Arthur Szlam

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CUNY Law School ∙ Facebook ∙ NYU college

Research Scientist at Facebook

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Humans can learn and reason under substantial uncertainty in a space of ...
10/06/2020 ∙ by Ramakrishna Vedantam, et al. ∙ 4 ∙ share

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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...
10/01/2020 ∙ by Prithviraj Ammanabrolu, et al. ∙ 21 ∙ share

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Deploying Lifelong Open-Domain Dialogue Learning

Much of NLP research has focused on crowdsourced static datasets and the...
08/18/2020 ∙ by Kurt Shuster, et al. ∙ 0 ∙ share

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Fast Adaptation via Policy-Dynamics Value Functions

Standard RL algorithms assume fixed environment dynamics and require a s...
07/06/2020 ∙ by Roberta Raileanu, et al. ∙ 12 ∙ share

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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...
06/22/2020 ∙ by Stephen Roller, et al. ∙ 19 ∙ share

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Residual Energy-Based Models for Text Generation

Text generation is ubiquitous in many NLP tasks, from summarization, to ...
04/22/2020 ∙ by Yuntian Deng, et al. ∙ 0 ∙ share

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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...
04/10/2020 ∙ by Lina Mezghani, et al. ∙ 3 ∙ share

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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...
02/07/2020 ∙ by Shrimai Prabhumoye, et al. ∙ 8 ∙ share

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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...
02/07/2020 ∙ by Shrimai Prabhumoye, et al. ∙ 6 ∙ share

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Generating Interactive Worlds with Text

Procedurally generating cohesive and interesting game environments is ch...
11/20/2019 ∙ by Angela Fan, et al. ∙ 29 ∙ share

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Why Build an Assistant in Minecraft?

In this document we describe a rationale for a research program aimed at...
07/22/2019 ∙ by Arthur Szlam, et al. ∙ 2 ∙ share

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CraftAssist: A Framework for Dialogue-enabled Interactive Agents

This paper describes an implementation of a bot assistant in Minecraft, ...
07/19/2019 ∙ by Jonathan Gray, et al. ∙ 0 ∙ share

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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...
06/07/2019 ∙ by Anton Bakhtin, et al. ∙ 0 ∙ share

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CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant

We propose a large scale semantic parsing dataset focused on instruction...
04/17/2019 ∙ by Yacine Jernite, et al. ∙ 0 ∙ share

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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...
03/07/2019 ∙ by Jack Urbanek, et al. ∙ 0 ∙ share

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The Second Conversational Intelligence Challenge (ConvAI2)

We describe the setting and results of the ConvAI2 NeurIPS competition t...
01/31/2019 ∙ by Emily Dinan, et al. ∙ 2 ∙ share

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Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining ...
11/22/2018 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Dialogue Natural Language Inference

Consistency is a long standing issue faced by dialogue models. In this p...
11/01/2018 ∙ by Sean Welleck, et al. ∙ 6 ∙ share

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Planning with Arithmetic and Geometric Attributes

A desirable property of an intelligent agent is its ability to understan...
09/06/2018 ∙ by David Folqué, et al. ∙ 10 ∙ share

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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...
04/20/2018 ∙ by Anton Bakhtin, et al. ∙ 0 ∙ share

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Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...
03/01/2018 ∙ by Amy Zhang, et al. ∙ 0 ∙ share

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Modeling Others using Oneself in Multi-Agent Reinforcement Learning

We consider the multi-agent reinforcement learning setting with imperfec...
02/26/2018 ∙ by Roberta Raileanu, et al. ∙ 0 ∙ share

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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...
01/22/2018 ∙ by Saizheng Zhang, et al. ∙ 0 ∙ share

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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...
11/21/2017 ∙ by Zhilin Yang, et al. ∙ 0 ∙ share

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Optimizing the Latent Space of Generative Networks

Generative Adversarial Networks (GANs) have been shown to be able to sam...
07/18/2017 ∙ by Piotr Bojanowski, et al. ∙ 0 ∙ share

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Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels for which onl...
06/07/2017 ∙ by Matthijs Douze, et al. ∙ 0 ∙ share

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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...
04/20/2017 ∙ by Sam Gross, et al. ∙ 0 ∙ share

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Training Language Models Using Target-Propagation

While Truncated Back-Propagation through Time (BPTT) is the most popular...
02/15/2017 ∙ by Sam Wiseman, et al. ∙ 0 ∙ share

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Automatic Rule Extraction from Long Short Term Memory Networks

Although deep learning models have proven effective at solving problems ...
02/08/2017 ∙ by W. James Murdoch, et al. ∙ 0 ∙ share

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Transformation-Based Models of Video Sequences

In this work we propose a simple unsupervised approach for next frame pr...
01/29/2017 ∙ by Joost van Amersfoort, et al. ∙ 0 ∙ share

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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...
12/12/2016 ∙ by Mikael Henaff, et al. ∙ 0 ∙ share

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Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a...
11/24/2016 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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Recurrent Orthogonal Networks and Long-Memory Tasks

Although RNNs have been shown to be powerful tools for processing sequen...
02/22/2016 ∙ by Mikael Henaff, et al. ∙ 0 ∙ share

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Simple Baseline for Visual Question Answering

We describe a very simple bag-of-words baseline for visual question answ...
12/07/2015 ∙ by Bolei Zhou, et al. ∙ 0 ∙ share

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MazeBase: A Sandbox for Learning from Games

This paper introduces MazeBase: an environment for simple 2D games, desi...
11/23/2015 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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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...
11/21/2015 ∙ by Jesse Dodge, et al. ∙ 0 ∙ share

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Convolutional networks and learning invariant to homogeneous multiplicative scalings

The conventional classification schemes -- notably multinomial logistic ...
06/26/2015 ∙ by Mark Tygert, et al. ∙ 0 ∙ share

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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...
06/18/2015 ∙ by Emily Denton, et al. ∙ 0 ∙ share

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End-To-End Memory Networks

We introduce a neural network with a recurrent attention model over a po...
03/31/2015 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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A mathematical motivation for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated...
03/11/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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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...
12/20/2014 ∙ by MarcAurelio Ranzato, et al. ∙ 0 ∙ share

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Better Feature Tracking Through Subspace Constraints

Feature tracking in video is a crucial task in computer vision. Usually,...
05/09/2014 ∙ by Bryan Poling, et al. ∙ 0 ∙ share

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Spectral Networks and Locally Connected Networks on Graphs

Convolutional Neural Networks are extremely efficient architectures in i...
12/21/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Unsupervised Feature Learning by Deep Sparse Coding

In this paper, we propose a new unsupervised feature learning framework,...
12/20/2013 ∙ by Yunlong He, et al. ∙ 0 ∙ share

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Signal Recovery from Pooling Representations

In this work we compute lower Lipschitz bounds of ℓ_p pooling operators ...
11/16/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Tree structured sparse coding on cubes

A brief description of tree structured sparse coding on the binary cube....
01/16/2013 ∙ by Arthur Szlam, et al. ∙ 0 ∙ share

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Learning Stable Group Invariant Representations with Convolutional Networks

Transformation groups, such as translations or rotations, effectively ex...
01/16/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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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 ...
02/28/2012 ∙ by Arthur Szlam, et al. ∙ 0 ∙ share

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Hybrid Linear Modeling via Local Best-fit Flats

We present a simple and fast geometric method for modeling data by a uni...
10/17/2010 ∙ by Teng Zhang, et al. ∙ 0 ∙ share

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Convolutional Matching Pursuit and Dictionary Training

Matching pursuit and K-SVD is demonstrated in the translation invariant ...
10/03/2010 ∙ by Arthur Szlam, et al. ∙ 0 ∙ share

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Arthur Szlam

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