Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Sequential models achieve state-of-the-art results in audio, visual and textual domains with
respect to both estimating the data distribution and generating desired samples. Efficient …

Abstract Model compression techniques, such as pruning and quantization, are becoming
increasingly important to reduce the memory footprints and the amount of computations …

Convolutional neural networks (CNNs) are at the core of many state-of-the-art deep learning
models in computer vision, speech, and text processing. Training and deploying such CNN …

Model compression has been introduced to reduce the required hardware resources while
maintaining the model accuracy. Lots of techniques for model compression, such as …

Low-rank approximation is an effective model compression technique to not only reduce
parameter storage requirements, but to also reduce computations. For convolutional neural …

Quantization based on the binary codes is gaining attention because each quantized bit can
be directly utilized for computations without dequantization using look-up tables. Previous …

Deep reinforcement learning (DRL) has shown remarkable success in sequential decision-
making problems but suffers from a long training time to obtain such good performance …

Weight pruning has been introduced as an efficient model compression technique. Even
though pruning removes significant amount of weights in a network, memory requirement …

Deep neural network compression is important and increasingly developed especially in
resource-constrained environments, such as autonomous drones and wearable devices …