Papers citing "Language Models are Few-Shot Learners"

12 / 12,362 papers shown

Title
Distributed Learning of Deep Neural Networks using Independent Subnet Training John Shelton Hyatt Cameron R. Wolfe Michael Lee Yuxin Tang Anastasios Kyrillidis Christopher M. Jermaine OOD 92 39 0 04 Oct 2019
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations Chanatip Saetia Ekapol Chuangsuwanich Tawunrat Chalothorn P. Vateekul 74 5 0 04 Aug 2019
Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods Aditya Mogadala M. Kalimuthu Dietrich Klakow VLM 141 136 0 22 Jul 2019
Norms for Beneficial A.I.: A Computational Analysis of the Societal Value Alignment Problem Pedro M. Fernandes Francisco C. Santos Manuel Lopes 45 11 0 26 Jun 2019
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? Tianxing He Jingzhao Zhang Zhiming Zhou James R. Glass 101 32 0 25 May 2019
An Information Theoretic Interpretation to Deep Neural Networks Shao-Lun Huang Xiangxiang Xu Lizhong Zheng G. Wornell FAtt 95 44 0 16 May 2019
An Attentive Survey of Attention Models S. Chaudhari Varun Mithal Gungor Polatkan R. Ramanath 192 666 0 05 Apr 2019
A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Zahra Atashgahi Joost Pieterse Shiwei Liu Decebal Constantin Mocanu Raymond N. J. Veldhuis Mykola Pechenizkiy 83 15 0 17 Mar 2019
Investigating Antigram Behaviour using Distributional Semantics Saptarshi Sengupta 36 0 0 15 Jan 2019
Automated Machine Learning: From Principles to Practices Quanming Yao Mengshuo Wang Hugo Jair Escalante Huan Zhao Qiang Yang 117 259 0 31 Oct 2018
Deep Learning for Genomics: A Concise Overview Tianwei Yue Yuanxin Wang Longxiang Zhang Chunming Gu Haohan Wang Wenping Wang Qi Lyu Yujie Dun AILaw VLM BDL 86 91 0 02 Feb 2018
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 100 7 0 31 May 2016