arXiv subjects

Shreyash Arya

Publications and source records attributed to Shreyash Arya.

4 recordsLinked to original sources

arXiv2026

Faithful, Interpretable Chest X-ray Diagnosis with Artifact-free B-cos Networks

Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical image analysis. B-cos networks modify the parameterization of convolutional and classification layers to measure class evidence via feature-weight alignment, enabling built-in, class-specific contribution maps without post-hoc explanations. While maintaining diagnostic performance competitive with state-of-the-art DNNs, standard B-cos networks exhibit severe aliasing artifacts in their explanation maps, rendering them unsuitable for clinical use, where clarity is essential. In this work, we address this limitation by introducing anti-aliasing strategies using ASAP and BlurPool (BP) to significantly improve explanation quality. Our experiments on chest X-ray datasets demonstrate that the modified $\text{B-cos}_\mathrm{ASAP}$ and $\text{B-cos}_\mathrm{BP}$ preserve strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings. Code is available at: https://github.com/shrebox/Artifact-free-B-cos-Networks.

cs.CV↗

arXiv2025

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable

B-cos Networks have been shown to be effective for obtaining highly human interpretable explanations of model decisions by architecturally enforcing stronger alignment between inputs and weight. B-cos variants of convolutional networks (CNNs) and vision transformers (ViTs), which primarily replace linear layers with B-cos transformations, perform competitively to their respective standard variants while also yielding explanations that are faithful by design. However, it has so far been necessary to train these models from scratch, which is increasingly infeasible in the era of large, pre-trained foundation models. In this work, inspired by the architectural similarities in standard DNNs and B-cos networks, we propose 'B-cosification', a novel approach to transform existing pre-trained models to become inherently interpretable. We perform a thorough study of design choices to perform this conversion, both for convolutional neural networks and vision transformers. We find that B-cosification can yield models that are on par with B-cos models trained from scratch in terms of interpretability, while often outperforming them in terms of classification performance at a fraction of the training cost. Subsequently, we apply B-cosification to a pretrained CLIP model, and show that, even with limited data and compute cost, we obtain a B-cosified version that is highly interpretable and competitive on zero shot performance across a variety of datasets. We release our code and pre-trained model weights at https://github.com/shrebox/B-cosification.

cs.CV↗

arXiv2021

The Influence of Social Networks on Human Society

This report gives a brief overview of the origin of social networks and their most popular manifestation in the modern era - the Online Social Networks (OSNs) or social media. It further discusses the positive and negative implications of OSNs on human society. The coupling of Data Science and social media (social media mining) is then put forward as a powerful tool to overcome the current challenges and pave the path for futuristic advancements

cs.SI↗

arXiv2020

Effect of lockdown interventions to control the COVID-19 epidemic in India

The pandemic caused by the novel Coronavirus SARS-CoV2 has been responsible for life threatening health complications, and extreme pressure on healthcare systems. While preventive and definite curative medical interventions are yet to arrive, Non-Pharmaceutical Interventions (NPIs) like physical isolation, quarantine and drastic social measures imposed by governing agencies are effective in arresting the spread of infections in a population. In densely populated countries like India, lockdown interventions are partially effective due to social and administrative complexities. Using detailed demographic data, we present an agent based model to imitate the behavior of the population and its mobility features, even under intervention. We demonstrate the effectiveness of contact tracing policies and how our model efficiently relates to empirical findings on testing efficiency. We also present various lockdown intervention strategies for mitigation - using the bare number of infections, the effective reproduction rate, as well as using reinforcement learning. Our analysis can help assess the socio-economic consequences of such interventions, and provide useful ideas and insights to policy makers for better decision making.

physics.soc-ph↗