World's Best Scientists 2026 revealed!

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Computer Science

D-Index
46
Citations
6708
World Ranking
6944
National Ranking
3035

Overview

Qiang Liu is affiliated with The University of Texas at Austin in the United States. Their research spans multiple fields including Computer Science and Engineering, with a particular focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Aerospace Engineering, and Control and Systems Engineering.

Their research topics cover a diverse range with notable emphasis on Robotics and Sensor-Based Localization, Topic Modeling, Robotic Path Planning Algorithms, Neural and Behavioral Psychology Studies, Natural Language Processing Techniques, Advanced Neural Network Applications, and 3D Surveying and Cultural Heritage.

Qiang Liu has contributed extensively to several publication venues. The most frequent include:

  • arXiv (Cornell University)
  • Journal of Vision
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Evidence-Based Mental Health
  • The British Journal of Psychiatry

Recent papers by Qiang Liu or associated with their research include:

  • Language Is Not All You Need: Aligning Perception with Language Models, 2023, arXiv (Cornell University)
  • AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds, 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • LLM+P: Empowering Large Language Models with Optimal Planning Proficiency, 2023, arXiv (Cornell University)
  • Natural language processing for structuring clinical text data on depression using UK-CRIS, 2020, Evidence-Based Mental Health
  • Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model, 2022, BMC Medicine

Frequent coauthors who collaborate with Qiang Liu include:

  • Alejo Nevado-Holgado
  • Xunyu Zhong
  • Andrey Kormilitzin
  • Xungao Zhong
  • Nemanja Vaci

Best Publications

  • Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

    Qiang Liu;Dilin Wang

  • Variational Inference for Crowdsourcing

    Qiang Liu;Jian Peng;Alex T Ihler

  • Language Is Not All You Need: Aligning Perception with Language Models

    Unknown

  • A kernelized stein discrepancy for goodness-of-fit tests

    Qiang Liu;Jason D. Lee;Michael Jordan

  • Breaking the curse of horizon: Infinite-horizon off-policy estimation

    Qiang Liu;Lihong Li;Ziyang Tang;Dengyong Zhou

  • Stein Variational Gradient Descent as Gradient Flow

    Qiang Liu

  • Communication-efficient sparse regression

    Jason D. Lee;Qiang Liu;Yuekai Sun;Jonathan E. Taylor

  • Practical Human Sensing in the Light

    Tianxing Li;Qiang Liu;Xia Zhou

  • KeepAugment: A Simple Information-Preserving Data Augmentation Approach

    Chengyue Gong;Dilin Wang;Meng Li;Vikas Chandra

  • Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

    Dilin Wang;Qiang Liu

  • Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy

    Dengyong Zhou;Qiang Liu;John Platt;Christopher Meek

  • An Optimization View on Dynamic Routing Between Capsules

    Dilin Wang;Qiang Liu

  • Conflict-Averse Gradient Descent for Multi-task learning

    Bo Liu;Xingchao Liu;Xiaojie Jin;Peter Stone

  • Learning Scale Free Networks by Reweighted L1 regularization

    Qiang Liu;Alexander T. Ihler

  • SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions

    Mao Ye;Chengyue Gong;Qiang Liu

  • Stein variational policy gradient

    Yang Liu;Prajit Ramachandran;Qiang Liu;Jian Peng

  • The Tesserae Project: Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers

    Stephen M. Mattingly;Julie M. Gregg;Pino Audia;Ayse Elvan Bayraktaroglu

  • Variational algorithms for marginal MAP

    Qiang Liu;Alexander Ihler

  • Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing

    Shayan Mirjafari;Kizito Masaba;Ted Grover;Weichen Wang

  • Regularized Minimax Conditional Entropy for Crowdsourcing

    Dengyong Zhou;Qiang Liu;John C. Platt;Christopher Meek

  • Bounding the Partition Function using Holder's Inequality

    Qiang Liu;Alexander T. Ihler

  • On the Margin Theory of Feedforward Neural Networks

    Colin Wei;Jason D. Lee;Qiang Liu;Tengyu Ma

  • On the Discrimination-Generalization Tradeoff in GANs

    Pengchuan Zhang;Qiang Liu;Dengyong Zhou;Tao Xu

  • Learning to Draw Samples with Amortized Stein Variational Gradient Descent.

    Yihao Feng;Dilin Wang;Qiang Liu

Frequent Co-Authors

Jian Peng
Jian Peng University of Illinois at Urbana-Champaign
Alexander T. Ihler
Alexander T. Ihler University of California, Irvine
Dengyong Zhou
Dengyong Zhou Google (United States)
Jason D. Lee
Jason D. Lee Princeton University
Peter Stone
Peter Stone The University of Texas at Austin
Julie A. McCann
Julie A. McCann Imperial College London
Padhraic Smyth
Padhraic Smyth University of California, Irvine
Lorenzo Torresani
Lorenzo Torresani Facebook (United States)
Bogi Andersen
Bogi Andersen University of California, Irvine

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