no code implementations • 13 Mar 2023 • Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi
Detecting successful behaviour is crucial for training intelligent agents.
no code implementations • 9 Feb 2023 • Pierre H. Richemond, Allison Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
With simple linear algebra, we show that when using a linear predictor, the optimal predictor is close to an orthogonal projection, and propose a general framework based on orthonormalization that enables to interpret and give intuition on why BYOL works.
no code implementations • 1 Feb 2023 • Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, Rob Fergus
On the other hand, Large Scale Language Models (LSLMs) have exhibited strong reasoning ability and the ability to to adapt to new tasks through in-context learning.
no code implementations • 12 Jan 2023 • Matko Bošnjak, Pierre H. Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations.
no code implementations • 11 Oct 2022 • Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew K. Lampinen, Felix Hill
In transformers trained on controlled stimuli, we find that generalization from weights is more rule-based whereas generalization from context is largely exemplar-based.
no code implementations • 5 Aug 2022 • Steven T. Piantadosi, Felix Hill
The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings.
no code implementations • 14 Jul 2022 • Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, Felix Hill
We find that state of the art large language models (with 7 or 70 billion parameters; Hoffman et al., 2022) reflect many of the same patterns observed in humans across these tasks -- like humans, models reason more effectively about believable situations than unrealistic or abstract ones.
no code implementations • 16 Jun 2022 • Aaditya K. Singh, David Ding, Andrew Saxe, Felix Hill, Andrew K. Lampinen
In a series of controlled experiments, we show that the speaker can adapt according to the idiosyncratic strengths and weaknesses of various pairs of different listeners.
1 code implementation • 22 Apr 2022 • Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, Felix Hill
In further experiments, we found that naturalistic data distributions were only able to elicit in-context learning in transformers, and not in recurrent models.
no code implementations • 8 Apr 2022 • Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane X. Wang, Andrea Banino, Felix Hill
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
no code implementations • 5 Apr 2022 • Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
1 code implementation • 15 Mar 2022 • Stephanie C. Y. Chan, Andrew K. Lampinen, Pierre H. Richemond, Felix Hill
As humans and animals learn in the natural world, they encounter distributions of entities, situations and events that are far from uniform.
no code implementations • 15 Dec 2021 • Wilka Carvalho, Andrew Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan
To generalize in object-centric tasks, a reinforcement learning (RL) agent needs to exploit the structure that objects induce.
no code implementations • 7 Dec 2021 • DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Mansi Gupta, Tim Harley, Felix Hill, Peter C Humphreys, Alden Hung, Jessica Landon, Timothy Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
no code implementations • 7 Dec 2021 • Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents.
2 code implementations • 18 Oct 2021 • Thomas Scialom, Felix Hill
There is currently no simple, unified way to compare, analyse or evaluate metrics across a representative set of tasks.
no code implementations • 29 Sep 2021 • Andrew Kyle Lampinen, Nicholas Andrew Roy, Ishita Dasgupta, Stephanie C.Y. Chan, Allison Tam, Chen Yan, Adam Santoro, Neil Charles Rabinowitz, Jane X Wang, Felix Hill
Explanations play a considerable role in human learning, especially in areas that remain major challenges for AI—forming abstractions, and learning about the relational and causal structure of the world.
no code implementations • 29 Sep 2021 • Wilka Torrico Carvalho, Andrew Kyle Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan
Taking inspiration from cognitive science, we term representations for reoccurring segments of an agent's experience, "perceptual schemas".
no code implementations • NeurIPS 2021 • Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill
When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples.
Ranked #14 on Visual Question Answering (VQA) on VQA v2 val
3 code implementations • NeurIPS 2021 • Andrew Kyle Lampinen, Stephanie C. Y. Chan, Andrea Banino, Felix Hill
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
no code implementations • 1 Jan 2021 • David Ding, Felix Hill, Adam Santoro, Matthew Botvinick
Transformer-based language models have proved capable of rudimentary symbolic reasoning, underlining the effectiveness of applying self-attention computations to sets of discrete entities.
1 code implementation • NeurIPS 2021 • David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning.
Ranked #4 on Video Object Tracking on CATER
no code implementations • 10 Dec 2020 • Josh Abramson, Arun Ahuja, Iain Barr, Arthur Brussee, Federico Carnevale, Mary Cassin, Rachita Chhaparia, Stephen Clark, Bogdan Damoc, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy Lillicrap, Kory Mathewson, Soňa Mokrá, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Duncan Williams, Nathaniel Wong, Chen Yan, Rui Zhu
These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone.
1 code implementation • ICLR 2021 • Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, Stephen Clark
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.
no code implementations • ICML 2020 • Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Josh Abramson, Alden Hung, Arun Ahuja, Stephen Clark, Gregory Wayne, Felix Hill
Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.
no code implementations • 19 May 2020 • Felix Hill, Sona Mokra, Nathaniel Wong, Tim Harley
Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.
no code implementations • 12 Dec 2019 • James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze
We take language to be a part of a system for understanding and communicating about situations.
no code implementations • ICLR 2020 • Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI.
no code implementations • 25 Sep 2019 • Felix Hill, Sona Mokra, Nathaniel Wong, Tim Harley
We address this issue by integrating language encoders that are pretrained on large text corpora into a situated, instruction-following agent.
no code implementations • IJCNLP 2019 • Mostafa Abdou, Artur Kulmizev, Felix Hill, Daniel M. Low, Anders Søgaard
Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e. g., fMRI, electrophysiology, behavior).
4 code implementations • NeurIPS 2019 • Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks.
no code implementations • 18 Apr 2019 • Adam Santoro, Felix Hill, David Barrett, David Raposo, Matthew Botvinick, Timothy Lillicrap
Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does.
6 code implementations • ICLR 2019 • David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.
Ranked #4 on Question Answering on Mathematics Dataset
2 code implementations • ICLR 2019 • Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy Lillicrap
Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data.
no code implementations • 3 Dec 2018 • Aishwarya Agrawal, Mateusz Malinowski, Felix Hill, Ali Eslami, Oriol Vinyals, tejas kulkarni
In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction.
22 code implementations • NeurIPS 2018 • Andrew Trask, Felix Hill, Scott Reed, Jack Rae, Chris Dyer, Phil Blunsom
Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training.
2 code implementations • ICML 2018 • David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy Lillicrap
To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways.
1 code implementation • ICLR 2019 • Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
10 code implementations • WS 2018 • Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.
Ranked #36 on Natural Language Inference on MultiNLI
Natural Language Inference Natural Language Understanding +1
no code implementations • ICLR 2018 • Felix Hill, Karl Moritz Hermann, Phil Blunsom, Stephen Clark
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds.
no code implementations • ICLR 2018 • Felix Hill, Stephen Clark, Karl Moritz Hermann, Phil Blunsom
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds.
1 code implementation • 20 Jun 2017 • Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
no code implementations • CL 2017 • Ivan Vulić, Daniela Gerz, Douwe Kiela, Felix Hill, Anna Korhonen
We introduce HyperLex - a dataset and evaluation resource that quantifies the extent of of the semantic category membership, that is, type-of relation also known as hyponymy-hypernymy or lexical entailment (LE) relation between 2, 616 concept pairs.
1 code implementation • EMNLP 2016 • Daniela Gerz, Ivan Vulić, Felix Hill, Roi Reichart, Anna Korhonen
Verbs play a critical role in the meaning of sentences, but these ubiquitous words have received little attention in recent distributional semantics research.
1 code implementation • NAACL 2016 • Felix Hill, Kyunghyun Cho, Anna Korhonen
Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data.
Ranked #16 on Subjectivity Analysis on SUBJ
3 code implementations • 7 Nov 2015 • Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
We introduce a new test of how well language models capture meaning in children's books.
2 code implementations • TACL 2016 • Felix Hill, Kyunghyun Cho, Anna Korhonen, Yoshua Bengio
Distributional models that learn rich semantic word representations are a success story of recent NLP research.
no code implementations • 19 Dec 2014 • Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model.
no code implementations • 2 Oct 2014 • Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Neural language models learn word representations that capture rich linguistic and conceptual information.
3 code implementations • CL 2015 • Felix Hill, Roi Reichart, Anna Korhonen
We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways.
no code implementations • TACL 2014 • Felix Hill, Roi Reichart, Anna Korhonen
Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition.