Papers
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning
Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang et al.
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
Junsu Kim, Younggyo Seo, Jinwoo Shin
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
Keji He, Yan Huang, Qi Wu et al.
Landscape analysis of an improved power method for tensor decomposition
Joe Kileel, Timo Klock, João M Pereira
Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil et al.
Laplace Redux - Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer et al.
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim, Felix Hohne, Xiuyu Li et al.
Large-Scale Learning with Fourier Features and Tensor Decompositions
Frederiek Wesel, Kim Batselier
Large-Scale Unsupervised Object Discovery
Van Huy Vo, Elena Sizikova, Cordelia Schmid et al.
Large-Scale Wasserstein Gradient Flows
Petr Mokrov, Alexander Korotin, Lingxiao Li et al.
Last-iterate Convergence in Extensive-Form Games
Chung-Wei Lee, Christian Kroer, Haipeng Luo
Last iterate convergence of SGD for Least-Squares in the Interpolation regime.
Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Paul Haider, Benjamin Ellenberger, Laura Kriener et al.
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages
Xinyun Chen, Dawn Song, Yuandong Tian
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn, Richard Kurle, Maximilian Soelch et al.
Lattice partition recovery with dyadic CART
OSCAR HERNAN MADRID PADILLA, Yi Yu, Alessandro Rinaldo
LEADS: Learning Dynamical Systems that Generalize Across Environments
Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac et al.
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding
Yang Li, Si Si, Gang Li et al.
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
HanQin Cai, Jialin Liu, Wotao Yin
Learning 3D Dense Correspondence via Canonical Point Autoencoder
An-Chieh Cheng, Xueting Li, Min Sun et al.
Learning and Generalization in RNNs
Abhishek Panigrahi, Navin Goyal
Learning a Single Neuron with Bias Using Gradient Descent
Gal Vardi, Gilad Yehudai, Ohad Shamir
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
Antonios Antoniadis, Christian Coester, Marek Elias et al.