World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
37
Citations
12957
World Ranking
741
National Ranking
118

Computer Science

D-Index
37
Citations
13536
World Ranking
10454
National Ranking
4369

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Felix Hill is affiliated with Google in the United States and specializes in the field of Computer Science with a focus on Artificial Intelligence. Their research integrates multiple subfields including Computer Vision and Pattern Recognition, Cognitive Neuroscience, Sociology and Political Science, and Developmental and Educational Psychology.

The scientist's recent publications reflect their focus on language models, multimodal learning, and reasoning in AI. Notable papers include:

  • Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models (2020, Proceedings of the National Academy of Sciences)
  • Multimodal Few-Shot Learning with Frozen Language Models (2021, arXiv (Cornell University))
  • Meaning without reference in large language models (2022, arXiv (Cornell University))
  • Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022, arXiv (Cornell University))
  • Language models show human-like content effects on reasoning tasks (2022, arXiv (Cornell University))

Frequent coauthors who have collaborated extensively with Felix Hill include:

  • Andrew K. Lampinen
  • Stephanie C. Y. Chan
  • Adam Santoro
  • Ishita Dasgupta
  • James L. McClelland

Felix Hill's work is also characterized by consistent publication in specific venues, primarily:

  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • PNAS Nexus
  • Machine Learning Science and Technology
  • Behavioral and Brain Sciences

The research topics covered in their work encompass various aspects of artificial intelligence and machine learning, including:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Natural Language Processing Techniques
  • Reinforcement Learning in Robotics
  • Explainable Artificial Intelligence (XAI)
  • Neural Networks and Applications

Felix Hill's academic output demonstrates a comprehensive engagement with advancing AI technologies through diverse methodological approaches and interdisciplinary perspectives within computational sciences.

Best Publications

  • GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

    Alex Wang;Amanpreet Singh;Julian Michael;Felix Hill

  • SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

    Alex Wang;Yada Pruksachatkun;Nikita Nangia;Amanpreet Singh

  • Simlex-999: Evaluating semantic models with genuine similarity estimation

    Felix Hill;Roi Reichart;Anna Korhonen

  • The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations

    Felix Hill;Antoine Bordes;Sumit Chopra;Jason Weston

  • Learning distributed representations of sentences from unlabelled data

    Felix Hill;Kyunghyun Cho;Anna Korhonen

  • SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

    Daniela Gerz;Ivan Vulic;Felix Hill;Roi Reichart

  • Grounded Language Learning in a Simulated 3D World

    Karl Moritz Hermann;Felix Hill;Simon Green;Fumin Wang

  • Measuring abstract reasoning in neural networks

    David G. T. Barrett;Felix Hill;Adam Santoro;Ari S. Morcos

  • Learning to Understand Phrases by Embedding the Dictionary

    Felix Hill;KyungHyun Cho;Anna Korhonen;Yoshua Bengio

  • Can language models learn from explanations in context?

    Unknown

  • Analysing Mathematical Reasoning Abilities of Neural Models

    David Saxton;Edward Grefenstette;Felix Hill;Pushmeet Kohli

  • Specializing Word Embeddings for Similarity or Relatedness

    Douwe Kiela;Felix Hill;Stephen Clark

  • Data Distributional Properties Drive Emergent In-Context Learning in Transformers

    Unknown

  • Neural arithmetic logic units

    Andrew Trask;Felix Hill;Scott E. Reed;Jack W. Rae

  • Language models show human-like content effects on reasoning

    Unknown

  • Adaptive Communication: Languages with More Non-Native Speakers Tend to Have Fewer Word Forms

    Christian Bentz;Annemarie Verkerk;Douwe Kiela;Felix Hill

  • Learning to Understand Goal Specifications by Modelling Reward

    Dzmitry Bahdanau;Felix Hill;Jan Leike;Edward Hughes

  • Meaning without reference in large language models

    Unknown

  • Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean

    Felix Hill;Anna Korhonen

  • Hyperlex: A large-scale evaluation of graded lexical entailment

    Ivan Vulić;Daniela Gerz;Douwe Kiela;Felix Hill

  • Improving Multi-Modal Representations Using Image Dispersion: Why Less is Sometimes More

    Douwe Kiela;Felix Hill;Anna Korhonen;Stephen Clark

  • Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models.

    James L McClelland;Felix Hill;Maja Rudolph;Jason Baldridge

  • Learning to Make Analogies by Contrasting Abstract Relational Structure

    Felix Hill;Adam Santoro;David G. T. Barrett;Ari S. Morcos

  • Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text

    Felix Hill;Sona Mokra;Nathaniel Wong;Tim Harley

Frequent Co-Authors

Anna Korhonen
Anna Korhonen University of Cambridge
Stephen Clark
Stephen Clark Cambridge Quantum Computing
Matthew Botvinick
Matthew Botvinick Yale University
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Yoshua Bengio
Yoshua Bengio University of Montreal
Roi Reichart
Roi Reichart Technion – Israel Institute of Technology
Douwe Kiela
Douwe Kiela Stanford University
Phil Blunsom
Phil Blunsom University of Oxford
James L. McClelland
James L. McClelland Stanford University
Omer Levy
Omer Levy Deep Mind

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