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Year Published

492 Results

January 7, 2016

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016 International Conference on Learning Representations

We stabilize Generative Adversarial networks with some architectural constraints and visualize the internals of the networks.

By: Alec Radford, Luke Metz, Soumith Chintala
December 15, 2015

Learning to Segment Object Candidates

NIPS

In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

By: Pedro Oliveira, Ronan Collobert, Piotr Dollar
December 7, 2015

Simple Bag-of-Words Baseline for Visual Question Answering

ArXiv PrePrint

We describe a very simple bag-of-words baseline for visual question answering.

By: Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus
December 1, 2015

Internet Use and Psychological Well-Being: Effects of Activity and Audience

Communications of the ACM

Two lines of research fifteen years apart demonstrate that talking with close friends online is associated with improvements in social support, depression, and other measures of well-being. Talking with strangers and reading about acquaintances are not.

By: Robert Kraut, Moira Burke
November 25, 2015

A Roadmap Towards Machine Intelligence

ArXiv PrePrint

We describe one possible roadmap how to develop intelligent machines with communication skills that can perform useful tasks for us.

By: Tomas Mikolov, Armand Joulin, Marco Baroni
November 23, 2015

MazeBase: A Sandbox for Learning from Games

ArXiv PrePrint

Environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning

By: Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus
November 23, 2015

Learning Simple Algorithms from Examples

ArXiv PrePrint

We present an approach for learning simple algorithms such as addition or multiplication. Our methods works as a hard attention model on both input and output and it is learn with reinforcement learning.

By: Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus
November 20, 2015

Sequence Level Training with Recurrent Neural Networks

ICLR 2016

This work aims at improving text generation for applications such as summarization, machine translation and image captioning. The key idea is to learn to predict not just the next word but a whole sequence of words, and to train end-to-end at the sequence level.

By: Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
November 19, 2015

Alternative Structures for Character-Level RNNs

ArXiv PrePrint

We present two alternative structures to character level recurrent networks. Character level RNNs are known to be very inefficient while achieving lower performance than word level RNNs.

By: Piotr Bojanowski, Armand Joulin, Tomas Mikolov
October 4, 2015

Holistic Configuration Management at Facebook

The 25th ACM Symposium on Operating Systems Principles

This paper gives a comprehensive description of the use cases, design, implementation, and usage statistics of a suite of tools that manage Facebook’s configuration end-to-end, including the frontend products, backend systems, and mobile apps.

By: Chunqiang (CQ) Tang, Thawan Kooburat, Pradeep Venkat, Akshay Chander, Zhe Wen, Aravind Narayanan, Patrick Dowell, Robert Karl