208 citations · 301 across the 10 of their papers we have counts for
12 papers
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen +2
Efficient model selection for identifying a suitable pre-trained neural network to a downstream task is a fundamental yet challenging task in deep learning. Current practice requir…
Learning to Rank Learning Curves
Martin Wistuba, Tejaswini Pedapati
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many dif…
Learning Global Transparent Models Consistent with Local Contrastive Explanations
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam +1
There is a rich and growing literature on producing local contrastive/counterfactual explanations for black-box models (e.g. neural networks). In these methods, for an input, an ex…
How can AI Automate End-to-End Data Science?
Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz +9
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the r…
A Survey on Neural Architecture Search
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architectu…
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan +3
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model. In this…