9 citations · 18 across the 11 of their papers we have counts for
7 papers
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
Rakshith Subramanyam, Mark Heimann, Jayram Thathachar +2
Model agnostic meta-learning algorithms aim to infer priors from several observed tasks that can then be used to adapt to a new task with few examples. Given the inherent diversity…
Single Model Uncertainty Estimation via Stochastic Data Centering
Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy +1
We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a str…
Out of Distribution Detection via Neural Network Anchoring
Rushil Anirudh, Jayaraman J. Thiagarajan
Our goal in this paper is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection. Heteroscedasticity here refers to the fa…
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series
Rushil Anirudh, Jayaraman J. Thiagarajan, Irene Kim +1
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ…
Diversity Promoting Online Sampling for Streaming Video Summarization
Rushil Anirudh, Ahnaf Masroor, Pavan Turaga
Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use…
Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data
Rushil Anirudh, Pavan Turaga
In this paper, we study the problem of `test-driving' a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement…