Using an MDL-based cost function with neural networks
Generalized dynamic feedforward networks using differential equations
Generalized dynamic feedforward networks using differential equations
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Gustav's blog Random things, mostly about technical stuff Menu Skip to content Neural Networks using Pylearn2 – termination criteria, momentum and learning rate adjustment 2 Replies A while ago I wrote a post describing how to use Pylearn2 for training neural networks. By my very modest standards it became quite popular so I thought I should follow it with a more advanced example that introduces more advanced termination criteria, momentum and learning rate adjustment. It should be noted that this post is
- » - Neural Networks - Edit on GitHub # Neural Networks ¶ The module pyro.nn provides implementations of neural network modules that are useful in the context of deep probabilistic programming. ## Pyro Modules ¶ Pyro includes a class PyroModule , a subclass of torch.nn.Module , whose attributes can be modified by Pyro effects. To create a poutine-aware attribute, use either the PyroParam struct or the PyroSample struct: my_module = PyroModule() my_module.x = PyroParam(torch.tensor(1.), constraint=cons
9. Recurrent Neural Networks navigate_next 9.5. Recurrent Neural Network Implementation from Scratch search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regres
Abstract page for arXiv paper 1505.07376: Texture Synthesis Using Convolutional Neural Networks
# Guest post by Julien Mairal: A Kernel Point of View on Convolutional Neural Networks, part I I (n.b., Julien Mairal ) have been interested in drawing links between neural networks and kernel methods for some time, and I am grateful to Sebastien for giving me the opportunity to say a few words about it on his blog. My initial motivation was not to provide another “why deep learning works” theory, but simply to encode into kernel methods a few successful principles from convolutional neural networks
This paper examines how deep neural networks and humans perform object recognition under degraded image conditions, revealing key differences in robustness