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Overview

Hendrik Strobelt is a researcher affiliated with IBM in the United States, specializing in computer science. Their research work is primarily focused on artificial intelligence and related subfields, including computer vision and pattern recognition, molecular biology, computational theory and mathematics, and computer networks and communications.

The main topics explored in Strobelt's research encompass:

  • Topic Modeling
  • Explainable Artificial Intelligence (XAI)
  • Natural Language Processing Techniques
  • Data Visualization and Analytics
  • Machine Learning and Data Classification
  • Adversarial Robustness in Machine Learning
  • Multimodal Machine Learning Applications

Strobelt has authored publications in several scholarly venues, with frequent contributions to the following journals and conference proceedings:

  • arXiv (Cornell University)
  • IEEE Transactions on Visualization and Computer Graphics
  • IEEE Computer Graphics and Applications
  • Proceedings of the National Academy of Sciences
  • Science Advances

Recent papers authored or coauthored by Strobelt include:

  • "Understanding the role of individual units in a deep neural network," 2020, Proceedings of the National Academy of Sciences
  • "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions," 2021, Science Advances
  • "Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models," 2022, IEEE Transactions on Visualization and Computer Graphics
  • "The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics," 2021, arXiv (Cornell University)
  • "ConfusionFlow: A Model-Agnostic Visualization for Temporal Analysis of Classifier Confusion," 2020, IEEE Transactions on Visualization and Computer Graphics

Their frequent collaborators include:

  • Benjamin Hoover
  • Hanspeter Pfister
  • Angie Boggust
  • Duen Horng Chau
  • Mennatallah El-Assady

Best Publications

  • UpSet: Visualization of Intersecting Sets.

    Alexander Lex;Nils Gehlenborg;Hendrik Strobelt;Romain Vuillemot

  • HiGlass: web-based visual exploration and analysis of genome interaction maps

    Peter Kerpedjiev;Nezar Abdennur;Fritz Lekschas;Chuck McCallum

  • LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

    Hendrik Strobelt;Sebastian Gehrmann;Hanspeter Pfister;Alexander M. Rush

  • Semantic photo manipulation with a generative image prior

    David Bau;Hendrik Strobelt;William Peebles;Jonas Wulff

  • GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

    David Bau;Jun-Yan Zhu;Hendrik Strobelt;Bolei Zhou

  • Understanding the role of individual units in a deep neural network.

    David Bau;Jun-Yan Zhu;Hendrik Strobelt;Agata Lapedriza

  • Seeing What a GAN Cannot Generate

    David Bau;Jun-Yan Zhu;Jonas Wulff;William Peebles

  • Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

    Payel Das;Payel Das;Tom Sercu;Tom Sercu;Kahini Wadhawan;Inkit Padhi

  • Extraction of organic chemistry grammar from unsupervised learning of chemical reactions.

    Philippe Schwaller;Philippe Schwaller;Benjamin Hoover;Jean-Louis Reymond;Hendrik Strobelt

  • GLTR: Statistical Detection and Visualization of Generated Text

    Sebastian Gehrmann;Hendrik Strobelt;Alexander M. Rush

  • S eq 2s eq -V is : A Visual Debugging Tool for Sequence-to-Sequence Models

    Hendrik Strobelt;Sebastian Gehrmann;Michael Behrisch;Adam Perer

  • exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models

    Benjamin Hoover;Hendrik Strobelt;Sebastian Gehrmann

  • The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

    Sebastian Gehrmann;Tosin Adewumi;Karmanya Aggarwal;Pawan Sasanka Ammanamanchi

  • Document Cards: A Top Trumps Visualization for Documents

    H. Strobelt;D. Oelke;C. Rohrdantz;A. Stoffel

  • Interactive Level-of-Detail Rendering of Large Graphs

    M. Zinsmaier;U. Brandes;O. Deussen;H. Strobelt

  • Gene mention normalization and interaction extraction with context models and sentence motifs

    Jörg Hakenberg;Jörg Hakenberg;Jörg Hakenberg;Conrad Plake;Loic Royer;Hendrik Strobelt

  • Seeing What a GAN Cannot Generate

    David Bau;Jun-Yan Zhu;Jonas Wulff;William Peebles

  • Rolled-out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives

    H. Strobelt;M. Spicker;A. Stoffel;D. Keim

  • NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity

    Ali K. Al-Awami;Johanna Beyer;Hendrik Strobelt;Narayanan Kasthuri

  • exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models

    Benjamin Hoover;Hendrik Strobelt;Sebastian Gehrmann

Frequent Co-Authors

Hanspeter Pfister
Hanspeter Pfister Harvard University
Alexander M. Rush
Alexander M. Rush Cornell University
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Bolei Zhou
Bolei Zhou University of California, Los Angeles
David Bau
David Bau Northeastern University
Oliver Deussen
Oliver Deussen University of Konstanz
Adam Perer
Adam Perer Carnegie Mellon University
Aleksandra Mojsilovic
Aleksandra Mojsilovic IBM (United States)

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