World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
9334
World Ranking
7143
National Ranking
3132

Overview

Douglas Eck is affiliated with Google in the United States and has an extensive research portfolio primarily focused on computer science. Their work covers a range of subfields including artificial intelligence, computer vision and pattern recognition, management science and operations research, information systems, and signal processing.

The scientist's research includes significant contributions to topics such as topic modeling, natural language processing techniques, multimodal machine learning applications, reinforcement learning in robotics, data quality and management, web data mining and analysis, and advanced malware detection techniques.

Douglas Eck's recent published papers include:

  • PaLM: Scaling Language Modeling with Pathways, 2022, arXiv (Cornell University)
  • Deduplicating Training Data Makes Language Models Better, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Gemma: Open Models Based on Gemini Research and Technology, 2024, arXiv (Cornell University)
  • A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, 2023, arXiv (Cornell University)
  • Emergent Social Learning via Multi-agent Reinforcement Learning, 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Douglas Eck include Daphne Ippolito, Katherine Lee, Chris Callison-Burch, Chiyuan Zhang, and Nicholas Carlini. The partnership patterns suggest active involvement in collaborative research efforts.

The scientist's works have appeared predominantly in the following publication venues:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Best Publications

  • A Neural Representation of Sketch Drawings

    David Ha;Douglas Eck

  • Neural audio synthesis of musical notes with WaveNet autoencoders

    Jesse Engel;Cinjon Resnick;Adam Roberts;Sander Dieleman

  • Aggregate features and ADABOOST for music classification

    James Bergstra;Norman Casagrande;Dumitru Erhan;Douglas Eck

  • LEARNING FEATURES FROM MUSIC AUDIO WITH DEEP BELIEF NETWORKS

    Philippe Hamel;Douglas Eck

  • A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

    Adam Roberts;Jesse H. Engel;Colin Raffel;Curtis Hawthorne

  • Music Transformer: Generating Music with Long-Term Structure

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

  • A First Look at Music Composition using LSTM Recurrent Neural Networks

    Douglas Eck;Juergen Schmidhuber

  • Finding temporal structure in music: blues improvisation with LSTM recurrent networks

    D. Eck;J. Schmidhuber

  • Applying LSTM to Time Series Predictable through Time-Window Approaches

    Felix Gers;Douglas Eck;Jürgen Schmidhuber

  • Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.

    Curtis Hawthorne;Andriy Stasyuk;Adam Roberts;Ian Simon

  • Automatic Generation of Social Tags for Music Recommendation

    Douglas Eck;Paul Lamere;Thierry Bertin-mahieux;Stephen Green

  • Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

    Jesse Engel;Cinjon Resnick;Adam Roberts;Sander Dieleman

  • Deduplicating Training Data Makes Language Models Better

    Katherine Lee;Daphne Ippolito;Andrew Nystrom;Chiyuan Zhang

  • Online and linear-time attention by enforcing monotonic alignments

    Colin Raffel;Minh-Thang Luong;Peter J. Liu;Ron J. Weiss

  • Automatic Detection of Generated Text is Easiest when Humans are Fooled

    Daphne Ippolito;Daniel Duckworth;Chris Callison-Burch;Douglas Eck

  • Onsets and Frames: Dual-Objective Piano Transcription

    Curtis Hawthorne;Erich Elsen;Jialin Song;Adam Roberts

  • This time with feeling: learning expressive musical performance

    Sageev Oore;Ian Simon;Sander Dieleman;Douglas Eck

  • Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases

    Thierry Bertin-Mahieux;Douglas Eck;François Maillet;Paul Lamere

  • Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

    Juan Antonio Pérez-Ortiz;Felix A. Gers;Douglas Eck;Jürgen Schmidhuber

  • Temporal pooling and multiscale learning for automatic annotation and ranking of music audio

    Philippe Hamel;Simon Lemieux;Yoshua Bengio;Douglas Eck

  • Music Transformer

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

Frequent Co-Authors

Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Colin Raffel
Colin Raffel University of Toronto
Samy Bengio
Samy Bengio Apple (United States)
Chris Callison-Burch
Chris Callison-Burch University of Pennsylvania
Yoshua Bengio
Yoshua Bengio University of Montreal
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
Richard E. Turner
Richard E. Turner University of Cambridge
Shixiang Gu
Shixiang Gu Google (United States)

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