Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR2k papers
- University of British ColumbiaCA1.9k papers
- McGill UniversityCA1.9k papers
- University of VictoriaCA1.8k papers
- University of California, Santa CruzUS1.7k papers
- Rutherford Appleton LaboratoryGB1.7k papers
- University of ManchesterGB1.7k papers
- Queen Mary University of LondonGB1.7k papers
- University of PisaIT1.6k papers
- University of EdinburghGB1.6k papers
- University of BirminghamGB1.6k papers
- Heidelberg UniversityDE1.6k papers
9 papers · 2 filters
Training deep neural networks with low precision multiplications
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxo…
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio +1
Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such fi…
A Hybrid Recurrent Neural Network For Music Transcription
Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski +3
We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recur…
NICE: Non-linear Independent Components Estimation
Laurent Dinh, David Krueger, Yoshua Bengio
We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good…
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann Dauphin, Razvan Pascanu, Caglar Gulcehre +3
A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-New…
Reweighted Wake-Sleep
Jörg Bornschein, Yoshua Bengio
Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models,…
On the saddle point problem for non-convex optimization
Razvan Pascanu, Yann N. Dauphin, Surya Ganguli +1
A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-New…
Multimodal Transitions for Generative Stochastic Networks
Sherjil Ozair, Li Yao, Yoshua Bengio
Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly…
Deep Belief Networks for Image Denoising
Mohammad Ali Keyvanrad, Mohammad Pezeshki, Mohammad Ali Homayounpour
Deep Belief Networks which are hierarchical generative models are effective tools for feature representation and extraction. Furthermore, DBNs can be used in numerous aspects of Ma…