Search Results for author: Zhiyuan Huang

Found 8 papers, 3 papers with code

AgentStudio: A Toolkit for Building General Virtual Agents

no code implementations26 Mar 2024 Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan

We have open-sourced the environments, datasets, benchmarks, and interfaces to promote research towards developing general virtual agents for the future.

Visual Grounding

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

no code implementations29 Oct 2023 Nan He, Hanyu Lai, Chenyang Zhao, Zirui Cheng, Junting Pan, Ruoyu Qin, Ruofan Lu, Rui Lu, Yunchen Zhang, Gangming Zhao, Zhaohui Hou, Zhiyuan Huang, Shaoqing Lu, Ding Liang, Mingjie Zhan

Based on TeacherLM-7. 1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting.

Data Augmentation Language Modelling

Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

1 code implementation3 Nov 2021 Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, Ding Zhao

Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

1 code implementation19 Jun 2021 Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.

Rare-Event Simulation for Neural Network and Random Forest Predictors

no code implementations10 Oct 2020 Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.

BIG-bench Machine Learning

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems

2 code implementations28 Jun 2020 Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao

Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.

Evaluation Uncertainty in Data-Driven Self-Driving Testing

no code implementations19 Apr 2019 Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao

These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.

Autonomous Vehicles

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

no code implementations1 Oct 2017 Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao

The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.

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