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Articles by Chethan Krishnamurthy
Computer Science > Machine Learning
[Submitted on 8 Jul 2026] Title:On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces View a PDF of the paper titled On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces, by Chethan Krishnamurthy Ramanaik and 3 other authors View PDF HTML (experimental) Abstract:Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry,...
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders
[Submitted on 6 May 2025] Title:ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders View a PDF of the paper titled ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders, by Chethan Krishnamurthy Ramanaik and 2 other authors View PDF HTML (experimental) Abstract:Despite the extensive use of deep autoencoders (AEs) in critical applications, their adversarial robustness remains relatively underexplored compared to classification models.
Red‐Emitting Pyrazole Azo Hydrazone and Its Metal Complexes for Photophysical Probing, Latent Fingerprints, Anti‐counterfeiting, and Biological Studies
Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description aoc7762-sup-0001-Supporting_Information.docxWord 2007 document , 412.2 KB Figure S1. 1H NMR spectra of compound (PAFT) Figure S2. IR spectra of PAFT and its metal complexes Figure S3. HRMS of fluorescent ligand (PAFT) Figure S4. HRMS of [Cu (PAFT)2] complex Figure S5. HRMS of [Ru(PAFT)Cl3] H2O complex Figure S6.
[2407.03864] Adversarial Robustness of VAEs across Intersectional Subgroups
[Submitted on 4 Jul 2024] Title:Adversarial Robustness of VAEs across Intersectional Subgroups View a PDF of the paper titled Adversarial Robustness of VAEs across Intersectional Subgroups, by Chethan Krishnamurthy Ramanaik and 2 other authors View PDF HTML (experimental) Abstract:Despite advancements in Autoencoders (AEs) for tasks like dimensionality reduction, representation learning and data generation, they remain vulnerable to adversarial attacks.
[2309.08228] Ensuring Toplogical Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss--Legendre nodes
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