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Neelabh Madan

2 accepted papers

2024

Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction

ICLR 2024poster

Extreme Classification (XC) architectures, which utilize a massive One-vs-All (OvA) classifier layer at the output, have demonstrated remarkable performance on problems with large label sets. Nonetheless, these architectures falter on tail labels with few representative samples. This phenomenon has…

Cited by 7SourcePDFScholar
2022

A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration

CVPR 2022oral

Deep Neural Networks (DNNs) are known to make overconfident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art (SOTA) calibration techniques improve on the confidence of predicted labels alone, and leave the confidence of non-max classes (e.g. top-2, top-5)…

Cited by 60PDFcodeScholar