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An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis

16 Jul 2019

Based on SVWH and an inter-frame prediction method in conventional video coding schemes, we propose a new \textit{Inter-Layer Weight Prediction} (ILWP) and quantization method which quantize the predicted residuals of the weights.


Learning Multimodal Fixed-Point Weights using Gradient Descent

16 Jul 2019

Due to their high computational complexity, deep neural networks are still limited to powerful processing units.


The Bach Doodle: Approachable music composition with machine learning at scale

14 Jul 2019

To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a machine learning model Coconet (Huang et al., 2017) in the style of Bach.


And the Bit Goes Down: Revisiting the Quantization of Neural Networks

12 Jul 2019

In this paper, we address the problem of reducing the memory footprint of ResNet-like convolutional network architectures.


A Targeted Acceleration and Compression Framework for Low bit Neural Networks

9 Jul 2019

In this paper, we propose a novel Targeted Acceleration and Compression (TAC) framework to improve the performance of 1 bit deep neural networks W e consider that the acceleration and compression effects of binarizing fully connected layer s are not sufficient to compensate for the accuracy loss caused by it In the proposed framework, t he convolutional and fully connected layer are separated and optimized i ndividually .


Multi-Scale Vector Quantization with Reconstruction Trees

8 Jul 2019

Our main technical contribution is an analysis of the expected distortion achieved by the proposed algorithm, when the data are assumed to be sampled from a fixed unknown distribution.


Non-structured DNN Weight Pruning Considered Harmful

3 Jul 2019

Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency.


Deep Convolutional Compression for Massive MIMO CSI Feedback

2 Jul 2019

In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.


Weight Normalization based Quantization for Deep Neural Network Compression

1 Jul 2019

WNQ adopts weight normalization to avoid the long-tail distribution of network weights and subsequently reduces the quantization error.


Compression of Acoustic Event Detection Models With Quantized Distillation

1 Jul 2019

Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems.