23 citations · 26 across the 2 of their papers we have counts for
6 papers
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania +1
There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors.…
Simple Unsupervised Multi-Object Tracking
Shyamgopal Karthik, Ameya Prabhu, Vineet Gandhi
Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the n…
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran +2
For an unknown (new) classification dataset, choosing an appropriate deep learning architecture is often a recursive, time-taking, and laborious process. In this research, we propo…
Sampling Bias in Deep Active Classification: An Empirical Study
Ameya Prabhu, Charles Dognin, Maneesh Singh
The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets. Identifying smaller representative data samples w…
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory
Ameya Prabhu, Vishal Batchu, Rohit Gajawada +2
Binarization is an extreme network compression approach that provides large computational speedups along with energy and memory savings, albeit at significant accuracy costs. We in…
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala +2
Deep neural networks are highly effective at a range of computational tasks. However, they tend to be computationally expensive, especially in vision-related problems, and also hav…