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04/05/2022
Bimodal Distributed Binarized Neural Networks
Binary Neural Networks (BNNs) are an extremely promising method to reduc...
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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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12/19/2021
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning
Quantization of the weights and activations is one of the main methods t...
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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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06/15/2020
Neural gradients are lognormally distributed: understanding sparse and quantized training
Neural gradient compression remains a main bottleneck in improving train...
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03/04/2020
Colored Noise Injection for Training Adversarially Robust Neural Networks
Even though deep learning have shown unmatched performance on various ta...
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02/18/2020
Robust Quantization: One Model to Rule Them All
Neural network quantization methods often involve simulating the quantiz...
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11/17/2019
Smoothed Inference for Adversarially-Trained Models
Deep neural networks are known to be vulnerable to inputs with malicious...
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11/17/2019
Loss Aware Post-training Quantization
Neural network quantization enables the deployment of large models on re...
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09/25/2019
CAT: Compression-Aware Training for bandwidth reduction
Convolutional neural networks (CNNs) have become the dominant neural net...
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05/26/2019
Feature Map Transform Coding for Energy-Efficient CNN Inference
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy i...
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04/22/2019