Torch | The power of Spatial Transformer Networks
September 7, 2015 by Alban Desmaison tl;dr A few weeks ago, Google DeepMind released an awesome paper called Spatial Transformer Networks aiming at boosting the geometric invariance of CNNs in a very elegant way. This approach was so appealing to us at Moodstocks that we decided to implement it and see how it performs on a not-so-simple dataset called the GTSRB. At the end of the day Spatial Trans
Visual Geometry Group - University of Oxford
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Luvit.io
Familiar API Luvit implements the same APIs as Node.js, but in Lua! This helps teams migrate without having to learn a new way of programming. Async Choice Choose your async model; we don’t mind; we encourage experimentation. If you don’t like callbacks and event emitters, use coroutines and write blocking style code without actually blocking your event loop! Modular Core The various projects in t
Understanding Natural Language with Deep Neural Networks Using Torch | NVIDIA Technical Blog
After the machine has learned word embeddings, the next problem to tackle is the ability to string words together appropriately in small, grammatically correct sentences which make sense. This is called language modeling. Language modeling is one part of quantifying how well the machine understands language. For example, given a sentence (“I am eating pasta for lunch.â€), and a word (“carsâ€), if th
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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. We introduce two new Fast Fourier Transform convolution implementations: one based on NVIDIA's cuFFT library, and another based on a Facebook authored FFT implementation, fbfft, that provides significant speedups over cuFFT (over 1.5x) for whole CNNs. Both of t
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Loading caffe imagenet models example by szagoruyko · Pull Request #25 · soumith/cuda-convnet2.torch
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