May 22, 2017

IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks

IEEE Symposium on Security and Privacy (IEEE S&P)

In this paper, we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants.

Paul Marinescu, Chad Parry, Marjori Pomarole, Yuan Tian, Patrick Tague, Ioannis Papagiannis
May 21, 2017

CAN: Creative Adversarial Networks

IEEE International Conference on Communications (ICCC)

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution.

Ahmed Elgammal, Bingchen Liu, Marian Mazzone, Mohamed Elhoseiny
May 16, 2017

Cultural Diffusion and Trends in Facebook Photographs

The International AAAI Conference on Web and Social Media (ICWSM)

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, i.e., photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs.

Quenzeng You, Dario Garcia, Manohar Paluri, Jiebo Luo, Jungseock Joo
May 6, 2017

Paradigm shift from Human Computer Interaction to Integration

Computer Human Interaction (CHI)

In 1960, JCR Licklider forecast three phases: human- computer interaction, human-computer symbiosis, and ultra-intelligent machines. Human-computer symbiosis or what we call “integration” is already well under way. This SIG will discuss how the CHI community should think about the paradigm shift from interaction to integration as designers, practitioners, researchers, and as a society.

Jonathan T. Grudin, Umer Farooq
April 27, 2017

Passive Realtime Datacenter Fault Detection

USENIX Symposium on Networked Systems Design and Implementation (NSDI) 2017

We describe how to expedite the process of detecting and localizing partial datacenter faults using an end-host method generalizable to most datacenter applications.

Arjun Roy, James Hongyi Zeng, Jasmeet Bagga, Alex C. Snoeren
April 24, 2017

Connective recovery in social networks after the death of a friend

Nature Human Behavior

Most individuals have few close friends, leading to potential isolation after a friend’s death. Do social networks heal to fill the space left by the loss? We conduct such a study of self-healing and resilience in social networks.

William Hobbs, Moira Burke
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.

Leon Bottou, Martin Arjovsky
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 Oquab
April 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 Szlam
April 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 Mikolov
April 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 Chintala
April 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 Weston
April 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 Weston
April 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 Tian
April 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 Wolf
April 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 Tian
April 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, Anitha Kannan, Dhruv Batra, Devi Parikh
April 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 a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation.

Armand Joulin, Edouard Grave, Nicolas Usunier
April 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 Mikolov
April 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 Weston