Asia
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Adil SALIM, Dmitry Koralev, Peter Richtarik
Neural Information Processing Systems http://nips.cc/
Online Pricing for Multi-User Multi-Item Markets
Since the provider's goal of maximizing revenue can only be achieved
Selecting the independent coordinates of manifolds with large aspect ratios
Neural Information Processing Systems http://nips.cc/
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Michelle Yuan, Benjamin Van Durme, Jordan L. Ying
Neural Information Processing Systems http://nips.cc/
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian
InMTL, there areDdifferent labeled domains where data are related and the goal is to improve the predictive power of all domains altogether.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Tharun Kumar Reddy Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava
Neural Information Processing Systems http://nips.cc/
Supplementary for Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning
Xiaoqian Wu Shanghai Jiao Tong University enlighten@sjtu.edu.cn In Tab. 1, we conclude the notations in this work for clarity.Notation Definition r A rule. The size of the premise symbols set M . S is the symbol set, and R is the rule set. A \ B The set difference of A and B. D A very large-scale activity images database.