Oceania
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar, Du Tran, Lorenzo Torresani
Toeasethelearning, wedemonstrate thatitisbeneficial toadoptacurriculum learning strategy [23], where harder negatives are introduced after an initial stage of learning on easiernegatives.
Sublinear Time Low-Rank Approximation of Distance Matrices
In an attempt to reduce their description size, we study low rank approximation of such matrices.
The Learnability of In-Context Learning
Our theoretical analysis reveals that in this setting, in-context learning is more about identifying the task than about learning it, a result which is in line with a series of recent empirical findings.
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar
Neural Information Processing Systems http://nips.cc/
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew G. Wilson
The loss functions of deep neural networks are complex and their geometric properties are not well understood.
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more andmore details.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Neural Information Processing Systems http://nips.cc/
