Aditya V. Nori mainly investigates Theoretical computer science, Programming language, Data mining, Robustness and Artificial neural network. His work carried out in the field of Theoretical computer science brings together such families of science as Algorithm, Probabilistic logic, Mathematical proof and Operator. Aditya V. Nori has researched Probabilistic logic in several fields, including Estimation of distribution algorithm, Inference, Program specification and Scripting language.
The various areas that Aditya V. Nori examines in his Data mining study include Scalability, Overfitting, Linear programming, MNIST database and Debugging. His Robustness study improves the overall literature in Artificial intelligence. His Artificial neural network study incorporates themes from Domain, Scale-space segmentation and Transfer of learning.
Aditya V. Nori mainly focuses on Theoretical computer science, Artificial intelligence, Probabilistic logic, Program analysis and Algorithm. His research in Theoretical computer science intersects with topics in Sampling, Programming language, Path and Speedup. His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His Probabilistic logic research incorporates elements of Probability distribution, Markov chain Monte Carlo, Correctness, Inference and Bayesian inference. His Program analysis study combines topics in areas such as Range, Property, Solver and Symbolic execution. His Algorithm research includes elements of Soundness, Mathematical proof and Alternation.
Artificial intelligence, Artificial neural network, Machine learning, Inference and Implementation are his primary areas of study. The concepts of his Artificial intelligence study are interwoven with issues in Differential privacy and Causal model. His Artificial neural network research is multidisciplinary, incorporating perspectives in Probabilistic logic, Markov chain, Robustness and Pattern recognition.
His Probabilistic logic study combines topics from a wide range of disciplines, such as Abstract interpretation, Probability distribution, Correctness and Importance sampling. His work deals with themes such as Computation, Task and Sample size determination, which intersect with Inference. The study incorporates disciplines such as Theoretical computer science, Inductive synthesis and Overfitting in addition to Implementation.
His main research concerns Artificial intelligence, Artificial neural network, Computation, Pattern recognition and Backpropagation. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Abstract interpretation and Machine learning, Markov chain. Aditya V. Nori interconnects Probability distribution, Probabilistic logic, Correctness and Importance sampling in the investigation of issues within Abstract interpretation.
His studies in Markov chain integrate themes in fields like Semi-supervised learning, Graph, Cluster analysis and Feature vector. His Computation research includes themes of Decision tree, Class, Feature learning and Task. His Backpropagation study frequently links to adjacent areas such as Inference.
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Andrew D. Gordon;Thomas A. Henzinger;Aditya V. Nori;Sriram K. Rajamani.
international conference on software engineering (2014)
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe.
international conference information processing (2017)
HOLMES: Effective statistical debugging via efficient path profiling
Trishul M. Chilimbi;Ben Liblit;Krishna Mehra;Aditya V. Nori.
international conference on software engineering (2009)
SYNERGY: a new algorithm for property checking
Bhargav S. Gulavani;Thomas A. Henzinger;Yamini Kannan;Aditya V. Nori.
foundations of software engineering (2006)
Measuring Neural Net Robustness with Constraints
Osbert Bastani;Yani Ioannou;Leonidas Lampropoulos;Dimitrios Vytiniotis.
neural information processing systems (2016)
DeepMedic for Brain Tumor Segmentation
Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig.
international workshop on brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries (2016)
Compositional may-must program analysis: unleashing the power of alternation
Patrice Godefroid;Aditya V. Nori;Sriram K. Rajamani;Sai Deep Tetali.
symposium on principles of programming languages (2010)
Proofs from Tests
N E Beckman;A V Nori;S K Rajamani;R J Simmons.
IEEE Transactions on Software Engineering (2010)
Merlin: specification inference for explicit information flow problems
Benjamin Livshits;Aditya V. Nori;Sriram K. Rajamani;Anindya Banerjee.
programming language design and implementation (2009)
Proofs from tests
Nels E. Beckman;Aditya V. Nori;Sriram K. Rajamani;Robert J. Simmons.
international symposium on software testing and analysis (2008)
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