no code implementations • EMNLP (insights) 2020 • Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta
Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.
no code implementations • 30 Apr 2020 • Ragav Venkatesan, Gurumurthy Swaminathan, Xiong Zhou, Anna Luo
We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
2 code implementations • 29 May 2019 • Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, Orchid Majumder
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
1 code implementation • 2 May 2017 • Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan, Baoxin Li
Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting.
no code implementations • 5 Apr 2017 • Ragav Venkatesan, Parag S. Chandakkar, Baoxin Li
All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication.
1 code implementation • 14 May 2016 • Ragav Venkatesan, Vijetha Gattupalli, Baoxin Li
It is curious that while the filters learned by these CNNs are related to the atomic structures of the images from which they are learnt, all datasets learn similar looking low-level filters.
1 code implementation • 27 Apr 2016 • Ragav Venkatesan, Baoxin Li
We studied various characteristics of such networks and found some interesting behaviors.
1 code implementation • IEEE International Conference on Computer Vision 2015 • Ragav Venkatesan, Parag Chandakkar, Baoxin Li
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances).
no code implementations • ICCV 2015 • Ragav Venkatesan, Parag Chandakkar, Baoxin Li
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances).