494 citations · 676 across the 8 of their papers we have counts for
9 papers
Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation
Goutham Ramakrishnan, Megha Sharma
Small language models (SLMs) are attractive for agentic deployment due to low latency, reduced cost, and on-device privacy, yet they struggle with tool-use tasks where training dat…
Towards Robustness to Label Noise in Text Classification via Noise Modeling
Siddhant Garg, Goutham Ramakrishnan, Varun Thumbe
Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to…
Towards Compliant Data Management Systems for Healthcare ML
Goutham Ramakrishnan, Aditya Nori, Hannah Murfet +1
The increasing popularity of machine learning approaches and the rising awareness of data protection and data privacy presents an opportunity to build truly secure and trustworthy…
Backdoors in Neural Models of Source Code
Goutham Ramakrishnan, Aws Albarghouthi
Deep neural networks are vulnerable to a range of adversaries. A particularly pernicious class of vulnerabilities are backdoors, where model predictions diverge in the presence of…
Advances in Quantum Deep Learning: An Overview
Siddhant Garg, Goutham Ramakrishnan
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing…
BAE: BERT-based Adversarial Examples for Text Classification
Siddhant Garg, Goutham Ramakrishnan
Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Rec…
Semantic Robustness of Models of Source Code
Goutham Ramakrishnan, Jordan Henkel, Zi Wang +3
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions. We study this problem for models of source code, where…
Synthesizing Action Sequences for Modifying Model Decisions
Goutham Ramakrishnan, Yun Chan Lee, Aws Albarghouthi
When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably c…
Fast GPU-Enabled Color Normalization for Digital Pathology
Goutham Ramakrishnan, Deepak Anand, Amit Sethi
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the sever…