24.4k citations
- Courant Institute of Mathematical SciencesUS7 papers
- Microsoft (United States)US7 papers
- New York UniversityUS7 papers
- Stanford UniversityUS6 papers
- Columbia UniversityUS5 papers
- University of California, BerkeleyUS5 papers
- University of WashingtonUS5 papers
- Carnegie Mellon UniversityUS4 papers
- Cornell UniversityUS4 papers
- Massachusetts Institute of TechnologyUS4 papers
- University of Illinois Urbana-ChampaignUS4 papers
- University of OxfordGB4 papers
30 papers · 1 filter
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Alex Beutel, Amr Ahmed, Alexander J. Smola
Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data. Instead of using low-rank factorization we…
The supervised hierarchical Dirichlet process
Andrew M. Dai, Amos J. Storkey
We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable dire…
Multiple Object Recognition with Visual Attention
Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu
We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend…
Learning Longer Memory in Recurrent Neural Networks
Tomas Mikolov, Armand Joulin, Sumit Chopra +2
Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using…
Deep Networks With Large Output Spaces
Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga +1
Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, th…
Attention for Fine-Grained Categorization
Pierre Sermanet, Andrea Frome, Esteban Real
This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained…