Generative Pretraining from Pixels - OpenAI

Language Models are Unsupervised Multitask Learners

Language Models are Unsupervised Multitask Learners Alec Radford * 1Jeffrey Wu Rewon Child David Luan 1Dario Amodei ** Ilya Sutskever ** 1 Abstract Natural language processing tasks, such as ques-tion answering, machine translation, reading com-

Benchmarking Safe Exploration in Deep Reinforcement …

range of prior work on safe reinforcement learning, we propose to standardize constrained RL as the main formalism for safe exploration. Second, we present the Safety Gym benchmark suite, a new slate of high-dimensional continuous control environments for measuring research progress on constrained RL. Finally, we

  Control, Learning, Reinforcement, Reinforcement learning

WebGPT: Browser-assisted question-answering with human ...

human feedback. To make human evaluation of factual accuracy easier, models ... or negative ways of talking about people’s religion, skin color, ability, or gender [3]. Often, people say bad words when they are experiencing strong emotions, …

  Feedback, Negative

Jukebox: A Generative Model for Music - OpenAI

frequencies perceptible to humans. As an example, a four-minute-long audio segment will have an input length of ˘10 million, where each position can have 16 bits of information. In comparison, a high-resolution RGB image with 1024 1024 pixels has an input length of ˘3 million, and each position has 24 bits of information. This makes learning

  Frequencies, Jukebox

Improving Language Understanding by Generative Pre …

Improving Language Understanding by Generative Pre-Training Alec Radford OpenAI alec@openai.com Karthik Narasimhan OpenAI karthikn@openai.com Tim Salimans OpenAI tim@openai.com Ilya Sutskever OpenAI ilyasu@openai.com Abstract Natural language understanding comprises a wide range of diverse tasks such

  Language, Understanding, Improving, Generative, Improving language understanding by generative pre, Language understanding

Dota 2 with Large Scale Deep Reinforcement Learning

Dota 2 with Large Scale Deep Reinforcement Learning OpenAI, ChristopherBerner,GregBrockman,BrookeChan,VickiCheung, Przemysław“Psyho"Dębiak,ChristyDennison ...

  Learning, Deep, Reinforcement, Otda, Deep reinforcement learning

Formal Mathematics Statement Curriculum Learning

more automation (such as more domain-specific statements generator or even informal to formal machine translation). 1.1. miniF2F benchmark In this work, we target the miniF2F (Zheng et al.,2021) benchmark, which consists of 244 validation and 244 test formalized statements of mathematical problems from var-ious competitions.

  Automation

Training language models to follow instructions with human ...

language models with human intent. 1 Introduction Large language models (LMs) can be “prompted” to perform a range of natural language process-ing (NLP) tasks, given some examples of the task as input. However, these models often express unintended behaviors such as making up facts, generating biased or toxic text, or simply not following

  Language, Model, Language model

Learning Transferable Visual Models From Natural …

of learning from natural language supervision. We study the scalability of CLIP by training a series of eight models spanning almost 2 orders of magnitude of compute and ob-serve that transfer performance is a smoothly predictable function of …

  Form, Language, Model, Learning, Visual, Natural, Transferable, Learning transferable visual models from natural

AAAI-21 Accepted Paper List.1.29

! 4!! 228:!Rain!Streak!Removal!via!Dual!Graph!Convolutional!Network! Xueyang!Fu,!Qi!Qi,!Yurui!Zhu,!Xinghao!Ding,!Zheng*Jun!Zha!! 233:!RevMan:!Revenue*Aware!Multi*Task ...

A Joint Neural Model for Information Extraction with ...

et al. ,2015;Lin et al. 2016). Recently, some ef-forts (Wadden et al.,2019;Luan et al.,2019) re-visit global inference approaches by designing neu-ral networks with embedding features to jointly model multiple subtasks. However, these methods use separate local task-specific classifiers in the final layer and do not explicitly model the inter-

  Information, Model, Extraction, Neural, Neural model for information extraction

CvT: Introducing Convolutions to Vision Transformers

*This work is done when Haiping Wu was an intern at Microsoft. ... Transformer: first, to replace the existing Position-wise Lin-ear Projection for the attention operation with our Convolu-tional Projection, and second, to use our multi-stage struc-

How to pronounce chinese names

Li, Lin, Liu, Luo, Lü Ma, Qiao Wang, Wu, Xu, Yang, Yu, Ye Zhang, Zeng, Zhu, Zhou, Zou, Zhuang. 5 PinYin in common first names Chun Cong Gong Guo Hui Hua Hui Kuo Jian Juan Guang Kuan Liang Luo Long Lian Jun Jie jia Qing Qiang Qiu Que Qi Rui Shou Tian Xuan Xiang Xiao Xiong Xiu Xin Xie Xia

EASY Does It: Elderberry and COVID

3. Weng J-R, Lin C-S, Lai H-C, Lin Y-P, Wang C-Y, Tsai Y-C, Wu K -C, Huang S-H, Lin C-W, Antiviral activity of Sambucous FormosanaNakai ethanol extract and related phenolic acid constituents against human coronavirus NL63, Virus Res, 2019, 273:197767. 4. Chen C, Zuckerman D, Brantley S, Sharpe M, Childress K, Hoiczyk E, Pendleton A, Sambucus nigra

  Does, Easy, Elderberry, Covid, Easy does it, Elderberry and covid

Approved Translator List - ICBC

Approved Translator List Surname Given name Language Phone Email Tsitsi Foti Albanian 604-356-2601 Foti_t@yahoo.com Velaj Aurora Albanian aurora.velaj@gmail.com Vesho Temo (Rola) Albanian 604-254-8022 interpretationservices@mosaicbc.com Beyene Temesghen Amharic 778-834-4500 Temesghen9@gmail.com Kebede Moges Amharic 250-88-0805 …

  Lists, Approved, Translator, Approved translator list

Chinese romanization table - Library of Congress

林老师 Lin lao shi 韋大夫 Wei dai fu 白沙先生 Baisha xian sheng 晦菴先生 Huian xian sheng 蔣經國先生 Jiang Jingguo xian sheng 2. Titles, and titles of royalty. Syllables in a title should be separated and written in lower-case.

  Library, Congress, Library of congress

Gender and Family in Contemporary China

segregation has persisted for non-agricultural jobs over time (Wu and Wu 2008). Although the gender gap in earnings was low among urban workers, especially educated urban workers, in the early years of the reform era (Xie and Hannum 1996), the gender gap has increased over time (Hauser and Xie 2005).