Sahil Sidheekh

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Narayanan Chatapuram KrishnanIndian Institute of Technology PalakkadVerified email at iitpkd.ac.in
Aroof AimenIIT Ropar | IIT Indore | University of Wisconsin MadisonVerified email at iitrpr.ac.in
Partha Sharathi DuttaAssociate Professor, Department of Mathematics, Indian Institute of Technology RoparVerified email at iitrpr.ac.in
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
Christopher F ClementsSenior Lecturer, University of BristolVerified email at bristol.ac.uk
Smita DebIIT ROPARVerified email at iitrpr.ac.in
VINEET MADANIndian Institute of Technology RoparVerified email at iitrpr.ac.in
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Maneesh SinghAI R&D. Indic aiDias. Spector.aiVerified email at ieee.org
Radu BalanProfessor of Applied Mathematics, University of MarylandVerified email at math.umd.edu
Tushar JainNYU, Verisk, AmazonVerified email at nyu.edu
Christopher Barton DockUniversity of MarylandVerified email at umd.edu
Bharat LadrechaSoftware Engineer, JioSaavnVerified email at jiosaavn.com
Saurabh MathurUniversity of Texas at DallasVerified email at utdallas.edu
Hansin AhujaGoogleVerified email at google.com
Duncan A O'BrienUniversity of BristolVerified email at bristol.ac.uk
Vinay P. NamboodiriDepartment of Computer Science, University of BathVerified email at bath.ac.uk
Avideep MukherjeeIndian Institute of Technology KanpurVerified email at cse.iitk.ac.in
Badri Narayana PatroSenior Research Scientist @Microsoft, Prev: Google, Samsung, Harman| @KUL, IIITH, IITK, IITB.Verified email at microsoft.com
Athresh KaranamUniversity of Texas at DallasVerified email at utdallas.edu

Sahil Sidheekh

Ph.D. Student - The University of Texas at Dallas. Ex-Verisk AI, Ex-IIT Ropar

Verified email at utdallas.edu - Homepage

Generative Models Meta-learning Exact Inference Tractable Probabilistic Models


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Machine learning methods trained on simple models can predict critical transitions in complex natural systems S Deb, S Sidheekh, CF Clements, NC Krishnan, PS Dutta Royal Society Open Science 9 (2), 211475, 2022	26	2022
On Characterizing GAN Convergence Through Proximal Duality Gap S Sidheekh, A Aimen, NC Krishnan The 38th International Conference on Machine Learning (ICML), PMLR 139, 2021, 2021	8	2021
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference S Sidheekh, K Kersting, S Natarajan Uncertainty in Artificial Intelligence, 1964-1973, 2023	6	2023
Stress Testing of Meta-learning Approaches for Few-shot Learning A Aimen, S Sidheekh, V Madan, NC Krishnan AAAI 2021 Meta-learning Workshop and Co-hosted Challenge, 2021	6	2021
On Duality Gap as a Measure for Monitoring GAN Training S Sidheekh, A Aimen, V Madan, NC Krishnan International Joint Conference on Neural Networks, IJCNN 2021, 2020	6	2020
VQ-Flows: Vector Quantized Local Normalizing Flows S Sidheekh, CB Dock, T Jain, R Balan, MK Singh The 38th Conference on Uncertainty in Artificial Intelligence (UAI'22), 2022	5	2022
Task Attended Meta-Learning for Few-Shot Learning A Aimen, S Sidheekh, NC Krishnan NeurIPS 2021 Workshop on Meta-Learning, 2021	5	2021
EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models DA O'Brien, S Deb, S Sidheekh, NC Krishnan, P Sharathi Dutta, ... Ecography 2023 (10), e06674, 2023	4*	2023
Leveraging task variability in meta-learning A Aimen, B Ladrecha, S Sidheekh, NC Krishnan SN Computer Science 4 (5), 539, 2023	4	2023
Scale invariant fast PHT based copy-move forgery detection A Aimen, A Kaur, S Sidheekh 2020 11th International Conference on Computing, Communication and …, 2020	2	2020
Adaptation: Blessing or Curse for Higher-way Meta-learning A Aimen, S Sidheekh, B Ladrecha, H Ahuja, NC Krishnan IEEE Transactions on Artificial Intelligence, 2023	1	2023
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits S Sidheekh, P Tenali, S Mathur, E Blasch, K Kersting, S Natarajan arXiv preprint arXiv:2403.03281, 2024		2024
Building Expressive and Tractable Probabilistic Generative Models: A Review S Sidheekh, S Natarajan arXiv preprint arXiv:2402.00759, 2024		2024
Deep Tractable Probabilistic Models S Sidheekh, S Mathur, A Karanam, S Natarajan Proceedings of the 7th Joint International Conference on Data Science …, 2024		2024
Credibility-aware Reliable Multi-Modal Fusion Using Probabilistic Circuits S Mathur, S Sidheekh, P Tenali, E Blasch, K Kersting, S Natarajan		2024
Bayesian Learning of Probabilistic Circuits with Domain Constraints A Karanam, S Mathur, S Sidheekh, S Natarajan The 6th Workshop on Tractable Probabilistic Modeling, 2023		2023
Meta-Learning Initialization vs Optimizer: Beyond 20 Ways Few-Shot Learning A Aimen, S Sidheekh, H Ahuja, NC Krishnan Available at SSRN 4266883, 2022		2022
Attentive Contractive Flow: Improved Contractive Flows with Lipschitz-constrained Self-Attention. A Mukherjee, BN Patro, S Sidheekh, M Singh, VP Namboodiri CoRR, 2021		2021
Learning Neural Networks on SVD Boosted Latent Spaces for Semantic Classification S Sidheekh Computing Research Repository, ArXiv, 2021		2021
VQ-Flows: Vector Quantized Local Normalizing Flows Supplementary Material S Sidheekh, CB Dock, T Jain, R Balan, MK Singh

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