27 citations · 83 across the 8 of their papers we have counts for
9 papers
On Evaluation of Vision Datasets and Models using Human Competency Frameworks
Rahul Ramachandran, Tejal Kulkarni, Charchit Sharma +2
Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evalua…
Automated Testing of AI Models
Swagatam Haldar, Deepak Vijaykeerthy, Diptikalyan Saha
The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we too…
A Framework for Learning Ante-hoc Explainable Models via Concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar +1
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation gen…
Verifying Individual Fairness in Machine Learning Models
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or un…
Exploring the Hyperparameter Landscape of Adversarial Robustness
Evelyn Duesterwald, Anupama Murthi, Ganesh Venkataraman +2
Adversarial training shows promise as an approach for training models that are robust towards adversarial perturbation. In this paper, we explore some of the practical challenges o…
An ADMM Based Framework for AutoML Pipeline Configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy +6
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervis…