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Found 6 papers, 4 papers with code
MLIR: A Compiler Infrastructure for the End of Moore's Law
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure.
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc.
Diagonal Rescaling For Neural Networks
We define a second-order neural network stochastic gradient training algorithm whose block-diagonal structure effectively amounts to normalizing the unit activations.
Training Language Models Using Target-Propagation
While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps.
Learning Visual Features from Large Weakly Supervised Data
We train convolutional networks on a dataset of 100 million Flickr photos and captions, and show that these networks produce features that perform well in a range of vision problems.
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.
