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Kilian Q. Weinberger

Kilian Q. Weinberger

D-Index & Metrics

Computer Science

D-Index
82
Citations
91728
World Ranking
922
National Ranking
500

Overview

Kilian Q. Weinberger is affiliated with Cornell University in the United States. Their research primarily falls within the field of Computer Science, with a significant focus on Artificial Intelligence and its applications. The scientist's work spans several interconnected subfields, including Computer Vision and Pattern Recognition, Automotive Engineering, Atomic and Molecular Physics and Optics, and Materials Chemistry.

The main topics of Kilian Q. Weinberger's research output include:

  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Autonomous Vehicle Technology and Safety
  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition

The scientist has a substantial publication record, frequently contributing to venues such as:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Proceedings of the National Academy of Sciences

Notable recent papers include:

  • Language-driven Semantic Segmentation, 2022, arXiv (Cornell University)
  • Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Revisiting Few-sample BERT Fine-tuning, 2020, arXiv (Cornell University)
  • Wasserstein Distances for Stereo Disparity Estimation, 2020, arXiv (Cornell University)
  • Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Kilian Q. Weinberger frequently collaborates with several co-authors, including:

  • Mark Campbell
  • Bharath Hariharan
  • Wei-Lun Chao
  • Yurong You
  • Katie Z Luo

Best Publications

  • Densely Connected Convolutional Networks

    Gao Huang;Zhuang Liu;Laurens van der Maaten;Kilian Q. Weinberger

  • Distance Metric Learning for Large Margin Nearest Neighbor Classification

    Kilian Q. Weinberger;Lawrence K. Saul

  • On calibration of modern neural networks

    Chuan Guo;Geoff Pleiss;Yu Sun;Kilian Q. Weinberger

  • BERTScore: Evaluating Text Generation with BERT

    Tianyi Zhang;Varsha Kishore;Felix Wu;Kilian Q. Weinberger

  • Distance Metric Learning for Large Margin Nearest Neighbor Classification

    Kilian Q. Weinberger;John Blitzer;Lawrence K. Saul

  • Unsupervised Learning of Image Manifolds by Semidefinite Programming

    Kilian Q. Weinberger;Lawrence K. Saul

  • Deep Networks with Stochastic Depth

    Gao Huang;Yu Sun;Zhuang Liu;Daniel Sedra

  • From Word Embeddings To Document Distances

    Matt Kusner;Yu Sun;Nicholas Kolkin;Kilian Weinberger

  • Simplifying Graph Convolutional Networks

    Felix Wu;Tianyi Zhang;Amauri Holanda de Souza;Christopher Fifty

  • Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

    Yan Wang;Wei-Lun Chao;Divyansh Garg;Bharath Hariharan

  • Feature hashing for large scale multitask learning

    Kilian Weinberger;Anirban Dasgupta;John Langford;Alex Smola

  • Compressing Neural Networks with the Hashing Trick

    Wenlin Chen;James Wilson;Stephen Tyree;Stephen Tyree;Kilian Weinberger

  • CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

    Gao Huang;Shichen Liu;Laurens van der Maaten;Kilian Q. Weinberger

  • Simplifying Graph Convolutional Networks

    Felix Wu;Amauri H. Souza;Tianyi Zhang;Christopher Fifty

  • Proceedings of the 26th International Conference on Neural Information Processing Systems

    C. J. C. Burges;L. Bottou;M. Welling;Z. Ghahramani

  • Marginalized Denoising Autoencoders for Domain Adaptation

    Minmin Chen;Zhixiang Xu;Kilian Weinberger;Fei Sha

  • Snapshot Ensembles: Train 1, Get M for Free

    Gao Huang;Yixuan Li;Geoff Pleiss;Zhuang Liu

  • Learning a kernel matrix for nonlinear dimensionality reduction

    Kilian Q. Weinberger;Fei Sha;Lawrence K. Saul

  • Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

    Joshua Sarfaty Siegel;Lenny E. Ramsey;Abraham Z. Snyder;Nicholas V. Metcalf

  • Marginalized Denoising Autoencoders for Domain Adaptation

    Minmin Chen;Zhixiang Xu;Fei Sha;Kilian Q. Weinberger

  • Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1

    Z. Ghahramani;M. Welling;C. Cortes;N. D. Lawrence

  • On Fairness and Calibration

    Geoff Pleiss;Manish Raghavan;Felix Wu;Jon M. Kleinberg

  • GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

    Jacob R. Gardner;Geoff Pleiss;Kilian Q. Weinberger;David Bindel

  • Feature Hashing for Large Scale Multitask Learning

    Kilian Weinberger;Anirban Dasgupta;Josh Attenberg;John Langford

Frequent Co-Authors

Gao Huang
Gao Huang Tsinghua University
Laurens van der Maaten
Laurens van der Maaten Facebook (United States)
Mark Campbell
Mark Campbell Cornell University
Lawrence K. Saul
Lawrence K. Saul University of California, San Diego
Fei Sha
Fei Sha Facebook (United States)
Yixin Chen
Yixin Chen Washington University in St. Louis
Olivier Chapelle
Olivier Chapelle Google (United States)
Andrew Gordon Wilson
Andrew Gordon Wilson New York University
Malcolm Slaney
Malcolm Slaney Stanford University
Léon Bottou
Léon Bottou Facebook (United States)

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