Search Results for author: Jia Zeng

Found 23 papers, 8 papers with code

Learning Manipulation by Predicting Interaction

1 code implementation1 Jun 2024 Jia Zeng, Qingwen Bu, Bangjun Wang, Wenke Xia, Li Chen, Hao Dong, Haoming Song, Dong Wang, Di Hu, Ping Luo, Heming Cui, Bin Zhao, Xuelong Li, Yu Qiao, Hongyang Li

To this end, we propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction (MPI) and enhances the visual representation. Given a pair of keyframes representing the initial and final states, along with language instructions, our algorithm predicts the transition frame and detects the interaction object, respectively.

Representation Learning

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming

no code implementations19 Apr 2024 Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu

Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.

Embodied Understanding of Driving Scenarios

1 code implementation7 Mar 2024 Yunsong Zhou, Linyan Huang, Qingwen Bu, Jia Zeng, Tianyu Li, Hang Qiu, Hongzi Zhu, Minyi Guo, Yu Qiao, Hongyang Li

Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans.

Autonomous Driving Language Modelling +1

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications

no code implementations11 Jan 2024 Xijun Li, Fangzhou Zhu, Hui-Ling Zhen, Weilin Luo, Meng Lu, Yimin Huang, Zhenan Fan, Zirui Zhou, Yufei Kuang, Zhihai Wang, Zijie Geng, Yang Li, Haoyang Liu, Zhiwu An, Muming Yang, Jianshu Li, Jie Wang, Junchi Yan, Defeng Sun, Tao Zhong, Yong Zhang, Jia Zeng, Mingxuan Yuan, Jianye Hao, Jun Yao, Kun Mao

To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.

Decision Making Management

Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future

2 code implementations6 Dec 2023 Hongyang Li, Yang Li, Huijie Wang, Jia Zeng, Huilin Xu, Pinlong Cai, Li Chen, Junchi Yan, Feng Xu, Lu Xiong, Jingdong Wang, Futang Zhu, Chunjing Xu, Tiancai Wang, Fei Xia, Beipeng Mu, Zhihui Peng, Dahua Lin, Yu Qiao

With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.

Autonomous Driving

Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning

no code implementations18 Oct 2023 Yufei Kuang, Xijun Li, Jie Wang, Fangzhou Zhu, Meng Lu, Zhihai Wang, Jia Zeng, Houqiang Li, Yongdong Zhang, Feng Wu

Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently.

reinforcement-learning Reinforcement Learning (RL)

Geometric-aware Pretraining for Vision-centric 3D Object Detection

1 code implementation6 Apr 2023 Linyan Huang, Huijie Wang, Jia Zeng, Shengchuan Zhang, Liujuan Cao, Junchi Yan, Hongyang Li

We also conduct experiments on various image backbones and view transformations to validate the efficacy of our approach.

3D Object Detection Autonomous Driving +2

Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model

no code implementations1 Feb 2023 Zhihai Wang, Xijun Li, Jie Wang, Yufei Kuang, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu

Cut selection -- which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs -- heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected.

Distilling Focal Knowledge From Imperfect Expert for 3D Object Detection

no code implementations CVPR 2023 Jia Zeng, Li Chen, Hanming Deng, Lewei Lu, Junchi Yan, Yu Qiao, Hongyang Li

Specifically, a set of queries are leveraged to locate the instance-level areas for masked feature generation, to intensify feature representation ability in these areas.

3D Object Detection Knowledge Distillation +2

Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe

2 code implementations12 Sep 2022 Hongyang Li, Chonghao Sima, Jifeng Dai, Wenhai Wang, Lewei Lu, Huijie Wang, Jia Zeng, Zhiqi Li, Jiazhi Yang, Hanming Deng, Hao Tian, Enze Xie, Jiangwei Xie, Li Chen, Tianyu Li, Yang Li, Yulu Gao, Xiaosong Jia, Si Liu, Jianping Shi, Dahua Lin, Yu Qiao

As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.

Autonomous Driving

Learning to Reformulate for Linear Programming

no code implementations17 Jan 2022 Xijun Li, Qingyu Qu, Fangzhou Zhu, Jia Zeng, Mingxuan Yuan, Kun Mao, Jie Wang

In the past decades, a serial of traditional operation research algorithms have been proposed to obtain the optimum of a given LP in a fewer solving time.

Learning-Aided Heuristics Design for Storage System

no code implementations14 Jun 2021 Yingtian Tang, Han Lu, Xijun Li, Lei Chen, Mingxuan Yuan, Jia Zeng

Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts.

reinforcement-learning Reinforcement Learning (RL)

Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

no code implementations27 May 2021 Xijun Li, Weilin Luo, Mingxuan Yuan, Jun Wang, Jiawen Lu, Jie Wang, Jinhu Lu, Jia Zeng

Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.

Graph Embedding Management +1

Bilevel Learning Model Towards Industrial Scheduling

no code implementations10 Aug 2020 Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders

In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.


A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

no code implementations9 Aug 2020 Md. Milon Islam, Fakhri Karray, Reda Alhajj, Jia Zeng

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years.

COVID-19 Diagnosis

Transfer Learning-Based Outdoor Position Recovery with Telco Data

no code implementations10 Dec 2019 Yige Zhang, Aaron Yi Ding, Jorg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao

In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy.

Position Transfer Learning

Towards Big Topic Modeling

no code implementations17 Nov 2013 Jian-Feng Yan, Jia Zeng, Zhi-Qiang Liu, Yang Gao

Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly with the vocabulary size and the number of topics, leading to a serious scalability problem.

Fast Online EM for Big Topic Modeling

no code implementations8 Oct 2012 Jia Zeng, Zhi-Qiang Liu, Xiao-Qin Cao

The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA), which has been one of the most popular probabilistic topic modeling methods in the past decade.


A New Approach to Speeding Up Topic Modeling

no code implementations1 Apr 2012 Jia Zeng, Zhi-Qiang Liu, Xiao-Qin Cao

To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration.

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