Bioinformatics: The Machine Learning Approach . Second Edition. Adaptive Computation and Machine Learning. By Pierre  Baldi and , Sørenv  Brunak. A Bradford Book. Cambridge (Massachusetts): MIT Press . $49.95. xxiii + 452 p; ill.; index. ISBN: 0–262–02506‐X. 2001

by Rannala, Bruce
Published in The Quarterly review of biology

Bioinformatics: The Machine Learning Approach. Adaptive Computation and Machine Learning. Pierre Baldi , Soren Brunak

by Slice, Dennis E.
Published in The Quarterly review of biology

Bioinformatics: The Machine Learning Approach. Adaptive Computation and Machine Learning

by Slice, Dennis E.
Published in The Quarterly Review of Biology

Ethem Alpaydin. Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). The MIT Press, 2004. ISBN: 0 262 01211 1 Price £32.95 / $50.00 (hardcover). xxx+415 pages

by Khan, Shahzad
Published in Natural Language Engineering

AlpaydinEthem. Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). The MIT Press, 2004. ISBN: 0 262 01211 1 Price £32.95 / $50.00 (hardcover). xxx+415 pages

by Khan, Shahzad

REINFORCEMENT LEARNING: AN INTRODUCTION by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii + 322 pp, ISBN 0-262-19398-1, (hardback, £31.95)

by Andrew, Alex M.
Published in Robotica

Single-stage gradient-based stellarator coil design: stochastic optimization

by Wechsung, Florian, Giuliani, Andrew, Landreman, Matt, Cerfon, Antoine, Stadler, Georg
Published in Nuclear fusion

A Survey of Techniques for Event Detection in Twitter

by Atefeh, Farzindar, Khreich, Wael
Published in Computational intelligence

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network

by Jung, Hwejin, Kim, Bumsoo, Lee, Inyeop, Yoo, Minhwan, Lee, Junhyun, Ham, Sooyoun, Woo, Okhee, Kang, Jaewoo
Published in PloS one

Self-verifying variational quantum simulation of lattice models

by Kokail, C, Maier, C, van Bijnen, R, Brydges, T, Joshi, M K, Jurcevic, P, Muschik, C A, Silvi, P, Blatt, R, Roos, C F, Zoller, P
Published in Nature (London)

Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling

by Schmitz, Gunnar, Artiukhin, Denis G., Christiansen, Ove
Published in The Journal of chemical physics

Learning from crowds in digital pathology using scalable variational Gaussian processes

by López-Pérez, Miguel, Amgad, Mohamed, Morales-Álvarez, Pablo, Ruiz, Pablo, Cooper, Lee A. D., Molina, Rafael, Katsaggelos, Aggelos K.
Published in Scientific reports

Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach

by Hilpert, Peter, Vowels, Matthew R, Mestdagh, Merijn, Sels, Laura
Published in PloS one

The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

by Bergmann, Paul, Batzner, Kilian, Fauser, Michael, Sattlegger, David, Steger, Carsten
Published in International journal of computer vision

Machine Learning for Mortality Analysis in Patients with COVID-19

by Sánchez-Montañés, Manuel, Rodríguez-Belenguer, Pablo, Serrano-López, Antonio J, Soria-Olivas, Emilio, Alakhdar-Mohmara, Yasser
Published in International journal of environmental research and public health

Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

by Canatar, Abdulkadir, Bordelon, Blake, Pehlevan, Cengiz
Published in Nature communications

Calibrated simplex-mapping classification

by Heese, Raoul, Schmid, Jochen, Walczak, Michał, Bortz, Michael
Published in PloS one

Estimation of Photometric Redshifts. II. Identification of Out-of-distribution Data with Neural Networks

by Lee, Joongoo, Shin, Min-Su
Published in The Astronomical journal

Search for boosted diphoton resonances in the 10 to 70 GeV mass range using 138 fb−1 of 13 TeV pp collisions with the ATLAS detector

by Abbott, B., Abbott, D. C., Albicocco, P., Asada, H., Atkin, R. J., Azuelos, G., Balasubramanian, R., Ballabene, E., Balli, F., Barr, A. J., Basan, A., Bjørke, K., Blocker, C., Bruschi, M., Cardenas, J. C. J., Carnesale, M., Castillo Garcia, L., Castro, N. F., Charman, T. P., Chwastowski, J. J., Cooper-Sarkar, A. M., D’amen, G., Darmora, S., De Sanctis, U., Delmastro, M., Denisov, S. P., Dubovsky, M., Dunne, K., Dziedzic, B. S., Ellajosyula, V., Fabbri, F., Fabbri, L., Fox, H., Francescato, S., Frid, Y. Y., Hadef, A., Hall, J. J., Hangal, D. A., He, Y., Helary, L., Hod, N., Huseynov, N., Ingebretsen Carlson, T., Kawamoto, T., Korcyl, K., Kourkoumeli-Charalampidi, A., Kumar, M., Lancaster, A. N., Li, H., Livan, M., Llorente Merino, J., Lobodzinska, E. M., Marcon, C., Martin-Haugh, S., Mastroberardino, A., Megy, T., Metcalfe, J., Miller, L. S., Murray, W. J., Nairz, A. M., Naseri, M., Noacco Rosende, S. J., Ould-Saada, F., Parrish, E. W., Przybycien, M., Qin, Y., Reidelsturz, J. A., Resconi, S., Rezanova, O. L., Roberts, B. A., Rodriguez Garcia, Y., Roland, B., Saha, S., Schaffer, A. C., Schegelsky, V. A., Serin, L., Shaheen, R., Shatalov, P. B., Shimmin, C. O., Shipsey, I. P. J., Smykiewicz, A., Staroba, P., Takashima, R., Talyshev, A. A., Tarem, S., Taylor, G. N., Trandafir, I. S., Uysal, Z., Vaslin, L., Wakasa, R., Walbrecht, V. M., Walker, R., Wang, H., Waugh, B. M., Weber, C., Weidberg, A. R., Wosiek, B. K., Xu, D., Zeitnitz, C., Zhu, C.
Published in The journal of high energy physics

Snorkel: rapid training data creation with weak supervision

by Ratner, Alexander, Bach, Stephen H., Ehrenberg, Henry, Fries, Jason, Wu, Sen, Ré, Christopher
Published in The VLDB journal