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03/21/2022
Optimal Fine-Grained N:M sparsity for Activations and Neural Gradients
In deep learning, fine-grained N:M sparsity reduces the data footprint a...
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02/16/2021
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Recently, researchers proposed pruning deep neural network weights (DNNs...
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12/03/2019
The Knowledge Within: Methods for Data-Free Model Compression
Background: Recently, an extensive amount of research has been focused o...
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11/06/2019
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning....
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01/27/2019
Augment your batch: better training with larger batches
Large-batch SGD is important for scaling training of deep neural network...
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05/25/2018
Scalable Methods for 8-bit Training of Neural Networks
Quantized Neural Networks (QNNs) are often used to improve network effic...
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01/14/2018
Fix your classifier: the marginal value of training the last weight layer
Neural networks are commonly used as models for classification for a wid...
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05/24/2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Background: Deep learning models are typically trained using stochastic ...
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11/07/2016
Playing SNES in the Retro Learning Environment
Mastering a video game requires skill, tactics and strategy. While these...
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10/02/2016
Deep unsupervised learning through spatial contrasting
Convolutional networks have marked their place over the last few years a...
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09/22/2016
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
We introduce a method to train Quantized Neural Networks (QNNs) --- neur...
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02/08/2016
