default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMohit Prabhushankar
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[j6]Ryan Benkert
, Mohit Prabhushankar, Ghassan AlRegib, Armin Pacharmi, Enrique Corona:
Gaussian Switch Sampling: A Second-Order Approach to Active Learning. IEEE Trans. Artif. Intell. 5(1): 38-50 (2024)- [j5]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Effective Data Selection for Seismic Interpretation Through Disagreement. IEEE Trans. Geosci. Remote. Sens. 62: 1-12 (2024) - [c17]Johannes Schneider, Mohit Prabhushankar:
Understanding and Leveraging the Learning Phases of Neural Networks. AAAI 2024: 14886-14893 - [c16]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Transitional Uncertainty with Layered Intermediate Predictions. ICML 2024 - [d2]Ghazal Kaviani, Yavuz Yarici, Mohit Prabhushankar, Ghassan AlRegib, Mashhour Solh, Ameya Patil:
DARai: Daily Activity Recordings for AI and ML aplications. IEEE DataPort, 2024
- [i38]Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib:
Perceptual Quality-based Model Training under Annotator Label Uncertainty. CoRR abs/2403.10190 (2024) - [i37]Mohit Prabhushankar, Ghassan AlRegib:
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks. CoRR abs/2405.13758 (2024) - [i36]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Transitional Uncertainty with Layered Intermediate Predictions. CoRR abs/2405.17494 (2024) - [i35]Mohit Prabhushankar, Ghassan AlRegib:
VOICE: Variance of Induced Contrastive Explanations to quantify Uncertainty in Neural Network Interpretability. CoRR abs/2406.00573 (2024) - [i34]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Effective Data Selection for Seismic Interpretation through Disagreement. CoRR abs/2406.05149 (2024) - [i33]Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar, Ghassan AlRegib:
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss. CoRR abs/2406.06848 (2024) - [i32]Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib:
Explaining Representation Learning with Perceptual Components. CoRR abs/2406.06930 (2024) - [i31]Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegib, Mohamed Deriche:
Are Objective Explanatory Evaluation metrics Trustworthy? An Adversarial Analysis. CoRR abs/2406.07820 (2024) - [i30]Efe Ozturk, Mohit Prabhushankar, Ghassan AlRegib:
Intelligent Multi-View Test Time Augmentation. CoRR abs/2406.08593 (2024) - [i29]Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada, Yavuz Yarici, Chen Zhou, Mohammad Alotaibi, Ghassan AlRegib, Ahmad Mustafa, Yusufjon Kumakov:
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults. CoRR abs/2408.11185 (2024) - 2023
- [b1]Mohit Prabhushankar:
Contrastive Reasoning in Neural Networks. Georgia Institute of Technology, Atlanta, GA, USA, 2023 - [j4]Jinsol Lee, Charlie Lehman, Mohit Prabhushankar, Ghassan AlRegib:
Probing the Purview of Neural Networks via Gradient Analysis. IEEE Access 11: 32716-32732 (2023) - [j3]Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar, Ghassan AlRegib, Charles C. Wykoff:
Clinically Labeled Contrastive Learning for OCT Biomarker Classification. IEEE J. Biomed. Health Informatics 27(9): 4397-4408 (2023) - [c15]Zoe Fowler, Kiran Premdat Kokilepersaud, Mohit Prabhushankar, Ghassan Alregib:
Clinical Trial Active Learning. BCB 2023: 6:1-6:10 - [c14]Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert, Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib, Enrique Corona, Kunjan Singh, Mostafa Parchami:
FOCAL: A Cost-Aware Video Dataset for Active Learning. IEEE Big Data 2023: 1269-1278 - [i28]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data. CoRR abs/2301.05106 (2023) - [i27]Mohit Prabhushankar, Ghassan AlRegib:
Stochastic Surprisal: An inferential measurement of Free Energy in Neural Networks. CoRR abs/2302.05776 (2023) - [i26]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib, Armin Pacharmi, Enrique Corona:
Gaussian Switch Sampling: A Second Order Approach to Active Learning. CoRR abs/2302.12018 (2023) - [i25]Jinsol Lee, Charlie Lehman, Mohit Prabhushankar, Ghassan AlRegib:
Probing the Purview of Neural Networks via Gradient Analysis. CoRR abs/2304.02834 (2023) - [i24]Kiran Kokilepersaud, Mohit Prabhushankar, Yavuz Yarici, Ghassan AlRegib, Armin Parchami:
Exploiting the Distortion-Semantic Interaction in Fisheye Data. CoRR abs/2305.00079 (2023) - [i23]Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar, Ghassan AlRegib, Charles C. Wykoff:
Clinically Labeled Contrastive Learning for OCT Biomarker Classification. CoRR abs/2305.15154 (2023) - [i22]Zoe Fowler, Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib:
Clinical Trial Active Learning. CoRR abs/2307.11209 (2023) - [i21]Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert, Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib, Enrique Corona, Kunjan Singh, Mostafa Parchami:
FOCAL: A Cost-Aware Video Dataset for Active Learning. CoRR abs/2311.10591 (2023) - [i20]Johannes Schneider, Mohit Prabhushankar:
Understanding and Leveraging the Learning Phases of Neural Networks. CoRR abs/2312.06887 (2023) - 2022
- [j2]Ghassan AlRegib, Mohit Prabhushankar:
Explanatory Paradigms in Neural Networks: Towards relevant and contextual explanations. IEEE Signal Process. Mag. 39(4): 59-72 (2022) - [c13]Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib:
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data. ICIP 2022: 2196-2200 - [c12]Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib, Stephanie Trejo Corona, Charles C. Wykoff:
Gradient-Based Severity Labeling for Biomarker Classification in Oct. ICIP 2022: 3416-3420 - [c11]Mohit Prabhushankar, Ghassan AlRegib:
Introspective Learning : A Two-Stage approach for Inference in Neural Networks. NeurIPS 2022 - [c10]Mohit Prabhushankar, Kiran Kokilepersaud, Yash-Yee Logan, Stephanie Trejo Corona, Ghassan AlRegib, Charles C. Wykoff:
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics. NeurIPS 2022 - [i19]Ghassan AlRegib, Mohit Prabhushankar:
Explanatory Paradigms in Neural Networks. CoRR abs/2202.11838 (2022) - [i18]Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib:
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation. CoRR abs/2206.08158 (2022) - [i17]Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib:
Gradient-Based Adversarial and Out-of-Distribution Detection. CoRR abs/2206.08255 (2022) - [i16]Mohit Prabhushankar, Ghassan AlRegib:
Introspective Learning : A Two-Stage Approach for Inference in Neural Networks. CoRR abs/2209.08425 (2022) - [i15]Mohit Prabhushankar, Kiran Kokilepersaud, Yash-Yee Logan, Stephanie Trejo Corona, Ghassan AlRegib, Charles C. Wykoff:
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics. CoRR abs/2209.11195 (2022) - [i14]Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib:
Clinical Contrastive Learning for Biomarker Detection. CoRR abs/2211.05092 (2022) - [i13]Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib:
On the Ramifications of Human Label Uncertainty. CoRR abs/2211.05871 (2022) - 2021
- [c9]Mohit Prabhushankar, Ghassan AlRegib:
Extracting Causal Visual Features For Limited Label Classification. ICIP 2021: 3697-3701 - [d1]Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib:
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition. IEEE DataPort, 2021 - [i12]Mohit Prabhushankar, Ghassan AlRegib:
Extracting Causal Visual Features for Limited label Classification. CoRR abs/2103.12322 (2021) - [i11]Mohit Prabhushankar, Ghassan AlRegib:
Contrastive Reasoning in Neural Networks. CoRR abs/2103.12329 (2021) - 2020
- [c8]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Backpropagated Gradient Representations for Anomaly Detection. ECCV (21) 2020: 206-226 - [c7]Yutong Sun, Mohit Prabhushankar, Ghassan AlRegib:
Implicit Saliency In Deep Neural Networks. ICIP 2020: 2915-2919 - [c6]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Novelty Detection Through Model-Based Characterization of Neural Networks. ICIP 2020: 3179-3183 - [c5]Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib:
Contrastive Explanations In Neural Networks. ICIP 2020: 3289-3293 - [i10]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Backpropagated Gradient Representations for Anomaly Detection. CoRR abs/2007.09507 (2020) - [i9]Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib:
Contrastive Explanations in Neural Networks. CoRR abs/2008.00178 (2020) - [i8]Yutong Sun, Mohit Prabhushankar, Ghassan AlRegib:
Implicit Saliency in Deep Neural Networks. CoRR abs/2008.01874 (2020) - [i7]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Novelty Detection Through Model-Based Characterization of Neural Networks. CoRR abs/2008.06094 (2020)
2010 – 2019
- 2019
- [c4]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Distorted Representation Space Characterization Through Backpropagated Gradients. ICIP 2019: 2651-2655 - [i6]Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib:
Semantically Interpretable and Controllable Filter Sets. CoRR abs/1902.06334 (2019) - [i5]Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Distorted Representation Space Characterization Through Backpropagated Gradients. CoRR abs/1908.09998 (2019) - 2018
- [c3]Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib:
Semantically Interpretable and Controllable Filter Sets. ICIP 2018: 1053-1057 - [i4]Dogancan Temel, Mohit Prabhushankar, Ghassan AlRegib:
UNIQUE: Unsupervised Image Quality Estimation. CoRR abs/1810.06631 (2018) - [i3]Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework. CoRR abs/1811.08927 (2018) - [i2]Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation. CoRR abs/1811.08947 (2018) - 2017
- [c2]Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
Generating adaptive and robust filter sets using an unsupervised learning framework. ICIP 2017: 3041-3045 - [c1]Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib:
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation. IQSP 2017: 30-35 - [i1]Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib:
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition. CoRR abs/1712.02463 (2017) - 2016
- [j1]Dogancan Temel, Mohit Prabhushankar, Ghassan AlRegib:
UNIQUE: Unsupervised Image Quality Estimation. IEEE Signal Process. Lett. 23(10): 1414-1418 (2016)
Coauthor Index
aka: Kiran Premdat Kokilepersaud
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-04 21:04 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
