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Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness
Open-domain dialogue agents have vastly improved, but still confidently ...
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Few-shot Sequence Learning with Transformers
Few-shot algorithms aim at learning new tasks provided only a handful of...
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CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Humans can learn and reason under substantial uncertainty in a space of ...
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How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
We seek to create agents that both act and communicate with other agents...
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Deploying Lifelong Open-Domain Dialogue Learning
Much of NLP research has focused on crowdsourced static datasets and the...
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Fast Adaptation via Policy-Dynamics Value Functions
Standard RL algorithms assume fixed environment dynamics and require a s...
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Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domai...
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Residual Energy-Based Models for Text Generation
Text generation is ubiquitous in many NLP tasks, from summarization, to ...
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Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Learning to navigate in a realistic setting where an agent must rely sol...
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I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Dialogue research tends to distinguish between chit-chat and goal-orient...
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I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-orientated dialogue agents
Dialogue research tends to distinguish between chit-chat and goal-orient...
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Generating Interactive Worlds with Text
Procedurally generating cohesive and interesting game environments is ch...
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Why Build an Assistant in Minecraft?
In this document we describe a rationale for a research program aimed at...
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CraftAssist: A Framework for Dialogue-enabled Interactive Agents
This paper describes an implementation of a bot assistant in Minecraft, ...
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Real or Fake? Learning to Discriminate Machine from Human Generated Text
Recent advances in generative modeling of text have demonstrated remarka...
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CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant
We propose a large scale semantic parsing dataset focused on instruction...
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Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a researc...
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The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition t...
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Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
In hierarchical reinforcement learning a major challenge is determining ...
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Dialogue Natural Language Inference
Consistency is a long standing issue faced by dialogue models. In this p...
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Planning with Arithmetic and Geometric Attributes
A desirable property of an intelligent agent is its ability to understan...
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Lightweight Adaptive Mixture of Neural and N-gram Language Models
It is often the case that the best performing language model is an ensem...
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Composable Planning with Attributes
The tasks that an agent will need to solve often are not known during tr...
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Modeling Others using Oneself in Multi-Agent Reinforcement Learning
We consider the multi-agent reinforcement learning setting with imperfec...
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Personalizing Dialogue Agents: I have a dog, do you have pets too?
Chit-chat models are known to have several problems: they lack specifici...
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Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Contrary to most natural language processing research, which makes use o...
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Optimizing the Latent Space of Generative Networks
Generative Adversarial Networks (GANs) have been shown to be able to sam...
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Low-shot learning with large-scale diffusion
This paper considers the problem of inferring image labels for which onl...
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Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Training convolutional networks (CNN's) that fit on a single GPU with mi...
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Training Language Models Using Target-Propagation
While Truncated Back-Propagation through Time (BPTT) is the most popular...
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Automatic Rule Extraction from Long Short Term Memory Networks
Although deep learning models have proven effective at solving problems ...
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Transformation-Based Models of Video Sequences
In this work we propose a simple unsupervised approach for next frame pr...
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Tracking the World State with Recurrent Entity Networks
We introduce a new model, the Recurrent Entity Network (EntNet). It is e...
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Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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Recurrent Orthogonal Networks and Long-Memory Tasks
Although RNNs have been shown to be powerful tools for processing sequen...
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Simple Baseline for Visual Question Answering
We describe a very simple bag-of-words baseline for visual question answ...
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MazeBase: A Sandbox for Learning from Games
This paper introduces MazeBase: an environment for simple 2D games, desi...
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Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
A long-term goal of machine learning is to build intelligent conversatio...
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Convolutional networks and learning invariant to homogeneous multiplicative scalings
The conventional classification schemes -- notably multinomial logistic ...
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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative parametric model capable of prod...
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End-To-End Memory Networks
We introduce a neural network with a recurrent attention model over a po...
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A mathematical motivation for complex-valued convolutional networks
A complex-valued convolutional network (convnet) implements the repeated...
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Video (language) modeling: a baseline for generative models of natural videos
We propose a strong baseline model for unsupervised feature learning usi...
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Better Feature Tracking Through Subspace Constraints
Feature tracking in video is a crucial task in computer vision. Usually,...
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Spectral Networks and Locally Connected Networks on Graphs
Convolutional Neural Networks are extremely efficient architectures in i...
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Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework,...
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Signal Recovery from Pooling Representations
In this work we compute lower Lipschitz bounds of ℓ_p pooling operators ...
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Tree structured sparse coding on cubes
A brief description of tree structured sparse coding on the binary cube....
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Learning Stable Group Invariant Representations with Convolutional Networks
Transformation groups, such as translations or rotations, effectively ex...
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Fast approximations to structured sparse coding and applications to object classification
We describe a method for fast approximation of sparse coding. The input ...
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