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
14 papers · 2 filters
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob +2
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue t…
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero, Pierre Luc Carrier, Akram Erraqabi +7
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training exam…
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau +3
This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally le…
Hierarchical Multiscale Recurrent Neural Networks
Junyoung Chung, Sungjin Ahn, Yoshua Bengio
Learning both hierarchical and temporal representation has been among the long-standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been cons…
Mollifying Networks
Caglar Gulcehre, Marcin Moczulski, Francesco Visin +1
The optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non-convex nature of the loss function, e.g. it can…
An Actor-Critic Algorithm for Sequence Prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu +5
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are li…
On Multiplicative Integration with Recurrent Neural Networks
Yuhuai Wu, Saizheng Zhang, Ying Zhang +2
We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from…
The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family
Alexandre de Brébisson, Pascal Vincent
Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly wi…
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference…
Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil +1
Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies…
Binarized Neural Networks
Itay Hubara, Daniel Soudry, Ran El Yaniv
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at tra…
Recurrent Batch Normalization
Tim Cooijmans, Nicolas Ballas, César Laurent +2
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the i…
Cascading Bandits for Large-Scale Recommendation Problems
Shi Zong, Hao Ni, Kenny Sung +3
Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine th…
Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the pre…