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.
We describe a very simple bag-of-words baseline for visual question answering.
We describe one possible roadmap how to develop intelligent machines with communication skills that can perform useful tasks for us.
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.
Environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning.
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.
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.
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...
Replicated storage for large Web services faces a trade-off between stronger forms of consistency and higher performance properties. Stronger consistency prevents anomalies, i.e., unexpected behavior visible to users, and reduces programming complexity.
Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their combination yields significant and consistent improvement on two different test sets.
