Statistical Learning
The Effect of Network Width on the Performance of Large-batch Training
Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris Papailiopoulos, Paraschos Koutris
Neural Information Processing Systems http://nips.cc/
Hierarchical Graph Representation Learning with Differentiable Pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec
This lack of hierarchical structure is especially problematic for the task of graph classification, where the goal is to predict thelabelassociatedwithan entiregraph.
78f9c04bdcb06f1ada3902912d8b64ba-Supplemental-Datasets_and_Benchmarks.pdf
The Corpus dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv.
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Neural Information Processing Systems http://nips.cc/
Provable Tempered Overfitting of Minimal Nets and Typical Nets
For both learning rules, we prove overfitting is tempered.
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization Haitz Sáez de Ocáriz Borde Oxford Robotics Institute University of Oxford Álvaro Arroyo
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure.
DppNet: Approximating Determinantal Point Processes with Deep Networks
Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek
Neural Information Processing Systems http://nips.cc/
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Hajin Shim, Sung Ju Hwang, Eunho Yang
Neural Information Processing Systems http://nips.cc/
Towards a Zero-One Law for Column Subset Selection
Zhao Song, David Woodruff, Peilin Zhong
Indeed, this is a central question of recent workstudyinggeneralized lowrankmodels.