Deep Learning

blogs

Coffee shop banter: Symbolic or Deep Learning? Promising directions of AI

Sparks flew when friends, Prof. Sriraam Natarajan, UT Dallas and Prof. Kristian Kersting, TU Darmstadt decided to get together over a cup of coffee and discuss on the topic “Symbolic or Deep Learning? Promising directions of AI”. The talk started with setting up the stage with “AI is changing the world and has a huge impact in economics, drug development, agriculture etc.” Kristian believes that AI should be an engine for innovation.

By Ashutosh Dilipbhai Kakadiya

• Jun 22, 2021

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Levelling up NLP for Indian Languages

We are working towards building a better ecosystem for Indian languages while also keeping up with the recent advancements in NLP. To this end, we are releasing IndicNLPSuite, which is a collection of various resources and models for Indian languages

By Divyanshu Kakwani , By Anoop Kunchukuttan

• Feb 10, 2021

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Ablation-CAM: Making AI trustworthy

As machine learning is set to change every aspect of our life, a key dilemma plagues the minds of researchers and its users- Can we trust machines to make key life decisions for us? Can we rely solely on the machine to drive us safely to the destination without knowing the basis of its function or shall we allow a machine to operate us instead of a doctor?

By Saurabh Desai , By Harish G. Ramaswamy

• Jan 13, 2021

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Deep learning in biomedical image analysis

Medical imaging, specifically radiologic imaging is the most commonly used diagnostic tool for disease diagnosis and treatment assessment for a wide variety of conditions. Over the last decades the image acquisition hardware has improved significantly and corresponding image reconstruction software has become more sophisticated. These provide increasingly complex data both in terms of size and content, making it a challenging task for radiologists to sift through and arrive at meaningful diagnosis and therapeutic assessment. The role of AI/ML techniques in this context is to act as a radiologist’s assistant to automate routine tasks and provide preliminary diagnosis. A radiologist can then use the outputs from these systems to speed up and improve accuracy of diagnosis.

By Mahendra Khened , By Avinash G Kori

• Nov 17, 2020

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events

RBCDSAI Seminar by Prof. Gugan Thoppe

May 2, 2023 04:00 PM to 05:00 PM

BT Seminar Hall

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RBCDSAI Distinguished Fellow Colloquium by Prof. Sriram Natarajan

April 25, 2023 to April 27, 2023 09:00 AM to 12:00 PM

IC&SR Hall 2

artificial intelligence , deep learning , reinforcement-learning

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IBSE ARP Talk - Prof. Vijay Tiwari

April 5, 2023 04:30 PM to 05:30 PM

IC&SR Hall 2

artificial intelligence , deep learning , reinforcement-learning

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Eighth RBCDSAI LatentView Colloquium by Dr. Rema Padman

March 16, 2023 05:00 PM to 06:30 PM

IC&SR Hall 2

LatentView-Colloquium , artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar Series - Prof Lavanya Marla

January 3, 2023 10:30 AM to 11:30 AM

Seminar Hall, RBCDSAI

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Prof Madhav Marathe

December 15, 2022 04:00 PM to 05:00 PM

CRC301, IITM

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Gugan Thoppe

October 7, 2022 04:15 PM to 05:15 PM

Seminar Hall, Block I, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Dr. Vivek Kumar

July 11, 2022 05:00 PM to 06:00 PM

Aryabhatta Hall, CSB 25, IITM

artificial intelligence , deep learning , reinforcement-learning

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Seventh RBCDSAI LatentView Colloquium by Prof. V. S. Subrahmanian

May 12, 2022 04:00 PM to 05:00 PM

Hybrid, CRC 302

LatentView-Colloquium , artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Gugan Thoppe

April 19, 2022 04:00 PM to 05:00 PM

CRC, IITM

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Distinguished Fellow Colloquium by Prof. Sriraam Natarajan

March 30, 2022 04:00 PM to 05:00 PM

CRC, IITM

artificial intelligence , deep learning , human-in-the-loop

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RBCDSAI 2-Day Workshop by Prof. Sriraam Natarajan

March 28, 2022 09:30 AM to 12:30 PM

CRC, IITM

artificial intelligence , deep learning , human-in-the-loop

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RBCDSAI Seminar - Balaji Vasan

March 25, 2022 03:00 PM to 04:00 PM

CRC, IITM

artificial intelligence , deep learning , human-in-the-loop

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people

Mitesh M. Khapra

Associate Professor

Specialization:

Statistical Machine Translation, Text Analytics, Deep Learning, Crowd-sourcing

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Krithi Shailya

Post Baccalaureate Fellow

Specialization:

Deep Learning, Healthcare Applications

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Adithya Sai Lenka

Post Baccalaureate Fellow

Specialization:

Computer Vision, Deep Learning

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Adhil Ahmed P M Shums

Post Baccalaureate Fellow

Specialization:

Big data, data visualisation, NLP, financial forensics, reinforcement learning, deep learning

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Mallikarjun Annappagouda Jamadarkhani

Post Baccalaureate Fellow

Specialization:

Time Series Analysis, Condition-Monitoring, Fault detection, Deep Learning, Graph Neural Networks

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Ragul S

Post Baccalaureate Fellow

Specialization:

Deep Learning, Medical Imaging

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Utsav Dey

MS Scholar

Specialization:

Multi Armed Bandit, Deep Learning

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Roshan M S B

PhD Scholar

Specialization:

Deep learning, NLP, Artificial metabolic pathway design

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S Gokull

Post Baccalaureate Fellow

Specialization:

Deep Learning, Computer Vision, Mobile Robotics

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Argha Boksi

MS Scholar

Specialization:

Machine Learning, Deep Learning, Reinforcement Learning

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Prithaj Banerjee

MS Scholar

Specialization:

Machine Learning, Deep Learning

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Rohan Saphal

Project Officer

Specialization:

Deep Reinforcement Learning, Deep Learning, Recommendation Systems, Robotic Control

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Prasan Shedligeri

PhD Scholar

Specialization:

Deep learning, Computational Photography, Computer Vision

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Rajan Kumar Soni

MS Scholar

Specialization:

Machine Learning, Explainability of neural networks, Deep learning

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Dhruv Ramani

Project Associate

Specialization:

Reinforcement Learning, Deep Learning

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Akansh Maurya

Post Baccalaureate Fellow

Specialization:

Explainable AI, Medical Imaging, Deep Learning

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Ananya Sai

PhD Scholar

Specialization:

NLP, Deep learning

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Lakshmi Narayan Pandey

MS Scholar

Specialization:

Machine Learning, Deep Learning

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Karthik Thiagarajan

MS Scholar

Specialization:

Deep Learning, Computer Vision, Interpretable machine learning, Image registration

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Abhishek Rao

Dual Degree

Specialization:

Deep learning, Proteins

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Nikitha Moghe

MS Scholar

Specialization:

Natural Language Processing, Deep Learning, Dialog Systems, Representation Learning

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Pritha Ganguly

MS Scholar

Specialization:

Deep Learning, Computer Vision, Visual Question Answering

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Nitesh Methani

MS Scholar

Specialization:

Deep Learning, Computer Vision, Visual Question Answering, Object Detection

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Preksha Nema

PhD Scholar

Specialization:

Deep Learning, Natural Language Processing

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Sivapriya Vellaichamy

Post Baccalaureate Fellow

Specialization:

Deep Learning, Reinforcement Learning

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Sourav Kumar Mohanty

Project Associate

Specialization:

Machine Learning, Deep Learning, Representation Learning

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Manoj Bharadhwaj

MS Scholar

Specialization:

Deep Learning, Reinforcement Learning, Intelligent Transportation Systems, Explainable AI

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Siddharth Nishtala

MS Scholar

Specialization:

Deep Learning, Reinforcement Learning

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Yadav Mahesh Lorik

Research Scholar

Specialization:

Machine Learning, Deep Learning

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Rahul Vashisht

PhD Scholar

Specialization:

Theoretical Machine Learning, Deep Learning, Gaussian Processes, Computer Vision, NLP

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Aravind Easwar

MS Scholar

Specialization:

Causal Discovery, Machine Learning, Deep Learning

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projects

Ganapathy Krishnamurthi , Balaji Srinivasan

Deep Learning Model for Chest X-ray Imaging

deep learning healthcare

Ganapathy Krishnamurthi , Balaji Srinivasan , B Ravindran

Interpretability of Deep Learning Models in Healthcare

deep learning interpretability

Balaji Srinivasan , Ganapathy Krishnamurthi , Sivaram Ambikasaran

Towards Autonomous PDE Solvers Automated Generation of Robust Numerical

autonomous PDE solvers deep learning

Srinivasa Chakravarthy V , Krishnan Balasubramanian

Developing Temporal Convolutional Neural Networks comprising Nonlinear Oscillators for Non-Destructive Evaluation using Active Thermographic images

deep learning non-destructive evaluation

Mitesh M Khapra

Attention Mechanisms in Deep Neural Networks

deep learning reinforcement learning

publications

Parse Challenge 2022: Pulmonary Arteries Segmentation Using Swin U-net Transformer(Swin UNETR) and U-net

Rohan Padhy, Akansh Maurya, Kunal Dasharath Patil, Kalluri Ramakrishna, Ganapathy Krishnamurthi,

IEEE 20th International Symposium on Biomedical Imaging (ISBI)

https://doi.org/10.1109/ISBI53787.2023.10230839

Scaling Graph Propagation Kernels for Predictive Learning

Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran,

Frontiers in Big Data - Data Mining and Management

https://doi.org/10.3389/fdata.2022.616617

Smooth Imitation Learning via Smooth Costs and Smooth Policies

Sapana Chauhary, B Ravindran,

5th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2022)

https://dl.acm.org/doi/abs/10.1145/3493700.3493716

Towards Building ASR Systems for the Next Billion Users

Tahir Javed, Sumanth Doddapaneni, Abhigyan Raman, Kaushal Santosh Bhogale, Gowtham Ramesh, Anoop Kunchukuttan, Pratyush Kumar, Mitesh M. Khapra,

To appear in the Proceedings of the Thirty-Sixth Annual Conference of the Association for Advancement of Artificial Intelligence (AAAI 2022)

https://arxiv.org/abs/2111.03945

CDiNN – Convex difference neural networks

Parameswaran Sankaranarayanan, Raghunathan Rengaswamy,

Neurocomputing

https://doi.org/10.1016/j.neucom.2022.01.024

Unsupervised Deep Video Denoising

Dev Yashpal Sheth, Sreyas Mohan, Joshua Vincent, Ramon Manzorro, Peter A. Crozier, Mitesh M. Khapra, Eero P. Simoncelli, Carlos Fernandez-Granda,

International Conference on Computer Vision (ICCV 2021)

https://arxiv.org/abs/2011.15045

Semi-Supervised Deep Learning for Multiplex Networks

Anasua Mitra, Priyesh Vijayan", Ranbir Sanasam", Diganta Goswami", Srinivasan Parthasarathy", Balaraman Ravindran",

To appear in the proceedings of the Twenty Seventh ACM SIGKDD Conference on Knowledge Discovery and Data Mining

https://doi.org/10.1145/3447548.3467443

Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis

Parth Natekar, Avinash Kori, Ganapathy Krishnamurthi,

Frontiers in computational neuroscience

https://doi.org/10.3389/fncom.2020.00006

Training a deep learning architecture for vehicle detection using limited heterogeneous traffic data

Deepak Mittal, Avinash Reddy, Gitakrishnan Ramadurai, Kaushik Mitra, B Ravindran,

Communication Systems & Networks

https://doi.org/10.1109/COMSNETS.2018.8328279

A neural attention based approach for clickstream mining

T. N Chandramohan, B Ravindran,

Proceedings of the ACM IKDD Joint International Conference on Data Science & Management of Data

https://doi.org/10.1145/3152494.3152505

Recovering from random pruning: On the plasticity of deep convolutional neural networks

Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra, Balaraman Ravindran,

In 2018 IEEE Winter Conference on Applications of Computer Vision

https://doi.org/10.1109/WACV.2018.00098

Correlational neural networks

Sarath Chandar, Mitesh M Khapra, Hugo Larochelle, B Ravindran,

Neural computation

https://doi.org/10.1162/neco_a_00801

research

Safety and Stability Preserving Reinforcement Learning

reinforcement learning deep learning