Europe
Learning Nonsymmetric Determinantal Point Processes
Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene
Neural Information Processing Systems http://nips.cc/
Should We Learn Most Likely Functions or Parameters? Shikai Qiu Tim G. J. Rudner Sanyam Kapoor Andrew Gordon Wilson New York University
Machine learning has matured to the point where we often take key design decisions for granted.
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová
Neural Information Processing Systems http://nips.cc/
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin
Neural Information Processing Systems http://nips.cc/
Time-series Generative Adversarial Networks
Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar
Neural Information Processing Systems http://nips.cc/
Objaverse-XL: A Universe of 10M+ 3D Objects
Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data.
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
In practice,θ is typically chosen as a neural network, for which it is often impossible to evaluate the induced densityµθ.
Bayesian Nonparametric Spectral Estimation
Neural Information Processing Systems http://nips.cc/
