Controlling Commercial Cooling Systems Using Reinforcement Learning

no code implementations • 11 Nov 2022 • Jerry Luo, Cosmin Paduraru, Octavian Voicu, Yuri Chervonyi, Scott Munns, Jerry Li, Crystal Qian, Praneet Dutta, Jared Quincy Davis, Ningjia Wu, Xingwei Yang, Chu-Ming Chang, Ted Li, Rob Rose, Mingyan Fan, Hootan Nakhost, Tinglin Liu, Brian Kirkman, Frank Altamura, Lee Cline, Patrick Tonker, Joel Gouker, Dave Uden, Warren Buddy Bryan, Jason Law, Deeni Fatiha, Neil Satra, Juliet Rothenberg, Mandeep Waraich, Molly Carlin, Satish Tallapaka, Sims Witherspoon, David Parish, Peter Dolan, Chenyu Zhao, Daniel J. Mankowitz

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.

Management reinforcement-learning +1

Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search

1 code implementation • 12 Aug 2022 • Yiming Qiu, Chenyu Zhao, Han Zhang, Jingwei Zhuo, TianHao Li, Xiaowei Zhang, Songlin Wang, Sulong Xu, Bo Long, Wen-Yun Yang

BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.

Intent Detection Question Answering +3

Polynomial Approximation of Discounted Moments

no code implementations • 30 Oct 2021 • Chenyu Zhao, Misha van Beek, Peter Spreij, Makhtar Ba

We introduce an approximation strategy for the discounted moments of a stochastic process that can, for a large class of problems, approximate the true moments.