no code implementations • 16 Oct 2023 • Anirudh Som, Karan Sikka, Helen Gent, Ajay Divakaran, Andreas Kathol, Dimitra Vergyri
Paraphrasing of offensive content is a better alternative to content removal and helps improve civility in a communication environment.
1 code implementation • 1 Jan 2022 • Eun Som Jeon, Anirudh Som, Ankita Shukla, Kristina Hasanaj, Matthew P. Buman, Pavan Turaga
In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis.
no code implementations • 17 Jun 2021 • Anirudh Som, Sujeong Kim, Bladimir Lopez-Prado, Svati Dhamija, Nonye Alozie, Amir Tamrakar
Collaboration is identified as a required and necessary skill for students to be successful in the fields of Science, Technology, Engineering and Mathematics (STEM).
no code implementations • 2 Feb 2021 • Ella Y. Wang, Anirudh Som, Ankita Shukla, Hongjun Choi, Pavan Turaga
In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings.
no code implementations • 22 Sep 2020 • Hongjun Choi, Anirudh Som, Pavan Turaga
Standard deep learning models that employ the categorical cross-entropy loss are known to perform well at image classification tasks.
no code implementations • 13 Jul 2020 • Anirudh Som, Sujeong Kim, Bladimir Lopez-Prado, Svati Dhamija, Nonye Alozie, Amir Tamrakar
K-12 classrooms consistently integrate collaboration as part of their learning experiences.
no code implementations • 6 May 2020 • Anirudh Som, Narayanan Krishnamurthi, Matthew Buman, Pavan Turaga
We show that the features extracted for the target dataset can be used to train an effective classification model.
1 code implementation • 21 Apr 2020 • Hongjun Choi, Anirudh Som, Pavan Turaga
We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM.
1 code implementation • 15 Apr 2020 • Afra Nawar, Farhan Rahman, Narayanan Krishnamurthi, Anirudh Som, Pavan Turaga
In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment.
1 code implementation • 5 Jun 2019 • Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy, Matthew Buman, Pavan Turaga
To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data.
1 code implementation • ECCV 2018 • Anirudh Som, Kowshik Thopalli, Karthikeyan Natesan Ramamurthy, Vinay Venkataraman, Ankita Shukla, Pavan Turaga
However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.