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D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
18414
World Ranking
8536
National Ranking
2351

Overview

Brian Kulis is affiliated with Boston University in the United States, contributing to research primarily in the field of computer science. Their work encompasses extensive publications across various subfields and topics related to artificial intelligence and signal processing.

The researcher has produced a number of papers focusing on diverse areas of computer science. Notable recent publications include:

  • Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning, 2024, IEEE Transactions on Parallel and Distributed Systems
  • Deep Divergence Learning, 2020, arXiv (Cornell University)
  • Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer, 2020, arXiv (Cornell University)
  • Piecewise Linear Regression via a Difference of Convex Functions, 2020, arXiv (Cornell University)
  • Real-time Localized Photorealistic Video Style Transfer, 2020, arXiv (Cornell University)

Their frequent coauthors include:

  • Christopher Liao
  • Theodoros Tsiligkaridis
  • Mohammad Omar Khursheed
  • Christin Jose
  • Xide Xia

Brian Kulis has published extensively in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Transactions on Parallel and Distributed Systems
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • Interspeech 2022

Their research is anchored primarily in computer science with a focus on several subfields including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Signal Processing
  • Computer Networks and Communications
  • Statistics and Probability

The main topics covered in their work include:

  • Music and Audio Processing
  • Generative Adversarial Networks and Image Synthesis
  • Speech and Audio Processing
  • Image Enhancement Techniques
  • Advanced Image Processing Techniques
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning

Best Publications

  • Adapting visual category models to new domains

    Kate Saenko;Brian Kulis;Mario Fritz;Trevor Darrell

  • Information-theoretic metric learning

    Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra

  • Kernel k-means: spectral clustering and normalized cuts

    Inderjit S. Dhillon;Yuqiang Guan;Brian Kulis

  • Weighted Graph Cuts without Eigenvectors A Multilevel Approach

    I.S. Dhillon;Yuqiang Guan;B. Kulis

  • Kernelized locality-sensitive hashing for scalable image search

    Brian Kulis;Kristen Grauman

  • Learning to Hash with Binary Reconstructive Embeddings

    Brian Kulis;Trevor Darrell

  • Metric Learning: A Survey

    Brian Kulis

  • Semi-supervised graph clustering: a kernel approach

    Brian Kulis;Sugato Basu;Inderjit Dhillon;Raymond Mooney

  • What you saw is not what you get: Domain adaptation using asymmetric kernel transforms

    Brian Kulis;Kate Saenko;Trevor Darrell

  • Tracking evolving communities in large linked networks

    John Hopcroft;Omar Khan;Brian Kulis;Bart Selman

  • Kernelized Locality-Sensitive Hashing

    B. Kulis;K. Grauman

  • Fast image search for learned metrics

    P. Jain;B. Kulis;K. Grauman

  • Revisiting k-means: New Algorithms via Bayesian Nonparametrics

    Brian Kulis;Michael I. Jordan

  • Fast Similarity Search for Learned Metrics

    B. Kulis;P. Jain;K. Grauman

  • Low-Rank Kernel Learning with Bregman Matrix Divergences

    Brian Kulis;Mátyás A. Sustik;Inderjit S. Dhillon

  • W-Net: A Deep Model for Fully Unsupervised Image Segmentation

    Xide Xia;Brian Kulis

  • Deep Metric Learning to Rank

    Fatih Cakir;Kun He;Xide Xia;Brian Kulis

  • Online Metric Learning and Fast Similarity Search

    Prateek Jain;Brian Kulis;Inderjit S. Dhillon;Kristen Grauman

  • Learning low-rank kernel matrices

    Brian Kulis;Mátyás Sustik;Inderjit Dhillon

  • Discovering Latent Domains for Multisource Domain Adaptation

    Judy Hoffman;Brian Kulis;Trevor Darrell;Kate Saenko

Frequent Co-Authors

Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Kate Saenko
Kate Saenko Boston University
Prateek Jain
Prateek Jain Google (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Kristen Grauman
Kristen Grauman The University of Texas at Austin
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Venkatesh Saligrama
Venkatesh Saligrama Boston University
David A. Castanon
David A. Castanon Boston University

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