Presentation Topics
- Statistical inference for noisy nonlinear ecological dynamic systems. SN Wood, Nature, 2010
- The spread of obesity in a large social network over 32 years. NA Christakis and JH Fowler, New England Journal of Medicine, 2007
- Nonlinear dimensionality reduction by locally linear embedding. ST Roweis, LK Saul, Science, 2000
- A Bayesian approach to filtering junk e-mail. M. Sahami et al., Learning for Text Categorization, 1998
- Regression shrinkage and selection via the lasso. R Tibshirani, Journal of the Royal Statistical Society. Series B, 1996
- Bayesian integration in sensorimotor learning. KP Körding and DM Wolpert, Nature, 2004
- A Bayesian approach to unsupervised one-shot learning of object categories. L Fei-Fei, R Fergus, P Perona, Ninth IEEE International Conference on Computer Vision (ICCV'03), 2003
- Learning Object Categories from Google's Image Search. R Fergus, L Fei-Fei, P Perona, A Zisserman, Tenth IEEE International Conference on Computer Vision (ICCV'05), 2005
- Bayesian ranking of biochemical system models. V Vyshemirsky and MA Girolami, Bioinformatics, 2008
- Phylogenetic inference using whole genomes. B Rannala and Z Yang, Annual Review of Genomics and Human Genetics, 2008
- Greenhouse-gas emission targets for limiting global warming to 2 C M Meinshausen et al. Nature, 2009
- Causal protein-signaling networks derived from multiparameter single-cell data. K Sachs et al., Science, 2005
- Sample selection bias as a specification error. JJ Heckman, Econometrica, 1979
- Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival. Nicolau, et al. PNAS, 108(17):7265–7270, 2011. doi:10.1073/pnas.1102826109
More! Papers on fMRI "Mind Reading" - Distributed and overlapping representations of faces and objects in ventral temporal cortex. Haxby, et al. Science 293(5539), 2425–30, 2001. doi:10.1126/science.1063736
- Training fMRI classifiers to discriminate cognitive states across multiple subjects. Wang et al. Proc. of The 17th Annual Conference on Neural Information Processing Systems, 2003
- Learning to Decode Cognitive States from Brain Images. Mitchell et al. Machine Learning, 57(1/2), 145–175, 2004. doi:10.1023/B:MACH.0000035475.85309.1b
- Decoding the visual and subjective contents of the human brain. Kamitani & Tong. Nature Neuroscience, 8(5), 679–85, 2005. doi:10.1038/nn1444
- Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Mourão-Miranda et al. NeuroImage, 28(4), 980–95, 2005. doi:10.1016/j.neuroimage.2005.06.070
- Predicting human brain activity associated with the meanings of nouns. Mitchell, et al. Science, 320(5880), 1191–5, 2008. doi:10.1126/science.1152876.
- Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings. Shinkareva, et al. PloS one, 3(1), e1394, 2008. doi:10.1371/journal.pone.0001394
- Unconscious determinants of free decisions in the human brain. Soon, et al. Nature neuroscience, 11(5), 543–5, 2008. doi:10.1038/nn.2112
- Recruitment of an area involved in eye movements during mental arithmetic. Knops et al. Science, 324(5934), 1583–5, 2009. doi:10.1126/science.1171599
- Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Marquand et al. NeuroImage, 49(3), 2178–89, 2010. doi:10.1016/j.neuroimage.2009.10.072
- Reproducibility distinguishes conscious from nonconscious neural representations. Schurger et al. Science 327(5961), 97–9, 2010. doi:10.1126/science.1180029
More! Papers on intersubject classification using MRI - Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Ecker et al. NeuroImage, 49(1), 44–56. 2001. doi:10.1016/j.neuroimage.2009.08.024
- Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Plant et al. NeuroImage, 50(1), 162–74, 2010. doi:10.1016/j.neuroimage.2009.11.046
- Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Davatzikos et al. Neurobiology of aging, 32(12), 2322.e19–27, 2011. doi:10.1016/j.neurobiolaging.2010.05.023
- Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND. Vemuri, et al. NeuroImage, 55(2), 522–31, 2011. doi:10.1016/j.neuroimage.2010.12.073
- Diagnostic neuroimaging across diseases. Klöppel, et al. NeuroImage, 61(2), 457–63, 2012. doi:10.1016/j.neuroimage.2011.11.002
- Prediction of Individual Brain Maturity Using fMRI. Dosenbach et al. Science, 329(5997), 1358–1361, 2010. doi:10.1126/science.1194144
More! - Margin based feature selection - theory and algorithms. Gilad-Bachrach et al. Twenty-first international conference on Machine learning - ICML ’04 (p. 43), 2004. New York, New York, USA: ACM Press. doi:10.1145/1015330.1015352
- Decoding Human Cytomegalovirus. Stern-Ginossar et al. Science 338(6110):1088-1093, 2012. doi:10.1126/science.1227919
- Teamwork: improved eQTL mapping using combinations of machine learning methods. Ackermann, et al. PloS one, 7(7), e40916, 2012. doi:10.1371/journal.pone.0040916
- Human gut microbiome viewed across age and geography. Yatsunenko et al. Nature, 486(7402), 222–7, 2012. doi:10.1038/nature11053
- A perceptual metric for photo retouching. Kee & Farid. Proceedings of the National Academy of Sciences, 108(50), 19907–12, 2011. doi:10.1073/pnas.1110747108
- Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Kharchenko et al. Nature 471:480–485, 2011. doi:10.1038/nature09725
- Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Neumann et al. Nature 464, 721-727, 2010. doi:10.1038/nature08869
- Probabilistic assessment of sea level during the last interglacial stage. Kopp et al. Nature 462:863-867, 2009. doi:10.1038/nature08686
- Combinatorial binding predicts spatio-temporal cis-regulatory activity. Zinzen, et al. Nature 462:65-70, 2009. doi:10.1038/nature08531
- Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Krogan et al. Nature 440, 637-643, 2006. doi:10.1038/nature04670
- Quantifying the Relationships among Drug Classes. Hert et al. J. Chem. Inf. Model., 48(4):755–765, 2008. doi:10.1021/ci8000259
One more Mind Reading paper! - Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Miyawaki et al. Neuron, 60(5), 915–29, 2008. doi:10.1016/j.neuron.2008.11.004 New!
No! Wait! More Mind Reading papers! - Predicting the orientation of invisible stimuli from activity in primary visual cortex. Haynes & Rees. Nature Neurosci. 8, 686–691, 2005.
- Predicting the Stream of Consciousness from Activity in Human Visual Cortex. Haynes & Rees. Current Biology, 15(14):1301-1307, 2005. doi:10.1016/j.cub.2005.06.026
- Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Davatzikos, C. et al. Neuroimage 28, 663–668, 2005.
- Real-time decoding and training of attention. deBettencourt et al. Journal of Vision, 12(9):377, 2012. doi: 10.1167/12.9.377