Neural network pruning---the task of reducing the size of a network by removing parameters--
-has been the subject of a great deal of work in recent years. We provide a meta-analysis of …

Pre-trained language models such as BERT have proven to be highly effective for natural
language processing (NLP) tasks. However, the high demand for computing resources in …

In this paper we describe a new mobile architecture, mbox {MobileNetV2}, that improves the
state of the art performance of mobile models on multiple tasks and benchmarks as well as …

In this paper we describe a new mobile architecture MobileNetV2 that improves the state of
the art performance of mobile models on multiple benchmarks across a spectrum of different …

Model pruning seeks to induce sparsity in a deep neural network's various connection
matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent …

The deployment of deep convolutional neural networks (CNNs) in many real world
applications is largely hindered by their high computational cost. In this paper, we propose a …

We explore a recently proposed Variational Dropout technique that provided an elegant
Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case …

Through the success of deep learning in various domains, artificial neural networks are
currently among the most used artificial intelligence methods. Taking inspiration from the …

In this paper, we present a simple and modularized neural network architecture, named
interleaved group convolutional neural networks (IGCNets). The main point lies in a novel …

Artificial Neural Networks are connectionist systems that perform a given task by learning on
examples without having prior knowledge about the task. This is done by finding an optimal …