World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
32341
World Ranking
4175
National Ranking
164

Overview

Colin Raffel is affiliated with the University of Toronto in Canada and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and machine learning. Their research output includes over 150 publications, reflecting a broad engagement with key areas of computational study.

The primary fields of study represented in their work include:

  • Computer Science

Within this overarching field, Raffel's research spans several subfields, notably:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Health Informatics
  • Statistical and Nonlinear Physics

The main topics covered in Raffel's publications encompass:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Machine Learning and Algorithms

Raffel's frequent coauthors include:

  • Mohit Bansal
  • Derek Tam
  • Teven Le Scao
  • Sharan Narang
  • Adam P. Roberts

Several key venues have published Raffel's work multiple times, illustrating a sustained engagement with prominent outlets. These venues are:

  • arXiv (Cornell University)
  • UNC Libraries
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Communications of the ACM

Notable recent papers authored or coauthored by Raffel include:

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence, 2020, arXiv (Cornell University)
  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Emergent Abilities of Large Language Models, 2022, arXiv (Cornell University)
  • Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options, 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • mT5: A massively multilingual pre-trained text-to-text transformer, 2020, arXiv (Cornell University)

Best Publications

  • Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

    Colin Raffel;Noam Shazeer;Adam Roberts;Katherine Lee

  • librosa: Audio and Music Signal Analysis in Python

    Brian McFee;Colin Raffel;Dawen Liang;Daniel P.W. Ellis

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

    Kihyuk Sohn;David Berthelot;Chun-Liang Li;Zizhao Zhang

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • MixMatch: A Holistic Approach to Semi-Supervised Learning

    David Berthelot;Nicholas Carlini;Ian Goodfellow;Nicolas Papernot

  • Emergent Abilities of Large Language Models

    Unknown

  • mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

    Linting Xue;Noah Constant;Adam Roberts;Mihir Kale

  • BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    Unknown

  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

    Avital Oliver;Augustus Odena;Colin A. Raffel;Ekin Dogus Cubuk

  • How Much Knowledge Can You Pack Into the Parameters of a Language Model

    Adam Roberts;Colin Raffel;Noam Shazeer

  • Multitask Prompted Training Enables Zero-Shot Task Generalization

    Victor Sanh;Albert Webson;Colin Raffel;Stephen H. Bach

  • Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

    Unknown

  • Crosslingual Generalization through Multitask Finetuning

    Unknown

  • Thermometer Encoding: One Hot Way To Resist Adversarial Examples

    Jacob Buckman;Aurko Roy;Colin Raffel;Ian Goodfellow

  • MIR_EVAL: A Transparent Implementation of Common MIR Metrics.

    Colin Raffel;Brian McFee;Eric J. Humphrey;Justin Salamon

  • ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Extracting Training Data from Large Language Models

    Nicholas Carlini;Florian Tramèr;Eric Wallace;Matthew Jagielski

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

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

  • PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

    Unknown

  • Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems

    Colin Raffel;Daniel P. W. Ellis

  • Lasagne: First release.

    Sander Dieleman;Michael Heilman;Jack Kelly;Martin Thoma

  • Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition.

    Yao Qin;Nicholas Carlini;Garrison W. Cottrell;Ian J. Goodfellow

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching

    Colin Raffel

  • Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

    Yao Qin;Nicholas Carlini;Ian Goodfellow;Garrison Cottrell

Frequent Co-Authors

Daniel P. W. Ellis
Daniel P. W. Ellis Google (United States)
Ian Goodfellow
Ian Goodfellow Google (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)
Douglas Eck
Douglas Eck Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Ekin D. Cubuk
Ekin D. Cubuk Google (United States)
Noam Shazeer
Noam Shazeer Google (United States)
Garrison W. Cottrell
Garrison W. Cottrell University of California, San Diego
Kihyuk Sohn
Kihyuk Sohn Google (United States)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Computer Science is a field with diverse career options, and many students consider online degrees to enhance their skills or specialize further. One fast-growing area is cybersecurity. Pursuing an accredited online cyber security degree can open doors to roles like information security analyst and threat intelligence specialist.

For those interested in overseeing projects and teams, a construction management masters degree online offers the flexibility to advance in industries such as tech infrastructure and smart building design.

Students who want to combine technology with law enforcement may find opportunities with the cheapest online criminal justice degrees. These programs emphasize topics like digital forensics and cybercrime, blending CS expertise with criminal justice practices.

Additionally, an online accounting degree can enable graduates to pursue data analytics, auditing, or finance tech roles. Many pathways exist for Computer Science students to specialize further, leveraging affordable and flexible online degree options to build a rewarding career.

Best Scientists Citing Colin Raffel

Trending Scientists

Recently Published Articles