10 citations · 11 across the 8 of their papers we have counts for
3 papers · 1 filter
Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectors
Tejaswi Kasarla, Max van Spengler, Pascal Mettes
Out-of-distribution recognition forms an important and well-studied problem in deep learning, with the goal to filter out samples that do not belong to the distribution on which a…
Maximally Separated Active Learning
Tejaswi Kasarla, Abhishek Jha, Faye Tervoort +2
Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling o…
Maximum Class Separation as Inductive Bias in One Matrix
Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler +3
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not…