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03/13/2023
Vision-Language Models as Success Detectors
Detecting successful behaviour is crucial for training intelligent agent...
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02/09/2023
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...
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02/01/2023
Collaborating with language models for embodied reasoning
Reasoning in a complex and ambiguous environment is a key goal for Reinf...
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01/12/2023
SemPPL: Predicting pseudo-labels for better contrastive representations
Learning from large amounts of unsupervised data and a small amount of s...
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10/11/2022
Transformers generalize differently from information stored in context vs in weights
Transformer models can use two fundamentally different kinds of informat...
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08/05/2022
Meaning without reference in large language models
The widespread success of large language models (LLMs) has been met with...
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07/14/2022
Language models show human-like content effects on reasoning
Abstract reasoning is a key ability for an intelligent system. Large lan...
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06/16/2022
Know your audience: specializing grounded language models with the game of Dixit
Effective communication requires adapting to the idiosyncratic common gr...
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04/08/2022
Semantic Exploration from Language Abstractions and Pretrained Representations
Continuous first-person 3D environments pose unique exploration challeng...
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04/05/2022
Can language models learn from explanations in context?
Large language models can perform new tasks by adapting to a few in-cont...
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03/15/2022
Zipfian environments for Reinforcement Learning
As humans and animals learn in the natural world, they encounter distrib...
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12/15/2021
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Deep reinforcement learning (Deep RL) has recently seen significant prog...
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12/07/2021
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit...
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12/07/2021
Tell me why! – Explanations support learning of relational and causal structure
Explanations play a considerable role in human learning, especially in a...
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10/18/2021
BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation
Natural language processing (NLP) systems are increasingly trained to ge...
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06/25/2021
Multimodal Few-Shot Learning with Frozen Language Models
When trained at sufficient scale, auto-regressive language models exhibi...
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05/28/2021
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Reinforcement learning agents often forget details of the past, especial...
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12/15/2020
Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures
Neural networks have achieved success in a wide array of perceptual task...
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12/10/2020
Imitating Interactive Intelligence
A common vision from science fiction is that robots will one day inhabit...
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09/03/2020
Grounded Language Learning Fast and Slow
Recent work has shown that large text-based neural language models, trai...
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06/01/2020
Probing Emergent Semantics in Predictive Agents via Question Answering
Recent work has shown how predictive modeling can endow agents with rich...
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05/19/2020
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text
Recent work has described neural-network-based agents that are trained w...
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12/12/2019
Extending Machine Language Models toward Human-Level Language Understanding
Language is central to human intelligence. We review recent breakthrough...
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10/01/2019
Emergent Systematic Generalization in a Situated Agent
The question of whether deep neural networks are good at generalising be...
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09/01/2019
Higher-order Comparisons of Sentence Encoder Representations
Representational Similarity Analysis (RSA) is a technique developed by n...
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05/02/2019
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
In the last year, new models and methods for pretraining and transfer le...
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04/18/2019
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette
Brette contends that the neural coding metaphor is an invalid basis for ...
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04/02/2019
Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---pres...
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01/31/2019
Learning to Make Analogies by Contrasting Abstract Relational Structure
Analogical reasoning has been a principal focus of various waves of AI r...
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12/03/2018
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
Advances in Deep Reinforcement Learning have led to agents that perform ...
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08/01/2018
Neural Arithmetic Logic Units
Neural networks can learn to represent and manipulate numerical informat...
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07/11/2018
Measuring abstract reasoning in neural networks
Whether neural networks can learn abstract reasoning or whether they mer...
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06/05/2018
Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
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04/20/2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
For natural language understanding (NLU) technology to be maximally usef...
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10/26/2017
Understanding Grounded Language Learning Agents
Neural network-based systems can now learn to locate the referents of wo...
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06/20/2017
Grounded Language Learning in a Simulated 3D World
We are increasingly surrounded by artificially intelligent technology th...
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08/06/2016
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
We introduce HyperLex - a dataset and evaluation resource that quantifie...
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08/02/2016
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Verbs play a critical role in the meaning of sentences, but these ubiqui...
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02/10/2016
Learning Distributed Representations of Sentences from Unlabelled Data
Unsupervised methods for learning distributed representations of words a...
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11/07/2015
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
We introduce a new test of how well language models capture meaning in c...
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04/02/2015
Learning to Understand Phrases by Embedding the Dictionary
Distributional models that learn rich semantic word representations are ...
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12/19/2014
Embedding Word Similarity with Neural Machine Translation
Neural language models learn word representations, or embeddings, that c...
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10/02/2014
