Farah Naaz, Ph.D. - Related publications

Washington University, Saint Louis, St. Louis, MO 
Learning and Memory, Visual Cognition, Computer based Learning
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50 most relevant papers in past 60 days:
Year Citation  Score
2019 Nouh RM, Lee HH, Lee WJ, Lee JD. A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services. Sensors (Basel, Switzerland). 19. PMID 30669651 DOI: 10.3390/s19020431   
2019 Lee SM, Seo JB, Yun J, Cho YH, Vogel-Claussen J, Schiebler ML, Gefter WB, van Beek EJR, Goo JM, Lee KS, Hatabu H, Gee J, Kim N. Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art. Journal of Thoracic Imaging. 34: 75-85. PMID 30802231 DOI: 10.1097/RTI.0000000000000387   
2019 Ribeiro LMC, Mamede S, de Brito EM, Moura AS, de Faria RMD, Schmidt HG. Effects of deliberate reflection on students' engagement in learning and learning outcomes. Medical Education. PMID 30677157 DOI: 10.1111/medu.13798   
2019 Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. Continual lifelong learning with neural networks: A review. Neural Networks : the Official Journal of the International Neural Network Society. 113: 54-71. PMID 30780045 DOI: 10.1016/j.neunet.2019.01.012   
2019 Guo N, Wang J, Wang X. Effect of starvation and high-carbohydrate diet on learning ability of . Heliyon. 5: e01289. PMID 30891518 DOI: 10.1016/j.heliyon.2019.e01289   
2019 Luo Z, Hauskrecht M. Hierarchical Active Learning with Proportion Feedback on Regions. Machine Learning and Knowledge Discovery in Databases : European Conference, Ecml Pkdd ... : Proceedings. Ecml Pkdd (Conference). 11052: 464-480. PMID 30740605 DOI: 10.1007/978-3-030-10928-8_28   
2019 Han E, Klein KC. Pre-Class Learning Methods for Flipped Classrooms. American Journal of Pharmaceutical Education. 83: 6922. PMID 30894772 DOI: 10.5688/ajpe6922   
2019 Lavdas I, Glocker B, Rueckert D, Taylor SA, Aboagye EO, Rockall AG. Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data. Clinical Radiology. PMID 30803815 DOI: 10.1016/j.crad.2019.01.012   
2019 Sewell DK, Jach HK, Boag RJ, Van Heer CA. Combining error-driven models of associative learning with evidence accumulation models of decision-making. Psychonomic Bulletin & Review. PMID 30719625 DOI: 10.3758/s13423-019-01570-4   
2019 Gars J, Ward PS. Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India. World Development. 115: 178-189. PMID 30828125 DOI: 10.1016/j.worlddev.2018.11.014   
2019 Ballard IC, McClure SM. Joint Modeling of Reaction Times and Choice Improves Parameter Identifiability in Reinforcement Learning Models. Journal of Neuroscience Methods. PMID 30664916 DOI: 10.1016/j.jneumeth.2019.01.006   
2019 Drouin A, Letarte G, Raymond F, Marchand M, Corbeil J, Laviolette F. Interpretable genotype-to-phenotype classifiers with performance guarantees. Scientific Reports. 9: 4071. PMID 30858411 DOI: 10.1038/s41598-019-40561-2   
2019 Moreira IC, Ramos I, Rua Ventura S, Pereira Rodrigues P. Learner's perception, knowledge and behaviour assessment within a breast imaging E-Learning course for radiographers. European Journal of Radiology. 111: 47-55. PMID 30691664 DOI: 10.1016/j.ejrad.2018.12.006   
2019 Arieli-Attali M, Ou L, Simmering VR. Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data. Frontiers in Psychology. 10: 83. PMID 30787889 DOI: 10.3389/fpsyg.2019.00083   
2019 Caligiore D, Arbib MA, Miall RC, Baldassarre G. The super-learning hypothesis: Integrating learning processes across cortex, cerebellum and basal ganglia. Neuroscience and Biobehavioral Reviews. 100: 19-34. PMID 30790636 DOI: 10.1016/j.neubiorev.2019.02.008   
2019 Lee CY, Chen YP. Machine learning on adverse drug reactions for pharmacovigilance. Drug Discovery Today. PMID 30876845 DOI: 10.1016/j.drudis.2019.03.003   
2019 Levine AB, Schlosser C, Grewal J, Coope R, Jones SJM, Yip S. Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis. Trends in Cancer. 5: 157-169. PMID 30898263 DOI: 10.1016/j.trecan.2019.02.002   
2019 Livesey EJ, McLaren IP. Revisiting peak shift on an artificial dimension: Effects of stimulus variability on generalisation. Quarterly Journal of Experimental Psychology (2006). 72: 132-150. PMID 30803341 DOI: 10.1177/1747021817739832   
2019 Yang Y, Ye Z, Su Y, Zhao Q, Li X, Ouyang D. Deep learning for prediction of pharmaceutical formulations. Acta Pharmaceutica Sinica. B. 9: 177-185. PMID 30766789 DOI: 10.1016/j.apsb.2018.09.010   
2019 Schiavo JK, Froemke RC. Capacities and neural mechanisms for auditory statistical learning across species. Hearing Research. PMID 30797628 DOI: 10.1016/j.heares.2019.02.002   
2019 Pfeiffer CN, Jabbar A. Adaptive e-Learning: Emerging Digital Tools for Teaching Parasitology. Trends in Parasitology. PMID 30738631 DOI: 10.1016/j.pt.2019.01.008   
2019 Cui C, Chou SS, Brattain L, Lehman CD, Samir AE. Data Engineering for Machine Learning in Women's Imaging and Beyond. Ajr. American Journal of Roentgenology. 1-10. PMID 30779668 DOI: 10.2214/AJR.18.20464   
2019 Li B, Xie W, Zeng W, Liu W. Learning to Update for Object Tracking with Recurrent Meta-learner. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. PMID 30802856 DOI: 10.1109/TIP.2019.2900577   
2019 Nolan MW, Balogh M, Waltman SS. Virtual Oncology Clinic. Journal of Veterinary Medical Education. 1-5. PMID 30721107 DOI: 10.3138/jvme.0817-107r   
2019 Korfiatis P, Erickson B. Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas. Clinical Radiology. PMID 30850092 DOI: 10.1016/j.crad.2019.01.028   
2019 Crowson MG, Ranisau J, Eskander A, Babier A, Xu B, Kahmke RR, Chen JM, Chan TCY. A contemporary review of machine learning in otolaryngology-head and neck surgery. The Laryngoscope. PMID 30706465 DOI: 10.1002/lary.27850   
2019 Ramola R, Jain S, Radivojac P. Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 24: 124-135. PMID 30864316   
2019 Zhang Y, Wang Y, Zhou W, Fan Y, Zhao J, Zhu L, Lu S, Lu T, Chen Y, Liu H. A Combined Drug Discovery Strategy Based on Machine Learning and Molecular Docking. Chemical Biology & Drug Design. PMID 30688405 DOI: 10.1111/cbdd.13494   
2019 Girguis MS, Li L, Lurmann F, Wu J, Urman R, Rappaport E, Breton C, Gilliland F, Stram D, Habre R. Exposure measurement error in air pollution studies: A framework for assessing shared, multiplicative measurement error in ensemble learning estimates of nitrogen oxides. Environment International. 125: 97-106. PMID 30711654 DOI: 10.1016/j.envint.2018.12.025   
2019 Jonsson A. Deep Reinforcement Learning in Medicine. Kidney Diseases (Basel, Switzerland). 5: 18-22. PMID 30815460 DOI: 10.1159/000492670   
2019 Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature. 566: 195-204. PMID 30760912 DOI: 10.1038/s41586-019-0912-1   
2019 Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WHW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. European Heart Journal. PMID 30815669 DOI: 10.1093/eurheartj/ehz056   
2019 Dobson JL, Linderholm T, Stroud L. Retrieval practice and judgements of learning enhance transfer of physiology information. Advances in Health Sciences Education : Theory and Practice. PMID 30810846 DOI: 10.1007/s10459-019-09881-w   
2019 Radulescu A, Niv Y, Ballard I. Holistic Reinforcement Learning: The Role of Structure and Attention. Trends in Cognitive Sciences. PMID 30824227 DOI: 10.1016/j.tics.2019.01.010   
2019 Ried K, Müller T, Briegel HJ. Modelling collective motion based on the principle of agency: General framework and the case of marching locusts. Plos One. 14: e0212044. PMID 30785947 DOI: 10.1371/journal.pone.0212044   
2019 Sajedian I, Badloe T, Rho J. Optimisation of colour generation from dielectric nanostructures using reinforcement learning. Optics Express. 27: 5874-5883. PMID 30876182 DOI: 10.1364/OE.27.005874   
2019 Zheng YY, Kong JL, Jin XB, Wang XY, Zuo M. CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture. Sensors (Basel, Switzerland). 19. PMID 30832283 DOI: 10.3390/s19051058   
2019 Uribe CF, Mathotaarachchi S, Gaudet VC, Smith KC, Rosa-Neto P, Benard F, Black SE, Zukotynski K. Part 1: Introduction to Machine Learning in the Nuclear Medicine Context. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. PMID 30733322 DOI: 10.2967/jnumed.118.223495   
2019 Liu J, Gong M, He H. Deep associative neural network for associative memory based on unsupervised representation learning. Neural Networks : the Official Journal of the International Neural Network Society. 113: 41-53. PMID 30780044 DOI: 10.1016/j.neunet.2019.01.004   
2019 Möller M, Bogacz R. Learning the payoffs and costs of actions. Plos Computational Biology. 15: e1006285. PMID 30818357 DOI: 10.1371/journal.pcbi.1006285   
2019 Whitehead T, Irwin B, Hunt PA, Segall M, Conduit G. Imputation of Assay Bioactivity Data using Deep Learning. Journal of Chemical Information and Modeling. PMID 30753070 DOI: 10.1021/acs.jcim.8b00768   
2019 Li J, Gao M, D'Agostino R. Evaluating classification accuracy for modern learning approaches. Statistics in Medicine. PMID 30701585 DOI: 10.1002/sim.8103   
2019 Li X, Du B, Zhang Y, Xu C, Tao D. Iterative Privileged Learning. Ieee Transactions On Neural Networks and Learning Systems. PMID 30843851 DOI: 10.1109/TNNLS.2018.2889906   
2019 Cashaback JGA, Lao CK, Palidis DJ, Coltman SK, McGregor HR, Gribble PL. The gradient of the reinforcement landscape influences sensorimotor learning. Plos Computational Biology. 15: e1006839. PMID 30830902 DOI: 10.1371/journal.pcbi.1006839   
2019 Khoo EJ, Chua SH, Kutzsche S. Applying educational theories into planning a psychomotor learning activity: an undergraduate neonatal resuscitation programme experience. Archivos Argentinos De Pediatria. 117: e181-e187. PMID 30869503 DOI: 10.5546/aap.2019.eng.e181   
2019 LaPierre N, Ju CJ, Zhou G, Wang W. MetaPheno: A Critical Evaluation of Deep Learning and Machine Learning in Metagenome-Based Disease Prediction. Methods (San Diego, Calif.). PMID 30885720 DOI: 10.1016/j.ymeth.2019.03.003   
2019 Dorfman HM, Bhui R, Hughes BL, Gershman SJ. Causal Inference About Good and Bad Outcomes. Psychological Science. 956797619828724. PMID 30759048 DOI: 10.1177/0956797619828724   
2019 Zeng M, Li M, Fei Z, Wu F, Li Y, Pan Y, Wang J. A deep learning framework for identifying essential proteins by integrating multiple types of biological information. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 30736002 DOI: 10.1109/TCBB.2019.2897679   
2019 Kang B, Nguyen TQ. Random Forest with Learned Representations for Semantic Segmentation. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. PMID 30872230 DOI: 10.1109/TIP.2019.2905081   
2019 Gong C, Liu T, Yang J, Tao D. Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. Ieee Transactions On Neural Networks and Learning Systems. PMID 30736009 DOI: 10.1109/TNNLS.2019.2892403