[HTML][HTML] A state-of-the-art survey on deep learning theory and architectures
In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …
domains. This new field of machine learning has been growing rapidly and has been …
The history began from alexnet: A comprehensive survey on deep learning approaches
Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …
the past few years. This new field of machine learning has been growing rapidly and applied …
Lamda: Language models for dialog applications
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of
Transformer-based neural language models specialized for dialog, which have up to 137B …
Transformer-based neural language models specialized for dialog, which have up to 137B …
Learning from noisy labels with distillation
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a
vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise …
vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise …
[HTML][HTML] Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges
The current expansion of theory and research on artificial intelligence in management and
organization studies has revitalized the theory and research on decision-making in …
organization studies has revitalized the theory and research on decision-making in …
Regularizing deep neural networks by noise: Its interpretation and optimization
Overfitting is one of the most critical challenges in deep neural networks, and there are
various types of regularization methods to improve generalization performance. Injecting …
various types of regularization methods to improve generalization performance. Injecting …
In defense of the triplet loss again: Learning robust person re-identification with fast approximated triplet loss and label distillation
The comparative losses (typically, triplet loss) are appealing choices for learning person re-
identification (ReID) features. However, the triplet loss is computationally much more …
identification (ReID) features. However, the triplet loss is computationally much more …
Autogan-distiller: Searching to compress generative adversarial networks
The compression of Generative Adversarial Networks (GANs) has lately drawn attention,
due to the increasing demand for deploying GANs into mobile devices for numerous …
due to the increasing demand for deploying GANs into mobile devices for numerous …
Gan slimming: All-in-one gan compression by a unified optimization framework
Generative adversarial networks (GANs) have gained increasing popularity in various
computer vision applications, and recently start to be deployed to resource-constrained …
computer vision applications, and recently start to be deployed to resource-constrained …
Advanced dropout: A model-free methodology for bayesian dropout optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …