Filter by
April 24, 2017
Towards Principled Methods for Training Generative Adversarial Networks
International Conference on Learning Representations (ICLR) 2017
The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. I
Martin Arjovsky, Leon Bottou
April 24, 2017
Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery
International Conference on Learning Representations (ICLR) 2017
In this paper, we aim to revive interest in the use of binary classifiers for two-sample testing. To this end, we review their fundamentals, previous literature on their use, compare their performance against alternative state-of-the-art two-sample tests, and propose them to evaluate generative adversarial network models applied to image synthesis.
David Lopez-Paz, Maxime OquabApril 24, 2017
Automatic Rule Extraction from Long Short Term Memory Networks
International Conference on Learning Representations (ICLR) 2017
In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output.
W. James Murdoch, Arthur SzlamApril 24, 2017
Variable Computation in Recurrent Neural Networks
International Conference on Learning Representations (ICLR) 2017
In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence’s time structure.
Yacine Jernite, Edouard Grave, Armand Joulin, Tomas MikolovApril 24, 2017
Episodic Exploration for Deep Deterministic Policies for StarCraft Micro-Management
International Conference on Learning Representations (ICLR) 2017
We consider scenarios from the real-time strategy game StarCraft as benchmarks for reinforcement learning algorithms.
Gabriel Synnaeve, Zeming Lin, Soumith ChintalaApril 24, 2017
Learning through Dialogue Interactions by Asking Questions
International Conference on Learning Representations (ICLR) 2017
In this work, we explore a dialogue agents ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction, by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher.
Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason WestonApril 24, 2017
Dialogue Learning with Human-in-the-Loop
International Conference on Learning Representations (ICLR) 2017
In this paper we explore interacting with a dialogue partner in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses.
Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason WestonApril 24, 2017
Training Agent for First-Person Shooter Game With Actor-Critic Curriculum Learning
International Conference on Learning Representations (ICLR) 2017
In this paper, we propose a new framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom.
Yuxin Wu, Yuandong TianApril 24, 2017
Unsupervised Cross-Domain Image Generation
International Conference on Learning Representations (ICLR) 2017
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given representation function f, which accepts inputs in either domains, would remain unchanged.
Yaniv Taigman, Adam Polyak, Lior WolfApril 24, 2017
An Analytical Formula of Population Gradient for Two-Layered ReLU network and its Applications in Convergence and Critical Point Analysis
International Conference on Learning Representations (ICLR) 2017
In this paper, we explore theoretical properties of training a two-layered ReLU network g(x; w) = PK j=1 σ(w | j x) with centered d-dimensional spherical Gaussian input x (σ=ReLU). We train our network with gradient descent on w to mimic the output of a teacher network with the same architecture and fixed parameters w∗.
Yuandong TianApril 24, 2017
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
International Conference on Learning Representations (ICLR)
We present LR-GAN: an adversarial image generation model which takes scene structure and context into account.
Jianwei Yang, Dhruv Batraand, Devi ParikhApril 24, 2017
Improving Neural Language Models with a Continuous Cache
International Conference on Learning Representations (ICLR) 2017
We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is […]
Edouard Grave, Armand Joulin, Nicolas UsunierApril 24, 2017
CommAI: Evaluating the First Steps Towards a Useful General AI
ICLR 2017 Workshop
We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.
Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas MikolovApril 24, 2017
Learning End-to-End Goal-Oriented Dialog
International Conference on Learning Representations (ICLR)
This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications.
Antoine Bordes, Y-Lan Boureau, Jason WestonApril 24, 2017
Tracking the World State with Recurrent Entity Networks
International Conference on Learning Representations (ICLR) 2017
We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data.
Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCunApril 24, 2017
Multi-Agent Cooperation and the Emergence of (Natural) Language
International Conference on Learning Representations (ICLR) 2017
This paper proposes a framework for language learning that relies on multi-agent communication.
Angeliki Lazaridou, Alexander Peysakhovich, Marco BaroniApril 19, 2017
Joint User-Entity Representation Learning for Event Recommendation in Social Network
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
In this work, we consider the heavy sparseness in both user and event feedback history caused by short lifespans (transiency) of events and user participation patterns in a production event system. We propose to solve the resulting cold-start problems by introducing a joint representation model to project users and events into the same latent space.
Lijun Tang, Eric Yi Liu
April 3, 2017
Bag of Tricks for Efficient Text Classification
European Chapter of the Association for Computational Linguistics (EACL)
This paper explores a simple and efficient baseline for text classification.
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
April 3, 2017
Understanding Short-term Changes in Online Activity Sessions
World Wide Web (WWW)
Using data from the online social networking site Facebook, we uncover temporal patterns at a much smaller time scale: within individual sessions.
Farshad Kooti, Karthik Subbian, Winter Mason, Lada Adamic, Kristina Lerman
April 1, 2017
Spinner: Scalable Graph Partitioning in the Cloud
IEEE International Conference on Data Engineering (ICDE)
In this paper, we present a graph partitioning algorithm to partition graphs with trillions of edges.
Claudio Martella, Dionysios Logothetis, Andreas Loukas, Georgos Siganos