no code implementations • 28 Feb 2024 • Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurelie Lozano, Payel Das, Georgios Kollias
Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks.
1 code implementation • 2 Feb 2024 • Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models.
no code implementations • 9 Jul 2023 • Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
no code implementations • 30 Jan 2023 • KrishnaTeja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh Iyer
Training deep networks and tuning hyperparameters on large datasets is computationally intensive.
no code implementations • 11 Jan 2022 • Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
no code implementations • 17 Dec 2021 • Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
no code implementations • NeurIPS 2021 • Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
We experiment on nonlinear synthetic functions and are able to accurately model as well as estimate feature attributions and even higher order terms in some cases, which is a testament to the representational power as well as interpretability of such architectures.
no code implementations • 14 Sep 2021 • Amit Dhurandhar, Tejaswini Pedapati
In this paper, we propose a meta-approach where we transfer information from the complex model to the simple model by dynamically selecting and/or constructing a sequence of intermediate models of decreasing complexity that are less intricate than the original complex model.
Explainable artificial intelligence Knowledge Distillation +2
no code implementations • ICML 2020 • 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 different model configurations.
no code implementations • NeurIPS 2020 • Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Based on a key insight we propose a novel method where we create custom boolean features from sparse local contrastive explanations of the black-box model and then train a globally transparent model on just these, and showcase empirically that such models have higher local consistency compared with other known strategies, while still being close in performance to models that are trained with access to the original data.
no code implementations • 22 Oct 2019 • Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander Gray
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
no code implementations • 31 May 2019 • Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
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.
no code implementations • 4 May 2019 • 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 architecture search.
no code implementations • 8 Mar 2019 • Martin Wistuba, Tejaswini Pedapati
First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets.
no code implementations • 17 Jan 2019 • Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
no code implementations • 30 Nov 2018 • Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.
no code implementations • 16 Nov 2017 • Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels
In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.