indian institute of science

research

∙ 12/18/2019

Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware

Every day around the world, interminable terabytes of data are being cap...

Yeshwanth Ravi Theja Bethi, et al. ∙

research

∙ 09/08/2019

CUDA: Contradistinguisher for Unsupervised Domain Adaptation

In this paper, we propose a simple model referred as Contradistinguisher...

Sourabh Balgi, et al. ∙

research

∙ 11/21/2019

EvAn: Neuromorphic Event-based Anomaly Detection

Event-based cameras are bio-inspired novel sensors that asynchronously r...

Lakshmi Annamalai, et al. ∙

research

∙ 06/09/2022

Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms

The COVID-19 pandemic has accelerated research on design of alternative,...

Debarpan Bhattacharya, et al. ∙

research

∙ 06/24/2022

Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals

The COVID-19 outbreak resulted in multiple waves of infections that have...

Debarpan Bhattacharya, et al. ∙

research

∙ 08/11/2019

GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions

Despite the remarkable success of generative adversarial networks, their...

Jogendra Nath Kundu, et al. ∙

research

∙ 08/03/2020

Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images

In the emerging commercial space industry there is a drastic increase in...

Gaurav Kumar Nayak, et al. ∙

research

∙ 05/26/2021

DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder

Accurate reconstruction of static environments from LiDAR scans of scene...

Prashant Kumar, et al. ∙

research

∙ 11/06/2018

Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Existing fast algorithms for bilateral and nonlocal means filtering most...

Pravin Nair, et al. ∙

research

∙ 05/20/2022

Open Arms: Open-Source Arms, Hands Control

Open Arms is a novel open-source platform of realistic human-like roboti...

David Hanson, et al. ∙

research

∙ 11/14/2018

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

Developing accurate, transferable and computationally inexpensive machin...

Soumya Sanyal, et al. ∙

research

∙ 03/19/2021

Online Lifelong Generalized Zero-Shot Learning

Methods proposed in the literature for zero-shot learning (ZSL) are typi...

Chandan Gautam, et al. ∙

research

∙ 06/11/2022

Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021

This report describes the system used for detecting COVID-19 positives u...

Deepak Mittal, et al. ∙

research

∙ 01/25/2020

The role of surrogate models in the development of digital twins of dynamic systems

Digital twin technology has significant promise, relevance and potential...

Souvik Chakraborty, et al. ∙

research

∙ 10/14/2021

MIMICause : Defining, identifying and predicting types of causal relationships between biomedical concepts from clinical notes

Understanding of causal narratives communicated in clinical notes can he...

Vivek Khetan, et al. ∙

research

∙ 04/22/2022

Spacing Loss for Discovering Novel Categories

Novel Class Discovery (NCD) is a learning paradigm, where a machine lear...

K J Joseph, et al. ∙

research

∙ 03/17/2020

Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines

Cerebrovascular accident (CVA) or stroke is the rapid loss of brain func...

Jerrin Thomas Panachakel, et al. ∙

research

∙ 06/27/2022

Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection

In this paper, we describe an approach for representation learning of au...

Debottam Dutta, et al. ∙

research

∙ 10/02/2018

Quantization-Aware Phase Retrieval

We address the problem of phase retrieval (PR) from quantized measuremen...

Subhadip Mukherjee, et al. ∙

research

∙ 07/25/2020

A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19

The outbreak of the novel coronavirus (COVID-19) is unfolding as a major...

Rahul Singh, et al. ∙

research

∙ 08/19/2021

Efficient remedies for outlier detection with variational autoencoders

Deep networks often make confident, yet incorrect, predictions when test...

Kushal Chauhan, et al. ∙

research

∙ 10/19/2021

Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm

Learning optimal behavior from existing data is one of the most importan...

Raghuram Bharadwaj Diddigi, et al. ∙

research

∙ 04/13/2022

CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

The last decade has witnessed a surge in the interaction of people throu...

Souvic Chakraborty, et al. ∙

research

∙ 12/06/2018

Binary Document Image Super Resolution for Improved Readability and OCR Performance

There is a need for information retrieval from large collections of low-...

Ram Krishna Pandey, et al. ∙

research

∙ 07/07/2020

Learning to Count in the Crowd from Limited Labeled Data

Recent crowd counting approaches have achieved excellent performance. ho...

Vishwanath A. Sindagi, et al. ∙

research

∙ 01/18/2021

TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection

Visual steel surface defect detection is an essential step in steel shee...

Praveen Damacharla, et al. ∙

research

∙ 12/26/2020

One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks

A robot that can operate in novel and unstructured environments must be ...

Sagar Gubbi Venkatesh, et al. ∙

research

∙ 08/03/2018

Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

Deep learning models are susceptible to input specific noise, called adv...

Konda Reddy Mopuri, et al. ∙

research

∙ 11/06/2018

Fast Adaptive Bilateral Filtering

In the classical bilateral filter, a fixed Gaussian range kernel is used...

Ruturaj G. Gavaskar, et al. ∙

research

∙ 08/11/2019

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation

Aiming towards human-level generalization, there is a need to explore ad...

Jogendra Nath Kundu, et al. ∙

research

∙ 03/14/2022

The Role of Interactivity in Structured Estimation

We study high-dimensional sparse estimation under three natural constrai...

Jayadev Acharya, et al. ∙

research

∙ 04/04/2022

DAD: Data-free Adversarial Defense at Test Time

Deep models are highly susceptible to adversarial attacks. Such attacks ...

Gaurav Kumar Nayak, et al. ∙

research

∙ 12/25/2020

Teaching Robots Novel Objects by Pointing at Them

Robots that must operate in novel environments and collaborate with huma...

Sagar Gubbi Venkatesh, et al. ∙

research

∙ 07/16/2019

Mango Tree Net -- A fully convolutional network for semantic segmentation and individual crown detection of mango trees

This work presents a method for semantic segmentation of mango trees in ...

Vikas Agaradahalli Gurumurthy, et al. ∙

research

∙ 05/25/2020

Contradistinguisher: Applying Vapnik's Philosophy to Unsupervised Domain Adaptation

A complex combination of simultaneous supervised-unsupervised learning i...

Sourabh Balgi, et al. ∙

research

∙ 07/28/2020

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

With the research into development of quadruped robots picking up pace, ...

Sashank Tirumala, et al. ∙

research

∙ 06/18/2019

Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection

We introduce a detection framework for dense crowd counting and eliminat...

Deepak Babu Sam, et al. ∙

research

∙ 11/10/2019

A Re-evaluation of Knowledge Graph Completion Methods

Knowledge Graph Completion (KGC) aims at automatically predicting missin...

Zhiqing Sun, et al. ∙

research

∙ 05/01/2020

A Naturalness Evaluation Database for Video Prediction Models

The study of video prediction models is believed to be a fundamental app...

Nagabhushan Somraj, et al. ∙

research

∙ 05/05/2022

Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Adversarial attack perturbs an image with an imperceptible noise, leadin...

Gaurav Kumar Nayak, et al. ∙

research

∙ 12/04/2017

A Dual Framework for Low-rank Tensor Completion

We propose a novel formulation of the low-rank tensor completion problem...

Madhav Nimishakavi, et al. ∙

research

∙ 11/21/2017

Groupwise Maximin Fair Allocation of Indivisible Goods

We study the problem of allocating indivisible goods among n agents in a...

Siddharth Barman, et al. ∙

research

∙ 11/14/2017

A unified decision making framework for supply and demand management in microgrid networks

This paper considers two important problems - on the supply-side and dem...

Raghuram Bharadwaj Diddigi, et al. ∙

research

∙ 11/27/2017

Transfer Learning in CNNs Using Filter-Trees

Convolutional Neural Networks (CNNs) are very effective for many pattern...

Suresh Kirthi Kumaraswamy, et al. ∙

research

∙ 10/26/2017

Artifact reduction for separable non-local means

It was recently demonstrated [J. Electron. Imaging, 25(2), 2016] that on...

Sanjay Ghosh, et al. ∙

research

∙ 11/21/2016

Generalized Dropout

Deep Neural Networks often require good regularizers to generalize well....

Suraj Srinivas, et al. ∙

research

∙ 09/14/2017

Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations

The main aim of this paper is the development of easily verifiable suffi...

Arunselvan Ramaswamy, et al. ∙

research

∙ 09/05/2017

SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks

The ability to semantically interpret hand-drawn line sketches, although...

Ravi Kiran Sarvadevabhatla, et al. ∙

research

∙ 08/22/2017

CNN Fixations: An unraveling approach to visualize the discriminative image regions

Deep convolutional neural networks (CNN) have revolutionized various fie...

Konda Reddy Mopuri, et al. ∙

research

∙ 08/01/2017

Switching Convolutional Neural Network for Crowd Counting

We propose a novel crowd counting model that maps a given crowd scene to...

Deepak Babu Sam, et al. ∙

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