World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
52
Citations
28897
World Ranking
4942
National Ranking
2296

Overview

Patrice Y. Simard is affiliated with Microsoft in the United States. Their research contributions span multiple fields, mainly focusing on computer science and social sciences, with additional work in artificial intelligence, safety research, and computer vision and pattern recognition.

The scientist's work centers on key topics including:

  • Explainable Artificial Intelligence (XAI)
  • Ethics and Social Impacts of AI
  • Data Visualization and Analytics

One of their recent publications is titled Interactive machine teaching: a human-centered approach to building machine-learned models, published in 2020 in the journal Human-Computer Interaction. This work has been cited extensively, reflecting engagement with the research community.

Frequent collaborators in their research activities include Gonzalo Ramos, Christopher Meek, Jina Suh, and Soroush Ghorashi. These coauthors have contributed alongside Simard on projects that explore machine learning and human-centered approaches to AI.

The primary publication venue noted for this researcher's work is:

  • Human-Computer Interaction

Across their academic career, Simard has developed expertise in advancing methods related to interpretability and ethical considerations within AI systems. Their research involves both technical development and the social implications of deploying AI technologies.

Best Publications

  • Learning long-term dependencies with gradient descent is difficult

    Y. Bengio;P. Simard;P. Frasconi

  • Best practices for convolutional neural networks applied to visual document analysis

    P.Y. Simard;D. Steinkraus;J.C. Platt

  • Comparison of classifier methods: a case study in handwritten digit recognition

    L. Bottou;C. Cortes;C. Cortes;J.S. Denker;J.S. Denker;H. Drucker;H. Drucker

  • Learning algorithms for classification: A comparison on handwritten digit recognition

    Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;Corinna Cortes;Corinna Cortes

  • Comparison of learning algorithms for handwritten digit recognition

    Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;A. Brunot

  • Efficient Pattern Recognition Using a New Transformation Distance

    Patrice Simard;Patrice Simard;Yann LeCun;John S. Denker;John S. Denker

  • Counterfactual reasoning and learning systems: the example of computational advertising

    Léon Bottou;Jonas Peters;Joaquin Quiñonero-Candela;Denis X. Charles

  • High Performance Convolutional Neural Networks for Document Processing

    Kumar Chellapilla;Sidd Puri;Patrice Simard

  • Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation

    Patrice Simard;Yann LeCun;John S. Denker;Bernard Victorri

  • Prior Knowledge in Support Vector Kernels

    Bernhard Schölkopf;Patrice Simard;Alex J. Smola;Vladimir Vapnik

  • Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)

    Kumar Chellapilla;Patrice Y. Simard

  • High quality document image compression with "DjVu"

    Léon Bottou;Patrick Haffner;Paul G. Howard;Patrice Y. Simard

  • Building segmentation based human-friendly human interaction proofs (HIPs)

    Kumar Chellapilla;Kevin Larson;Patrice Y. Simard;Mary Czerwinski

  • Tangent Prop - A formalism for specifying selected invariances in an adaptive network

    Patrice Simard;Patrice Simard;Bernard Victorri;Yann LeCun;John Denker;John Denker

  • Using GPUs for machine learning algorithms

    D. Steinkraus;I. Buck;P.Y. Simard

  • The problem of learning long-term dependencies in recurrent networks

    Y. Bengio;P. Frasconi;P. Simard

  • Designing human friendly human interaction proofs (HIPs)

    Kumar Chellapilla;Kevin Larson;Patrice Simard;Mary Czerwinski

  • ModelTracker: Redesigning Performance Analysis Tools for Machine Learning

    Saleema Amershi;Max Chickering;Steven M. Drucker;Bongshin Lee

  • BOOSTING PERFORMANCE IN NEURAL NETWORKS

    Harris Drucker;Robert E. Schapire;Patrice Y. Simard

  • Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)

    Kumar Chellapilla;Kevin Larson;Patrice Y. Simard;Mary Czerwinski

Frequent Co-Authors

Kumar Chellapilla
Kumar Chellapilla Amazon Web Services
Léon Bottou
Léon Bottou Facebook (United States)
John S. Denker
John S. Denker Nokia (United States)
Henrique S. Malvar
Henrique S. Malvar Microsoft (United States)
Paul A. Viola
Paul A. Viola Microsoft (United States)
Yann LeCun
Yann LeCun Facebook (United States)
Steven M. Drucker
Steven M. Drucker Microsoft (United States)
David Grangier
David Grangier Google (United States)
Corinna Cortes
Corinna Cortes Google (United States)
Maneesh Agrawala
Maneesh Agrawala Stanford University

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