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Journal Articles
- 2022
- [j28]Halil Bisgin
, Tanmay Bera, Leihong Wu, Hongjian Ding, Neslihan Bisgin, Zhichao Liu, Monica Pava-Ripoll, Amy Barnes, James F. Campbell, Himansi Vyas, Cesare Furlanello, Weida Tong, Joshua Xu:
Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning. Frontiers Artif. Intell. 5 (2022) - [j27]Alessia Marcolini
histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing. SoftwareX 20: 101237 (2022) - 2020
- [j26]Isotta Landi
Deep representation learning of electronic health records to unlock patient stratification at scale. npj Digit. Medicine 3 (2020) - [j25]Lauren R. Kennedy-Metz
Sensors for Continuous Monitoring of Surgeon's Cognitive Workload in the Cardiac Operating Room. Sensors 20(22): 6616 (2020) - [j24]Andrea Bizzego
Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals. Sensors 20(23): 6778 (2020) - 2019
- [j23]Leihong Wu, Zhichao Liu, Tanmay Bera, Hongjian Ding, Darryl Langley, Amy Barnes, Cesare Furlanello, Valerio Maggio
A deep learning model to recognize food contaminating beetle species based on elytra fragments. Comput. Electron. Agric. 166 (2019) - [j22]Andrea Bizzego
Evaluating reproducibility of AI algorithms in digital pathology with DAPPER. PLoS Comput. Biol. 15(3) (2019) - [j21]Gabriele Franch
MASS-UMAP: Fast and Accurate Analog Ensemble Search in Weather Radar Archives. Remote. Sens. 11(24): 2922 (2019) - [j20]Andrea Bizzego, Alessandro Battisti, Giulio Gabrieli
pyphysio: A physiological signal processing library for data science approaches in physiology. SoftwareX 10: 100287 (2019) - 2018
- [j19]Diego Fioravanti, Ylenia Giarratano, Valerio Maggio
Phylogenetic convolutional neural networks in metagenomics. BMC Bioinform. 19-S(2): 49:1-49:13 (2018) - [j18]Nastaran Mohammadian Rad
Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders. Sensors 18(10): 3533 (2018) - [j17]Nastaran Mohammadian Rad
Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders. Signal Process. 144: 180-191 (2018) - 2014
- [j16]The Fantom Consortium, RIKEN PMII, RIKEN CLST (DGT), Alistair R. R. Forrest, Hideya Kawaji, Michael Rehli, J. Kenneth Baillie, Michiel J. L. de Hoon, Vanja Haberle, Timo Lassmann, Ivan V. Kulakovskiy, Marina Lizio, Masayoshi Itoh, Robin Andersson, Christopher J. Mungall, Terrence F. Meehan, Sebastian Schmeier, Nicolas Bertin, Mette Jørgensen, Emmanuel Dimont, Erik Arner, Christian Schmidl, Ulf Schaefer, Yulia A. Medvedeva
A promoter-level mammalian expression atlas. Nat. 507(7493): 462-470 (2014) - 2013
- [j15]Davide Albanese
minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinform. 29(3): 407-408 (2013) - 2010
- [j14]Giorgio Guzzetta
A machine learning pipeline for quantitative phenotype prediction from genotype data. BMC Bioinform. 11(S-8): S3 (2010) - 2008
- [j13]Annalisa Barla
Machine learning methods for predictive proteomics. Briefings Bioinform. 9(2): 119-128 (2008) - [j12]Giuseppe Jurman
Algebraic stability indicators for ranked lists in molecular profiling. Bioinform. 24(2): 258-264 (2008) - [j11]Silvano Paoli, Giuseppe Jurman
Integrating gene expression profiling and clinical data. Int. J. Approx. Reason. 47(1): 58-69 (2008) - 2007
- [j10]Stefano Merler
Parallelizing AdaBoost by weights dynamics. Comput. Stat. Data Anal. 51(5): 2487-2498 (2007) - [j9]Samantha Riccadonna
Supervised classification of combined copy number and gene expression data. J. Integr. Bioinform. 4(3) (2007) - 2006
- [j8]Cesare Furlanello
Combining feature selection and DTW for time-varying functional genomics. IEEE Trans. Signal Process. 54(6-2): 2436-2443 (2006) - 2005
- [j7]Cesare Furlanello
Semisupervised Learning for Molecular Profiling. IEEE ACM Trans. Comput. Biol. Bioinform. 2(2): 110-118 (2005) - 2004
- [j6]Stefano Merler
Bias-Variance Control Via Hard Points Shaving. Int. J. Pattern Recognit. Artif. Intell. 18(5): 891-903 (2004) - 2003
- [j5]Cesare Furlanello
Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinform. 4: 54 (2003) - [j4]Stefano Merler
Automatic model selection in cost-sensitive boosting. Inf. Fusion 4(1): 3-10 (2003) - [j3]Cesare Furlanello
An accelerated procedure for recursive feature ranking on microarray data. Neural Networks 16(5-6): 641-648 (2003) - 1997
- [j2]Cesare Furlanello
Speaker Normalization and Model Selection of Combined Neural Networks. Connect. Sci. 9(1): 31-50 (1997) - 1993
- [j1]Bruno Crespi, Cesare Furlanello
A memory-based approach to navigation. Biol. Cybern. 69(5-6): 385-393 (1993)
Conference and Workshop Papers
- 2020
- [c26]Lauren R. Kennedy-Metz, Andrea Bizzego
Autonomic Activity and Surgical Flow Disruptions in Healthcare Providers during Cardiac Surgery. CogSIMA 2020: 200-204 - [c25]Lauren R. Kennedy-Metz, Andrea Bizzego
Feasibility of Healthcare Providers' Autonomic Activation Recognition in Real-Life Cardiac Surgery Using Noninvasive Sensors. HCI (48) 2020: 402-408 - [c24]Nicole Bussola, Alessia Marcolini, Valerio Maggio
AI Slipping on Tiles: Data Leakage in Digital Pathology. ICPR Workshops (1) 2020: 167-182 - 2019
- [c23]Andrea Bizzego
Integrating deep and radiomics features in cancer bioimaging. CIBCB 2019: 1-8 - 2017
- [c22]Conrad Bielski
Coupling early warning services, crowdsourcing, and modelling for improved decision support and wildfire emergency management. IEEE BigData 2017: 3705-3712 - [c21]Andrea Gobbi
A heat wave forecast system for Europe. IEEE BigData 2017: 3734-3738 - [c20]Gabriele Calabrò, Andrea Bizzego
M-MS: A Multi-Modal Synchrony Dataset to Explore Dyadic Interaction in ASD. IIH-MSP (1) 2017: 543-553 - 2016
- [c19]Nastaran Mohammadian Rad, Seyed Mostafa Kia
Stereotypical Motor Movement Detection in Dynamic Feature Space. ICDM Workshops 2016: 487-494 - [c18]Nastaran Mohammadian Rad
Applying Deep Learning to Stereotypical Motor Movement Detection in Autism Spectrum Disorders. ICDM Workshops 2016: 1235-1242 - 2015
- [c17]Giuseppe Jurman
The HIM glocal metric and kernel for network comparison and classification. DSAA 2015: 1-10 - 2011
- [c16]Annalisa Barla
A Machine Learning Pipeline for Discriminant Pathways Identification. CIBB 2011: 36-48 - 2010
- [c15]Giuseppe Jurman
An introduction to spectral distances in networks. WIRN 2010: 227-234 - 2008
- [c14]Pablo M. Granitto
Efficient Feature Selection for PTR-MS Fingerprinting of Agroindustrial Products. ICANN (2) 2008: 42-51 - 2007
- [c13]Stefano Merler, Giuseppe Jurman, Cesare Furlanello
Deriving the Kernel from Training Data. MCS 2007: 32-41 - 2006
- [c12]Stefano Merler, Giuseppe Jurman, Cesare Furlanello, Caterina Rizzo, Antonino Bella, Marco Massari, Marta Luisa Ciofi degli Atti:
Strategies for containing an influenza pandemic: the case of Italy. BIONETICS 2006: 11 - [c11]Annalisa Barla
Proteome Profiling without Selection Bias. CBMS 2006: 941-946 - 2005
- [c10]Stefano Merler
Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs. ICIAP 2005: 735-742 - [c9]Silvano Paoli, Giuseppe Jurman
Semisupervised Profiling of Gene Expressions and Clinical Data. WILF 2005: 284-289 - 2004
- [c8]Bruno Caprile, Stefano Merler, Cesare Furlanello
Exact Bagging with k-Nearest Neighbour Classifiers. Multiple Classifier Systems 2004: 72-81 - 2002
- [c7]Bruno Caprile, Cesare Furlanello
Highlighting Hard Patterns via AdaBoost Weights Evolution. Multiple Classifier Systems 2002: 72-80 - 2001
- [c6]Stefano Merler, Cesare Furlanello
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis. Multiple Classifier Systems 2001: 32-42 - 2000
- [c5]Cesare Furlanello, Stefano Merler:
Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS. Multiple Classifier Systems 2000: 220-229 - 1995
- [c4]Cesare Furlanello, Diego Giuliani, Edmondo Trentin, Daniele Falavigna:
Application of Generalized Radial Basis Functions In Speaker Normalization and Identification. ISCAS 1995: 1704-1707 - 1994
- [c3]Cesare Furlanello, Diego Giuliani, Edmondo Trentin:
Connectionist Speaker Normalization with Generalized Resource Allocating Networks. NIPS 1994: 865-874 - 1993
- [c2]Bruno Crespi, Cesare Furlanello, Luigi Stringa:
Memory-Based Navigation. IJCAI 1993: 1654-1660 - 1992
- [c1]Anio O. Arigoni, Cesare Furlanello
Incremental Learning of Roughly Represented Concepts. IPMU 1992: 192-198
Informal and Other Publications
- 2024
- [i16]Gianpaolo Alvari, Ersilia Vallefuoco, Melanie Cristofolini, Elio Salvadori, Marco Dianti, Alessia Moltani, Davide Dal Castello, Paola Venuti, Cesare Furlanello:
Exploring Physiological Responses in Virtual Reality-based Interventions for Autism Spectrum Disorder: A Data-Driven Investigation. CoRR abs/2404.07159 (2024) - 2020
- [i15]Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah T. Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto:
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale. CoRR abs/2003.06516 (2020) - 2019
- [i14]Andrea Gobbi, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello:
High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning. CoRR abs/1909.07786 (2019) - [i13]Luca Coviello, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello:
In-field grape berries counting for yield estimation using dilated CNNs. CoRR abs/1909.12083 (2019) - [i12]Gabriele Franch, Giuseppe Jurman, Luca Coviello, Marta Pendesini, Cesare Furlanello:
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive. CoRR abs/1910.01211 (2019) - 2017
- [i11]Marco Cristoforetti, Giuseppe Jurman, Andrea I. Nardelli, Cesare Furlanello:
Towards meaningful physics from generative models. CoRR abs/1705.09524 (2017) - [i10]Cesare Furlanello, Manlio De Domenico, Giuseppe Jurman, Nicole Bussola:
Towards a scientific blockchain framework for reproducible data analysis. CoRR abs/1707.06552 (2017) - [i9]Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello:
Phylogenetic Convolutional Neural Networks in Metagenomics. CoRR abs/1709.02268 (2017) - [i8]Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello:
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders. CoRR abs/1709.05956 (2017) - [i7]Valerio Maggio
A multiobjective deep learning approach for predictive classification in Neuroblastoma. CoRR abs/1711.08198 (2017) - 2015
- [i6]Nastaran Mohammadian Rad
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism. CoRR abs/1511.01865 (2015) - [i5]Marco Cristoforetti, Marco Guerini, Giuseppe Jurman, Cesare Furlanello:
Community dynamics in connected time-dependent multilayer networks. CoRR abs/1511.03447 (2015) - 2014
- [i4]Gabriele Lami
Entropy Dynamics of Community Alignment in the Italian Parliament Time-Dependent Network. CoRR abs/1411.0827 (2014) - 2012
- [i3]Giuseppe Jurman, Roberto Visintainer, Samantha Riccadonna, Michele Filosi, Cesare Furlanello:
A glocal distance for network comparison. CoRR abs/1201.2931 (2012) - [i2]Davide Albanese, Roberto Visintainer, Stefano Merler, Samantha Riccadonna, Giuseppe Jurman, Cesare Furlanello:
mlpy: Machine Learning Python. CoRR abs/1202.6548 (2012) - 2002
- [i1]Bruno Caprile, Cesare Furlanello, Stefano Merler:
The Dynamics of AdaBoost Weights Tells You What's Hard to Classify. CoRR cs.LG/0201014 (2002)
Coauthor Index
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last updated on 2024-11-05 20:59 CET by the dblp team
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