most citedOver-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

1 citations · 1 across the 1 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Regression-aware Continual Learning for Android Malware Detection

Daniele Ghiani, Daniele Angioni, Giorgio Piras +6

Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets…

cs.LG20261 cited

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

Srishti Gupta, Zhang Chen, Luca Demetrio +9

Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is consi…

cs.SD2026

Evaluation of Audio Language Models for Fairness, Safety, and Security

Ranya Aloufi, Srishti Gupta, Soumya Shaw +2

Audio large language models (ALLMs) have recently advanced spoken interaction by integrating speech processing with large language models. However, existing evaluations of fairness…

cs.LG2025

Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking

Srishti Gupta, Riccardo Balia, Daniele Angioni +7

Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields, fueled by increased computational resources, large-sca…

cs.CR2025

Chasing Shadows: Pitfalls in LLM Security Research

Jonathan Evertz, Niklas Risse, Nicolai Neuer +12

Large language models (LLMs) are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of re…

cs.LG2025

Efficiency vs. Fidelity: A Comparative Analysis of Diffusion Probabilistic Models and Flow Matching on Low-Resource Hardware

Srishti Gupta, Yashasvee Taiwade

Denoising Diffusion Probabilistic Models (DDPMs) have established a new state-of-the-art in generative image synthesis, yet their deployment is hindered by significant computationa…