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Found 14 papers, 9 papers with code
Open-Ended Learning Leads to Generally Capable Agents
The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.
Disentangling factors of variation in deep representations using adversarial training
During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.
Energy-based Generative Adversarial Network
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
Deep multi-scale video prediction beyond mean square error
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics.
Learning to Linearize Under Uncertainty
Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision.
Stacked What-Where Auto-encoders
The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet.
Learning Longer Memory in Recurrent Neural Networks
In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent.
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units.
Video (language) modeling: a baseline for generative models of natural videos
We propose a strong baseline model for unsupervised feature learning using video data.
The Loss Surfaces of Multilayer Networks
We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum.
Fast Approximation of Rotations and Hessians matrices
A new method to represent and approximate rotation matrices is introduced.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks.
Fast Training of Convolutional Networks through FFTs
Convolutional networks are one of the most widely employed architectures in computer vision and machine learning.
Learning Convolutional Feature Hierarchies for Visual Recognition
We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features.
