Search Results for author: Yuxuan Liu

Found 31 papers, 10 papers with code

Disentangle Estimation of Causal Effects from Cross-Silo Data

no code implementations4 Jan 2024 Yuxuan Liu, Haozhao Wang, Shuang Wang, Zhiming He, Wenchao Xu, Jialiang Zhu, Fan Yang

Estimating causal effects among different events is of great importance to critical fields such as drug development.

Democratizing Reasoning Ability: Tailored Learning from Large Language Model

1 code implementation20 Oct 2023 Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.

Instruction Following Language Modelling +1

PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers

no code implementations28 Sep 2023 Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling.

Calibrating LLM-Based Evaluator

no code implementations23 Sep 2023 Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation.

In-Context Learning Language Modelling +1

Self-Supervised Instance Segmentation by Grasping

no code implementations10 May 2023 Yuxuan Liu, Xi Chen, Pieter Abbeel

Leveraging this insight, we learn a grasp segmentation model to segment the grasped object from before and after grasp images.

Instance Segmentation Robotic Grasping +2

Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN

no code implementations3 May 2023 Yuxuan Liu, Nikhil Mishra, Pieter Abbeel, Xi Chen

Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications.

Instance Segmentation Object Recognition +2

Random Feature Models for Learning Interacting Dynamical Systems

1 code implementation11 Dec 2022 Yuxuan Liu, Scott G. McCalla, Hayden Schaeffer

Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behavior of complex interacting systems.

Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction

no code implementations13 Oct 2022 Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, Pieter Abbeel, Xi Chen

3D bounding boxes are a widespread intermediate representation in many computer vision applications.

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

1 code implementation23 Aug 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).


RNGDet: Road Network Graph Detection by Transformer in Aerial Images

no code implementations16 Feb 2022 Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, Lujia Wang

To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.

Imitation Learning Motion Planning

csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

no code implementations11 Nov 2021 Zhenhua Xu, Yuxuan Liu, Lu Gan, Xiangcheng Hu, Yuxiang Sun, Ming Liu, Lujia Wang

To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation.

Autonomous Driving Boundary Detection +1

Unified Group Fairness on Federated Learning

no code implementations9 Nov 2021 Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao

We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Attribute Fairness +1

Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

no code implementations18 Jul 2021 Peide Cai, Hengli Wang, Huaiyang Huang, Yuxuan Liu, Ming Liu

In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs.

Autonomous Driving Car Racing +3

YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection

1 code implementation17 Mar 2021 Yuxuan Liu, Lujia Wang, Ming Liu

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs.

3D Object Detection From Stereo Images Disparity Estimation +3

ATG-PVD: Ticketing Parking Violations on A Drone

no code implementations21 Aug 2020 Hengli Wang, Yuxuan Liu, Huaiyang Huang, Yuheng Pan, Wenbin Yu, Jialin Jiang, Dianbin Lyu, Mohammud J. Bocus, Ming Liu, Ioannis Pitas, Rui Fan

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD).

Optical Flow Estimation

Difference Attention Based Error Correction LSTM Model for Time Series Prediction

no code implementations30 Mar 2020 Yuxuan Liu, Jiangyong Duan, Juan Meng

In this paper, we propose a novel model for time series prediction in which difference-attention LSTM model and error-correction LSTM model are respectively employed and combined in a cascade way.

Time Series Time Series Prediction

Learning to Drive from Simulation without Real World Labels

no code implementations10 Dec 2018 Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, Alex Kendall

Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems.

Image-to-Image Translation Translation

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

1 code implementation11 Jul 2017 YuXuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator.

Imitation Learning Translation +1

Building Program Vector Representations for Deep Learning

1 code implementation11 Sep 2014 Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang

In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis.

Representation Learning

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