Search Results for author: Bei Shi

Found 15 papers, 5 papers with code

On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm

2 code implementations6 Dec 2023 Peng Sun, Bei Shi, Daiwei Yu, Tao Lin

Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands.

Learning Diverse Policies in MOBA Games via Macro-Goals

no code implementations NeurIPS 2021 Yiming Gao, Bei Shi, Xueying Du, Liang Wang, Guangwei Chen, Zhenjie Lian, Fuhao Qiu, Guoan Han, Weixuan Wang, Deheng Ye, Qiang Fu, Wei Yang, Lanxiao Huang

Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings.

Dota 2

A Theoretical Analysis of the Repetition Problem in Text Generation

1 code implementation29 Dec 2020 Zihao Fu, Wai Lam, Anthony Man-Cho So, Bei Shi

The experimental results show that our theoretical framework is applicable in general generation models and our proposed rebalanced encoding approach alleviates the repetition problem significantly.

Text Generation

Towards Playing Full MOBA Games with Deep Reinforcement Learning

no code implementations NeurIPS 2020 Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.

Dota 2 reinforcement-learning +1

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations25 Nov 2020 Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

DHER: Hindsight Experience Replay for Dynamic Goals

1 code implementation ICLR 2019 Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang

Dealing with sparse rewards is one of the most important challenges in reinforcement learning (RL), especially when a goal is dynamic (e. g., to grasp a moving object).

Object Tracking Reinforcement Learning (RL)

Learning Domain-Sensitive and Sentiment-Aware Word Embeddings

no code implementations ACL 2018 Bei Shi, Zihao Fu, Lidong Bing, Wai Lam

Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot produce domain-sensitive embeddings.

Data Augmentation General Classification +4

Transformation Networks for Target-Oriented Sentiment Classification

2 code implementations ACL 2018 Xin Li, Lidong Bing, Wai Lam, Bei Shi

Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.

Aspect-Based Sentiment Analysis (ABSA) Classification +3

Jointly Learning Word Embeddings and Latent Topics

no code implementations21 Jun 2017 Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, Kwun Ping Lai

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus.

Learning Word Embeddings Topic Models

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