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Evaluating Inexact Unlearning Requires Revisiting Forgetting



Shashwat Goel , Ameya Prabhu , Ponnurangam Kumaraguru


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No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks



Shyamgopal Karthik , Ameya Prabhu , Puneet K. Dokania , Vineet Gandhi


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Simple Unsupervised Multi-Object Tracking



Shyamgopal Karthik , Ameya Prabhu , Vineet Gandhi


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"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets



Ameya Prabhu , Riddhiman Dasgupta , Anush Sankaran , Srikanth Tamilselvam , Senthil Mani

* NeurIPS 2019, New in ML Group 

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Sampling Bias in Deep Active Classification: An Empirical Study



Ameya Prabhu , Charles Dognin , Maneesh Singh

* Accepted at EMNLP 2019 

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Deep Expander Networks: Efficient Deep Networks from Graph Theory



Ameya Prabhu , Girish Varma , Anoop Namboodiri

* ECCV'18 

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Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory



Ameya Prabhu , Vishal Batchu , Rohit Gajawada , Sri Aurobindo Munagala , Anoop Namboodiri

* Accepted in WACV'18 (Oral) 

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Distribution-Aware Binarization of Neural Networks for Sketch Recognition



Ameya Prabhu , Vishal Batchu , Sri Aurobindo Munagala , Rohit Gajawada , Anoop Namboodiri

* Accepted at WACV '18 (Oral) 

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Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity



Vinayak Athavale , Shreenivas Bharadwaj , Monik Pamecha , Ameya Prabhu , Manish Shrivastava

* 7 pages 

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Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text



Ameya Prabhu , Aditya Joshi , Manish Shrivastava , Vasudeva Varma

* Accepted paper at COLING 2016 

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