Search Results for author: Wei Cao

Found 29 papers, 13 papers with code

Qubit-Wise Architecture Search Method for Variational Quantum Circuits

no code implementations7 Mar 2024 Jialin Chen, Zhiqiang Cai, Ke Xu, Di wu, Wei Cao

Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates.

Evolutionary Algorithms Neural Architecture Search +1

Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking

no code implementations CVPR 2024 Wei Cao, Chang Luo, Biao Zhang, Matthias Nießner, Jiapeng Tang

To address these challenges, we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations.

4D reconstruction Denoising +2

NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining

1 code implementation11 Oct 2023 Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu

In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.

Anomaly Detection Few-Shot Learning +3

What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving

no code implementations14 Sep 2023 Hongkuan Zhou, Aifen Sui, Wei Cao, Zhenshan Bing

More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time.

Autonomous Driving Imitation Learning +1

Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series

1 code implementation14 Jun 2023 Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li

Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.

Irregular Time Series Representation Learning +1

UADB: Unsupervised Anomaly Detection Booster

1 code implementation3 Jun 2023 Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian

This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.

Unsupervised Anomaly Detection

Autonomous Driving Simulator based on Neurorobotics Platform

no code implementations31 Dec 2022 Wei Cao, Liguo Zhou, Yuhong Huang, Alois Knoll

There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.

Autonomous Driving object-detection +1

OpenFE: Automated Feature Generation with Expert-level Performance

2 code implementations22 Nov 2022 Tianping Zhang, Zheyu Zhang, Zhiyuan Fan, Haoyan Luo, Fengyuan Liu, Qian Liu, Wei Cao, Jian Li

In the two competitions, features generated by OpenFE with a simple baseline model can beat 99. 3% and 99. 6% data science teams respectively.

Feature Importance

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

1 code implementation ICLR 2022 Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu

However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.

Scheduling Time Series +1

Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study

no code implementations9 Apr 2021 Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu

We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.

Pinpointing the Memory Behaviors of DNN Training

no code implementations1 Apr 2021 Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.

LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

no code implementations1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.

Graph Representation Learning

Dynamic Graph Representation Learning with Fourier Temporal State Embedding

1 code implementation1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.

Graph Embedding Graph Representation Learning

Forecast with Forecasts: Diversity Matters

no code implementations3 Dec 2020 Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li

In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features.

Computational Efficiency Diversity +4

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

2 code implementations NeurIPS 2020 Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.

imbalanced classification Meta-Learning

Nonparametric Estimation of the Fisher Information and Its Applications

no code implementations7 May 2020 Wei Cao, Alex Dytso, Michael Fauß, H. Vincent Poor, Gang Feng

First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived.

Relational State-Space Model for Stochastic Multi-Object Systems

no code implementations ICLR 2020 Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.

Object Time Series +1

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

1 code implementation27 Nov 2019 Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions.


Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 Sep 2019 Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu

To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.

Classification General Classification +1

LMVP: Video Predictor with Leaked Motion Information

no code implementations24 Jun 2019 Dong Wang, Yitong Li, Wei Cao, Liqun Chen, Qi Wei, Lawrence Carin

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.

On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs

no code implementations NeurIPS 2015 Wei Cao, Jian Li, Yufei Tao, Zhize Li

This paper discusses how to efficiently choose from $n$ unknowndistributions the $k$ ones whose means are the greatest by a certainmetric, up to a small relative error.

Multi-Armed Bandits

Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models

no code implementations24 May 2013 Yin Song, Longbing Cao, Xuhui Fan, Wei Cao, Jian Zhang

These sequence-level latent parameters for each sequence are modeled as latent Dirichlet random variables and parameterized by a set of deterministic database-level hyper-parameters.

General Classification

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