no code implementations • 8 Sep 2023 • Aroof Aimen, Arsh Verma, Makarand Tapaswi, Narayanan C. Krishnan
Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation.
no code implementations • 20 Jun 2021 • Aroof Aimen, Sahil Sidheekh, Narayanan C. Krishnan
The popular approaches for ML either learn a generalizable initial model or a generic parametric optimizer through episodic training.
1 code implementation • 11 May 2021 • Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and influencing GAN training.
no code implementations • 21 Jan 2021 • Aroof Aimen, Sahil Sidheekh, Vineet Madan, Narayanan C. Krishnan
Our results show a quick degradation in the performance of initialization strategies for ML (MAML, TAML, and MetaSGD), while surprisingly, approaches that use an optimization strategy (MetaLSTM) perform significantly better.
1 code implementation • 12 Dec 2020 • Sahil Sidheekh, Aroof Aimen, Vineet Madan, Narayanan C. Krishnan
Further, we show that our estimate, with its ability to identify model convergence/divergence, is a potential performance measure that can be used to tune the hyperparameters of a GAN.