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Learning Reasoning Strategies in End-to-End Differentiable Proving
Attempts to render deep learning models interpretable, data-efficient, a...
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The NetHack Learning Environment
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...
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Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in ...
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Differentiable Reasoning on Large Knowledge Bases and Natural Language
Reasoning with knowledge expressed in natural language and Knowledge Bas...
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RTFM: Generalising to Novel Environment Dynamics via Reading
Obtaining policies that can generalise to new environments in reinforcem...
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TorchBeast: A PyTorch Platform for Distributed RL
TorchBeast is a platform for reinforcement learning (RL) research in PyT...
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Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep...
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A Survey of Reinforcement Learning Informed by Natural Language
To be successful in real-world tasks, Reinforcement Learning (RL) needs ...
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Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
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Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---pres...
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Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
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Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
While deep learning has led to remarkable results on a number of challen...
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Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
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Can Neural Networks Understand Logical Entailment?
We introduce a new dataset of logical entailments for the purpose of mea...
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The NarrativeQA Reading Comprehension Challenge
Reading comprehension (RC)---in contrast to information retrieval---requ...
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Learning Explanatory Rules from Noisy Data
Artificial Neural Networks are powerful function approximators capable o...
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Discovering Discrete Latent Topics with Neural Variational Inference
Topic models have been widely explored as probabilistic generative model...
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Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some wor...
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Learning to Compose Words into Sentences with Reinforcement Learning
We use reinforcement learning to learn tree-structured neural networks f...
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The Neural Noisy Channel
We formulate sequence to sequence transduction as a noisy channel decodi...
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Semantic Parsing with Semi-Supervised Sequential Autoencoders
We present a novel semi-supervised approach for sequence transduction an...
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Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditione...
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Reasoning about Entailment with Neural Attention
While most approaches to automatically recognizing entailment relations ...
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Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive ...
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Learning to Transduce with Unbounded Memory
Recently, strong results have been demonstrated by Deep Recurrent Neural...
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Concrete Sentence Spaces for Compositional Distributional Models of Meaning
Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a com...
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