Poster "uncertainty estimation" Papers
35 papers found
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho et al.
Causal Discovery via Bayesian Optimization
Bao Duong, Sunil Gupta, Thin Nguyen
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar et al.
Do LLMs estimate uncertainty well in instruction-following?
Juyeon Heo, Miao Xiong, Christina Heinze-Deml et al.
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation
Maregu Assefa, Muzammal Naseer, IYYAKUTTI IYAPPAN GANAPATHI et al.
Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection
Yinjie Min, Furong Xu, Xinyao Li et al.
Multi-domain Distribution Learning for De Novo Drug Design
Arne Schneuing, Ilia Igashov, Adrian Dobbelstein et al.
Neural Context Flows for Meta-Learning of Dynamical Systems
Roussel Desmond Nzoyem, David Barton, Tom Deakin
Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David Blei
SEGA: Shaping Semantic Geometry for Robust Hashing under Noisy Supervision
Yiyang Gu, Bohan Wu, Qinghua Ran et al.
Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
Guoxuan Xia, Olivier Laurent, Gianni Franchi et al.
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han et al.
Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data
Xingyu Ren, Pengwei Liu, Pengkai Wang et al.
Uncertainty modeling for fine-tuned implicit functions
Anna Susmelj, Mael Macuglia, Natasa Tagasovska et al.
Uncertainty Weighted Gradients for Model Calibration
Jinxu Lin, Linwei Tao, Minjing Dong et al.
Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling
Guillem Capellera, Antonio Rubio, Luis Ferraz et al.
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
Sebastian Gregor Gruber, Florian Buettner
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Weijie Tu, Weijian Deng, Dylan Campbell et al.
cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process
Yihang Chen, TSAI HOR CHAN, Guosheng Yin et al.
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
Yanfei Zhou, Lars Lindemann, Matteo Sesia
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
Ha Manh Bui, Anqi Liu
Depth-guided NeRF Training via Earth Mover’s Distance
Anita Rau, Josiah Aklilu, Floyd C Holsinger et al.
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
Johannes Zenn, Robert Bamler
Efficient Exploration for LLMs
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao et al.
Enabling Uncertainty Estimation in Iterative Neural Networks
Nikita Durasov, Doruk Oner, Jonathan Donier et al.
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Xixun Lin, Wenxiao Zhang, Fengzhao Shi et al.
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
JoonHo Lee, Jae Oh Woo, Juree Seok et al.
Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Yunfei Long, Zilin Tian, Liguo Zhang et al.
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen, Qinghua Tao, Francesco Tonin et al.
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Shenghua Wan, Ziyuan Chen, Le Gan et al.
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Anthony Chen, Huanrui Yang, Yulu Gan et al.
Transitional Uncertainty with Layered Intermediate Predictions
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Uncertainty Estimation by Density Aware Evidential Deep Learning
Taeseong Yoon, Heeyoung Kim
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation
Pei Liu, Luping Ji