According to our database1, Mohak Shah authored at least 39 papers between 2003 and 2019.
Legend:Book In proceedings Article PhD thesis Other
Benchmarking large-scale data management for Internet of Things.
The Journal of Supercomputing, 2019
Concept drift detection and adaptation with hierarchical hypothesis testing.
J. Franklin Institute, 2019
Auptimizer - an Extensible, Open-Source Framework for Hyperparameter Tuning.
On-Device Machine Learning: An Algorithms and Learning Theory Perspective.
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize.
Robust Neural Network Training using Periodic Sampling over Model Weights.
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving.
Multiclass Universum SVM.
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling.
Effective Building Block Design for Deep Convolutional Neural Networks using Search.
Distributed NoSQL Data Stores: Performance Analysis and a Case Study.
Proceedings of the IEEE International Conference on Big Data, 2018
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing.
A clustering-based rule-mining approach for monitoring long-term energy use and understanding system behavior.
Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, 2017
Structured Causal Inference for Rare Events: An Industrial Application to Analyze Heating-Cooling Device Failure.
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification.
Universum Learning for Multiclass SVM.
Can Software Project Maturity Be Accurately Predicted Using Internal Source Code Metrics?
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2016
An architecture for the deployment of statistical models for the big data era.
Proceedings of the 2016 IEEE International Conference on Big Data, 2016
Big Data and the Internet of Things.
Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning.
ADMM based scalable machine learning on Spark.
Proceedings of the 2015 IEEE International Conference on Big Data, 2015
Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields.
IEEE Trans. Med. Imaging, 2012
A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments.
IEEE/ACM Trans. Comput. Biology Bioinform., 2011
Evaluating intensity normalization on MRIs of human brain with multiple sclerosis.
Medical Image Analysis, 2011
Generalized Agreement Statistics over Fixed Group of Experts.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011
Exploiting text mining techniques in the analysis of execution traces.
Proceedings of the IEEE 27th International Conference on Software Maintenance, 2011
Relaxed Exponential Kernels for Unsupervised Learning.
Proceedings of the Pattern Recognition - 33rd DAGM Symposium, Frankfurt/Main, Germany, August 31, 2011
Learning the set covering machine by bound minimization and margin-sparsity trade-off.
Machine Learning, 2010
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis.
Proceedings of the Machine Learning in Medical Imaging, First International Workshop, 2010
Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2010
Risk Bounds for Randomized Sample Compressed Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
Sample compression bounds for decision trees.
Proceedings of the Machine Learning, 2007
Process-Specific Information for Learning Electronic Negotiation Outcomes.
Fundam. Inform., 2006
A PAC-Bayes approach to the Set Covering Machine.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005
Margin-Sparsity Trade-Off for the Set Covering Machine.
Proceedings of the Machine Learning: ECML 2005, 2005
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
The Set Covering Machine with Data-Dependent Half-Spaces.
Proceedings of the Machine Learning, 2003