activity
20172024
most citedTowards Crafting Text Adversarial Samples

166 citations · 167 across the 2 of their papers we have counts for

collaborators

6 papers

cs.SE20241 cited

ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement

Oishik Chatterjee, Pooja Aggarwal, Suranjana Samanta +8

In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud application…

cs.CL2020

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection

Debanjana Kar, Mohit Bhardwaj, Suranjana Samanta +1

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence o…

cs.CL2020

Meta-Context Transformers for Domain-Specific Response Generation

Debanjana Kar, Suranjana Samanta, Amar Prakash Azad

Despite the tremendous success of neural dialogue models in recent years, it suffers a lack of relevance, diversity, and some times coherence in generated responses. Lately, transf…

cs.CL2020

Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers' Conversations in Hybrid Cloud Operations

Suranjana Samanta, Ajay Gupta, Prateeti Mohapatra +1

Conversational channels are changing the landscape of hybrid cloud service management. These channels are becoming important avenues for Site Reliability Engineers (SREs) %Subject…

cs.LG2020

Addressing target shift in zero-shot learning using grouped adversarial learning

Saneem Ahmed Chemmengath, Soumava Paul, Samarth Bharadwaj +2

Zero-shot learning (ZSL) algorithms typically work by exploiting attribute correlations to be able to make predictions in unseen classes. However, these correlations do not remain…

cs.LG2017166 cited

Towards Crafting Text Adversarial Samples

Suranjana Samanta, Sameep Mehta

Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial sam…