deep learning research paper

Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and ...

www.microsoft.com·8.9 MB

by A Behrouz · Cited by 90 — In this paper, we aim to present a unifying learning paradigm that not only provides new insights about existing algorithms, methods, and architectures,

abehrouz.github.io·5.8 MB

by T Hoefler · 2021 · Cited by 1436 — In this paper, we survey prior work on sparsity in deep learning and provide an extensive tutorial of sparsification for both inference and training.

www.jmlr.org·3.4 MB

Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Through enabling ...

royalsociety.org·3.3 MB

As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, ...

nn.cs.utexas.edu·2.0 MB

From the earliest days of pattern recognition22,23, the aim of research- ers has been to replace hand-engineered features with trainable multilayer networks, ...

www.cs.toronto.edu·2.0 MB

The method proceeds by training a supervised DNN for feature extrac- tion for the targeted classification task and using the extracted feature representation.

rex.libraries.wsu.edu·1.8 MB

Rapid developments in data generation, storage and analytics propel increasing interests in harnessing the power of big datasets (Cooper et al., 2013; Xu et al.

wastedfood.american.edu·1.8 MB

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data.

arxiv.org·1.8 MB

by A Krizhevsky · Cited by 153783 — Simple recognition tasks can be solved quite well with datasets of this size, especially if they are augmented with label-preserving transformations. For ...

proceedings.neurips.cc·1.4 MB

Finally, governments should encourage research and education in machine learning algorithms and applications, particularly those that enhance privacy, security, ...

www.belfercenter.org·1.1 MB

by Y LeCun · 1998 · Cited by 88603 — This paper reviews var- ious methods applied to handwritten character recognition and compares them on a standard handwritten digit recog- nition task.

vision.stanford.edu·982.5 KB

by S Han · Cited by 10896 — Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks ...

users.soe.ucsc.edu·951.0 KB

Training deep multi-layered neural networks is known to be hard. The standard learning strategy— consisting of randomly initializing the weights of the network ...

jmlr.org·950.2 KB

Government entities and social science researchers are increasingly interested in using machine learning to shed light on important policy questions.

www.dol.gov·878.6 KB

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning.

faculty.sites.iastate.edu·839.6 KB

by SH Lin · Cited by 3 — The paper finds improvements in model size, training performance, and scalability with 100 billion parameter models on up to. 400 GPUs using ...

pssg.cs.umd.edu·718.8 KB

In later chapters we present a variety of case studies mostly in computer vision, speech recognition, and machine translation.

home.ttic.edu·516.3 KB

Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network ...

zenodo.org·393.3 KB

This paper gives overview of Artificial Neural. Network, working & training of ANN. It also explain the application and advantages of ANN. Keywords:- ANN( ...

ijritcc.org·166.7 KB