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Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution.
Key word-in-context index for technical literature (kwic index)
Luhn, H. P · 1960
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On the genetic basis of variation and heterogeneity of DNA base composition
Sueoka, N · 1962
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Novelty Detection and Neural Network Validation
Bishop, C. M · 1994
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The value of prior knowledge in discovering motifs with MEME
Bailey, T. L. and Elkan, C · 1995
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Diversity of the human intestinal microbial flora
Eckburg, P. B., Bik, E. M., Bernstein, C. N., Purdom, E., Dethlefsen, L., Sargent, M., Gill, S. R., Nelson, K. E., and Relman, D. A · 2005
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The All-Species Living Tree project: a 16S rRNA-based phylogenetic tree of all sequenced type strains
Yarza, P., Richter, M., Peplies, J., Euzeby, J., Amann, R., Schleifer, K.-H., Ludwig, W., Glöckner, F. O., and Rosselló-Móra, R · 2008
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Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models
Brady, A. and Salzberg, S. L · 2009
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Alignment-free sequence comparison (I): statistics and power
Reinert, G., Chew, D., Sun, F., and Waterman, M. S · 2009
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Evidence of selection upon genomic GC-content in bacteria
Hildebrand, F., Meyer, A., and Eyre-Walker, A · 2010
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NBC: the naive Bayes classification tool webserver for taxonomic classification of metagenomic reads
Rosen, G. L., Reichenberger, E. R., and Rosenfeld, A. M · 2010
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NotMNIST dataset, 2011
Bulatov, Y · 2011
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Taxonomic metagenome sequence assignment with structured output models
Patil, K. R., Haider, P., Pope, P. B., Turnbaugh, P. J., Morrison, M., Scheffer, T., and McHardy, A. C · 2011
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Machine learning that matters
Wagstaff, K. L · 2012
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Inferring phylogenies of evolving sequences without multiple sequence alignment
Chan, C. X., Bernard, G., Poirion, O., Hogan, J. M., and Ragan, M. A · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
Cited alongside, same era.
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J. and Troyanskaya, O. G · 2015
Cited alongside, same era.
Alignment-free oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences
Ahlgren, N. A., Ren, J., Lu, Y. Y., Fuhrman, J. A., and Sun, F · 2016
Cited alongside, same era.
Concrete problems in AI safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
Cited alongside, same era.
Alignment-free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer
Bernard, G., Chan, C. X., and Ragan, M. A · 2016
Cited alongside, same era.
PixelCNN++: A PixelCNN implementation with discretized logistic mixture likelihood and other modifications
Salimans, T., Karpathy, A., Chen, X., Kingma, D. P., and Bulatov, Y · 2017
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Uncertainty in the variational information bottleneck
Alemi, A. A., Fischer, I., and Dillon, J. V · 2018
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A deep learning approach to pattern recognition for short DNA sequences
Busia, A., Dahl, G. E., Fannjiang, C., Alexander, D. H., Dorfman, E., Poplin, R., McLean, C. Y., Chang, P.-C., and DePristo, M · 2018
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WAIC, but why? Generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 2018
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Generative recurrent networks for de novo drug design
Gupta, A., Müller, A. T., Huisman, B. J., Fuchs, J. A., Schneider, P., and Schneider, G · 2018
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
Cited alongside, same era.
Exploring the limits of language modeling
Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., and Wu, Y · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Oord, A. v. d., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Conditional image generation with PixelCNN decoders
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Generating and designing DNA with deep generative models
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., and Frey, B. J · 2017
Cited alongside, same era.
Later among the works it cites.
Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T. G · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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Does your model know the digit 6 is not a cat? a less biased evaluation of" outlier" detectors
Shafaei, A., Schmidt, M., and Little, J. J · 2018
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Metagenomic unmapped reads provide important insights into human microbiota and disease associations
Zhu, Z., Ren, J., Michail, S., and Sun, F · 2018
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A primer on deep learning in genomics
Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., and Telenti, A · 2018
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Analytical and clinical validation of a microbial cell-free dna sequencing test for infectious disease
Blauwkamp, T. A., Thair, S., Rosen, M. J., Blair, L., Lindner, M. S., Vilfan, I. D., Kawli, T., Christians, F. C., Venkatasubrahmanyam, S., Wall, G. D., et al · 2019
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Hybrid models with deep and invertible features
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
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New insights from uncultivated genomes of the global human gut microbiome
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S., and Kyrpides, N. C · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
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The promises and pitfalls of machine learning for detecting viruses in aquatic metagenomes
Ponsero, A. J. and Hurwitz, B. L · 2019
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