Papers
Learning Control-Oriented Dynamical Structure from Data
Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan et al.
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi et al.
Learning Deep Time-index Models for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo et al.
Learning Dense Correspondences between Photos and Sketches
Xuanchen Lu, Xiaolong Wang, Judith E Fan
Learning Distributions over Quantum Measurement Outcomes
Weiyuan Gong, Scott Aaronson
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Yiming Cui, Linjie Yang, Haichao Yu
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Dominik Schnaus, Jongseok Lee, Daniel Cremers et al.
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Pengfei Li, Jianyi Yang, Shaolei Ren
Learning Functional Distributions with Private Labels
Changlong Wu, Yifan Wang, Ananth Grama et al.
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov et al.
Learning Globally Smooth Functions on Manifolds
Juan Cervino, Luiz F. O. Chamon, Benjamin David Haeffele et al.
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
Binyamin Perets, Mark Kozdoba, Shie Mannor
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan Lee, Alekh Agarwal, Christoph Dann et al.
Learning Instance-Specific Augmentations by Capturing Local Invariances
Ning Miao, Tom Rainforth, Emile Mathieu et al.
Learning Intuitive Policies Using Action Features
Mingwei Ma, Jizhou Liu, Samuel Sokota et al.
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Shengcao Cao, Mengtian Li, James Hays et al.
Learning Mixtures of Gaussians with Censored Data
Wai Ming Tai, Bryon Aragam
Learning Mixtures of Markov Chains and MDPs
Chinmaya Kausik, Kevin Tan, Ambuj Tewari
Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
Pingchuan Ma, Peter Yichen Chen, Bolei Deng et al.
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
Makoto Takamoto, Francesco Alesiani, Mathias Niepert
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George et al.
Learning Perturbations to Explain Time Series Predictions
Joseph Enguehard
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh et al.
Learning Preconditioners for Conjugate Gradient PDE Solvers
Yichen Li, Peter Yichen Chen, Tao Du et al.
Learning Prescriptive ReLU Networks
Wei Sun, Asterios Tsiourvas