Mohak Shah

According to our database1, Mohak Shah authored at least 39 papers between 2003 and 2019.

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Bibliography

2019
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.
CoRR, 2019

On-Device Machine Learning: An Algorithms and Learning Theory Perspective.
CoRR, 2019

Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize.
CoRR, 2019

Robust Neural Network Training using Periodic Sampling over Model Weights.
CoRR, 2019

2018
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving.
CoRR, 2018

Multiclass Universum SVM.
CoRR, 2018

Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling.
CoRR, 2018

Effective Building Block Design for Deep Convolutional Neural Networks using Search.
CoRR, 2018

Distributed NoSQL Data Stores: Performance Analysis and a Case Study.
Proceedings of the IEEE International Conference on Big Data, 2018

2017
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing.
CoRR, 2017

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

2016
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification.
CoRR, 2016

Universum Learning for Multiclass SVM.
CoRR, 2016

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

2015
Big Data and the Internet of Things.
CoRR, 2015

Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning.
CoRR, 2015

ADMM based scalable machine learning on Spark.
Proceedings of the 2015 IEEE International Conference on Big Data, 2015

2012
Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields.
IEEE Trans. Med. Imaging, 2012

2011
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

2010
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
CoRR, 2010

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

2008
Risk Bounds for Randomized Sample Compressed Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Sample compression bounds for decision trees.
Proceedings of the Machine Learning, 2007

2006
Process-Specific Information for Learning Electronic Negotiation Outcomes.
Fundam. Inform., 2006

2005
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

2004
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

2003
The Set Covering Machine with Data-Dependent Half-Spaces.
Proceedings of the Machine Learning, 2003


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