no code implementations • 19 Dec 2021 • Martin Ferianc, Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Quentin Cappart
Neural networks (NNs) are making a large impact both on research and industry.
no code implementations • 11 Jan 2021 • Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Yasser Idris, Davis Sawyer, MohammadHossein AskariHemmat, Ghouthi Boukli Hacene
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers.
1 code implementation • 20 May 2020 • Naveen Panwar, Tarun Tater, Anush Sankaran, Senthil Mani
Existing deep learning approaches for learning visual features tend to overlearn and extract more information than what is required for the task at hand.
1 code implementation • 26 Nov 2019 • Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran, Srikanth Tamilselvam, Senthil Mani
Further, we predict the performance accuracy of the recommended architecture on the given unknown dataset, without the need for training the model.
no code implementations • 26 Nov 2019 • Raunak Sinha, Anush Sankaran, Mayank Vatsa, Richa Singh
Five different GAN models are implemented as a part of this framework and the performance of the different GAN models are shown using the benchmark MNIST dataset.
no code implementations • 17 Nov 2019 • Senthil Mani, Anush Sankaran, Srikanth Tamilselvam, Akshay Sethi
Further, we conduct various experiments to demonstrate the effectiveness of systematic test case generation system for evaluating deep learning models.
no code implementations • 7 May 2019 • Srikanth Tamilselvam, Naveen Panwar, Shreya Khare, Rahul Aralikatte, Anush Sankaran, Senthil Mani
Deep learning is one of the fastest growing technologies in computer science with a plethora of applications.
no code implementations • 18 Nov 2018 • Saksham Suri, Anush Sankaran, Mayank Vatsa, Richa Singh
In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model.
no code implementations • 11 Nov 2018 • Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy, Senthil Mani
Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex.
no code implementations • EMNLP 2018 • Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
no code implementations • 4 Jan 2018 • Senthil Mani, Anush Sankaran, Rahul Aralikatte
Using an attention mechanism enables the model to learn the context representation over a long word sequence, as in a bug report.
no code implementations • 1 Jan 2018 • Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
no code implementations • 9 Nov 2017 • Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, Senthil Mani
To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph.
no code implementations • 2 Nov 2017 • Senthil Mani, Neelamadhav Gantayat, Rahul Aralikatte, Monika Gupta, Sampath Dechu, Anush Sankaran, Shreya Khare, Barry Mitchell, Hemamalini Subramanian, Hema Venkatarangan
Question answering is one of the primary challenges of natural language understanding.
no code implementations • 25 Sep 2017 • Vitobha Munigala, Srikanth Tamilselvam, Anush Sankaran
Persuasivenes is a creative art aimed at making people believe in certain set of beliefs.
no code implementations • 16 Aug 2017 • Naveen Panwar, Shreya Khare, Neelamadhav Gantayat, Rahul Aralikatte, Senthil Mani, Anush Sankaran
Cross-modal data retrieval has been the basis of various creative tasks performed by Artificial Intelligence (AI).