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Demystifying the Optimal Performance of Multi-Class Classification
Inspired by the work by Ishida et al. (2023), we propose an estimator
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin, Bouaziz Sofien, Leonidas J. Guibas
Neural Information Processing Systems http://nips.cc/
64792f7bd5d400c9ac310c6fef97ef2d-Paper-Conference.pdf
Synthesizing novel photo-realistic views of captured 3D scenes is important for many domains including virtual/augmented reality, video games or movie productions.
From Stochastic Planning to Marginal MAP
Hao(Jackson) Cui, Radu Marinescu, Roni Khardon
Neural Information Processing Systems http://nips.cc/
Cross-modal Learningfor Image-Guided Point Cloud Shape Completion
Theimageencoderisbuiltwitha ResNet18 [36] as 6 Table 1: Mean 3).
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher
Neural Information Processing Systems http://nips.cc/
Continual Unsupervised Representation Learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell
Continual learning aims to improve the ability of modern learning systems todeal with non-stationary distributions, typically by attempting to learn a seriesof tasks sequentially.
Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
We therefore assume that the algorithm has access to an oracle that knows intuitively what it means to be fair, but cannot explicitly enunciate the fairness metric.
