang Li et al.
Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment
Simon Weber, Je Hyeong Hong, Daniel Cremers
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
Zhili Chen, Maosheng Ye, Shuangjie Xu et al.
PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching
Haitao Lin, Odin Zhang, Huifeng Zhao et al.
PQ-SAM: Post-training Quantization for Segment Anything Model
Xiaoyu Liu, Xin Ding, Lei Yu et al.
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Zining Chen, Weiqiu Wang, Zhicheng Zhao et al.
Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
Kai Xu, Hong Ge
Practical Measurements of Translucent Materials with Inter-Pixel Translucency Prior
Zhenyu Chen, Jie Guo, Shuichang Lai et al.
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
Fei Deng, Qifei Wang, Wei Wei et al.
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Elvis Dohmatob, Meyer Scetbon
PreciseControl: Enhancing Text-To-Image Diffusion Models with Fine-Grained Attribute Control
Rishubh Parihar, Sachidanand VS, Sabariswaran Mani et al.
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
Zidong Wang, Zeyu Lu, Di Huang et al.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Haoyu Li, Shichang Zhang, Longwen Tang et al.
Predicting Dose-Response Curves with Deep Neural Networks
Pedro A. Campana, Paul Prasse, Tobias Scheffer
Predicting Emergent Abilities with Infinite Resolution Evaluation
Shengding Hu, Xin Liu, Xu Han et al.
Predicting Lagrangian Multipliers for Mixed Integer Linear Programs
Francesco Demelas, Joseph Roux, Mathieu Lacroix et al.
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Yi Feng, Georgios Piliouras, Xiao Wang
Prediction Error-based Classification for Class-Incremental Learning
Michał Zając, Tinne Tuytelaars, Gido M van de Ven
Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
Yufan Liu, Wanqian Zhang, Dayan Wu et al.
Prediction-powered Generalization of Causal Inferences
Ilker Demirel, Ahmed Alaa, Anthony Philippakis et al.
Predictive auxiliary objectives in deep RL mimic learning in the brain
Ching Fang, Kimberly Stachenfeld
Predictive Coding beyond Correlations
Tommaso Salvatori, Luca Pinchetti, Amine M'Charrak et al.
Predictive Dynamic Fusion
Bing Cao, Yinan Xia, Yi Ding et al.
Predictive Performance Comparison of Decision Policies Under Confounding
Luke Guerdan, Amanda Coston, Ken Holstein et al.
PredToken: Predicting Unknown Tokens and Beyond with Coarse-to-Fine Iterative Decoding
Xuesong Nie, Haoyuan Jin, Yunfeng Yan et al.
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar, Anikait Singh, Archit Sharma et al.