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Topic: Machine Learning ✕
2,559 papers on Machine Learning
Most cited Newest first Oldest first
117,037 citations 🔓 OPEN ACCESS 2013
American Psychiatric Association Publishing eBooks

Diagnostic and Statistical Manual of Mental Disorders

Andrés Santiago González Moreno
En la actualidad es importante detectar a tiempo la depresión, con el fin de llevar un tratamiento oportuno y mejorar la calidad de vida de las personas, este proceso de evaluación o detección requiere el uso de herramientas o test…
DOI: 10.1176/appi.books.9780890425596 📄 PDF
63,589 citations 🔓 OPEN ACCESS 2012
ORBi (University of Liège)

Scikit-learn: Machine Learning in Python

Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Michel, Vincent, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Müller, Andreas, Nothman, Joel, Louppe, Gilles, Peter Prettenhofer, Ron J. Weiss, Dubourg, Vincent, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of…
DOI: 10.48550/arxiv.1201.0490 📄 PDF
54,117 citations 🔓 OPEN ACCESS 2016

XGBoost

Tianqi Chen, Carlos Guestrin
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning…
DOI: 10.1145/2939672.2939785 📄 PDF
48,169 citations 🔓 OPEN ACCESS 2013
Molecular Biology and Evolution

MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0

Koichiro Tamura, Glen Stecher, Daniel Peterson, Alan Filipski, Sudhir Kumar
The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens,…
DOI: 10.1093/molbev/mst197 📄 PDF
47,923 citations 🔓 OPEN ACCESS 2021
Nature

Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon Köhl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera‐Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michał Zieliński, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian W. Bodenstein, David Silver, Oriol Vinyals
Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1–4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents…
DOI: 10.1038/s41586-021-03819-2 📄 PDF
41,426 citations 2011
ACM Transactions on Intelligent Systems and Technology

LIBSVM

Chih-Chung Chang, Chih‐Jen Lin
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine…
DOI: 10.1145/1961189.1961199 View paper →
33,902 citations 2014

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining…
Jan 1, 2014 View paper →
25,366 citations 🔓 OPEN ACCESS 2014

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Kyunghyun Cho, Bart van Merriënboer, Çağlar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014.
DOI: 10.3115/v1/d14-1179 📄 PDF
24,747 citations 🔓 OPEN ACCESS 2024

Learning Multiple Layers of Features from Tiny Images

Alex Krizhevsky
April 8, 2009Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried…
DOI: 10.57702/zp44cu3g 📄 PDF
23,913 citations 2009
IEEE Transactions on Knowledge and Data Engineering

A Survey on Transfer Learning

Sinno Jialin Pan, Qiang Yang
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold.…
DOI: 10.1109/tkde.2009.191 View paper →