N. C. Krishnan

发表

Activity Discovery and Activity Recognition: A New Partnership

Diane J. Cook, Parisa Rashidi, Narayanan Chatapuram Krishnan, 2013, IEEE Transactions on Cybernetics.

Activity Learning in the Wild

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

How Is grandma doing

Alex Mihailidis, Diane J. Cook, Gwenn Englebienne, 2012 .

Applications of Activity Learning

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

The Future of Activity Learning

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

Artificial intelligence for gerontechnology : papers from the AAAI Fall Symposium

Alex Mihailidis, Diane J. Cook, Marjorie Skubic, 2012 .

Wiley Series on Parallel And Distributed Computing

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

Activity Learning: Cook/Activity Learning

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

Can google trends search inform us about the population response and public health impact of abrupt change in alcohol policy? A case study from India during the covid-19 pandemic

N. C. Krishnan, Abhishek Ghosh, Shinjini Choudhury, 2020, International Journal of Drug Policy.

Explainable Supervised Domain Adaptation

N. C. Krishnan, V. Kamakshi, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning

N. C. Krishnan, Aroof Aimen, Bharat Ladrecha, 2022 .

Discovering and Tracking Patterns of Interest in Security Sensor Streams

Diane J. Cook, Parisa Rashidi, Narayanan C. Krishnan, 2012 .

The role of eye movement signals in dorsal and ventral processing

Sethuraman Panchanathan, Narayanan Chatapuram Krishnan, John A. Black, 2007, Electronic Imaging.

RACOG and wRACOG: Two Probabilistic Oversampling Techniques

Diane J. Cook, Narayanan Chatapuram Krishnan, Barnan Das, 2015, IEEE Transactions on Knowledge and Data Engineering.

wRACOG: A Gibbs Sampling-Based Oversampling Technique

Diane J. Cook, Narayanan Chatapuram Krishnan, Barnan Das, 2013, 2013 IEEE 13th International Conference on Data Mining.

On Characterizing GAN Convergence Through Proximal Duality Gap

Narayanan C. Krishnan, Aroof Aimen, Sahil Sidheekh, 2021, ICML.

Deep Cross Modal Learning for Caricature Verification and Identification (CaVINet)

Narayanan C. Krishnan, Jatin Garg, Skand Vishwanath Peri, 2018, ACM Multimedia.

Pho(SC)Net: An Approach Towards Zero-shot Word Image Recognition in Historical Documents

Sukalpa Chanda, Narayanan C. Krishnan, Anuj Rai, 2021, ICDAR.

Shallow Domain Adaptation

Sanatan Sukhija, Narayanan C. Krishnan, N. C. Krishnan, 2020 .

Supervised Heterogeneous Domain Adaptation via Random Forests

Narayanan Chatapuram Krishnan, Sanatan Sukhija, Gurkanwal Singh, 2016, IJCAI.

Transfer learning for activity recognition: a survey

Diane J. Cook, Narayanan Chatapuram Krishnan, Kyle D. Feuz, 2013, Knowledge and Information Systems.

Wheat Crop Yield Prediction Using Deep LSTM Model pdf

Sujit Rai, Narayanan C. Krishnan, Sagarika Sharma, 2020, ArXiv.

Designing sulfonated polyimide-based fuel cell polymer electrolyte membranes using machine learning approaches

N. C. Krishnan, D. Mahajan, Narinder Singh, 2023, Computational Materials Science.

Transfer learning for activity recognition: a survey

Kyle D. Feuz, D. Cook, N. C. Krishnan, 2013, Knowledge and Information Systems.

Mining the home environment

D. Cook, N. C. Krishnan, 2014, Journal of Intelligent Information Systems.

A computational framework for wearable accelerometer based activity and gesture recognition

Narayanan Chatapuram Krishnan, N. C. Krishnan, 2010 .

Activity recognition on streaming sensor data

Diane J. Cook, Narayanan Chatapuram Krishnan, D. Cook, 2014, Pervasive Mob. Comput..

CASAS: A Smart Home in a Box

Diane J. Cook, Narayanan Chatapuram Krishnan, Brian L. Thomas, 2013, Computer.

Simple and Complex Activity Recognition through Smart Phones

Diane J. Cook, Narayanan Chatapuram Krishnan, Barnan Das, 2012, 2012 Eighth International Conference on Intelligent Environments.

Defining the Complexity of an Activity

Diane J. Cook, Narayanan Chatapuram Krishnan, Yasamin Sahaf, 2011, Activity Context Representation.

Analysis of low resolution accelerometer data for continuous human activity recognition

Sethuraman Panchanathan, Narayanan Chatapuram Krishnan, S. Panchanathan, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Supervised heterogeneous feature transfer via random forests

Narayanan Chatapuram Krishnan, Sanatan Sukhija, N. C. Krishnan, 2019, Artif. Intell..

PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs

Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks pdf

Ashish Kumar, Prerna Garg, Vidhya Kamakshi, 2020, IEEE Transactions on Artificial Intelligence.

Task Attended Meta-Learning for Few-Shot Learning pdf

Narayanan C. Krishnan, Aroof Aimen, Sahil Sidheekh, 2021, ArXiv.

Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

Diane J. Cook, Narayanan C. Krishnan, D. Cook, 2015 .

Machine learning methods trained on simple models can predict critical transitions in complex natural systems

N. C. Krishnan, P. Dutta, S. Deb, 2021, bioRxiv.

Semantically Aligned Bias Reducing Zero Shot Learning

Narayanan C. Krishnan, Prateek Munjal, Akanksha Paul, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Multi-Task Deep Learning for Predicting Poverty From Satellite Images

Tushar Agarwal, Narayanan C. Krishnan, Shailesh M. Pandey, 2018, AAAI.

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

Deepti R. Bathula, N. C. Krishnan, A. Sambyal, 2021, IEEE International Symposium on Biomedical Imaging.

SpotGarbage: smartphone app to detect garbage using deep learning

Gaurav Mittal, Mohit Garg, Kaushal B. Yagnik, 2016, UbiComp.

Digital screening and brief intervention for illicit drug misuse in college students: A mixed methods, pilot, cluster, randomized trial from India.

N. C. Krishnan, Anil Kumar, S. Kathirvel, 2022, Asian journal of psychiatry.

MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers pdf

Shweta Jain, Rajat Sharma, Nikhil Reddy, 2020, CD-MAKE.

Multi-label Learning for Activity Recognition

Narayanan Chatapuram Krishnan, Rahul Kumar, Imroj Qamar, 2015, 2015 International Conference on Intelligent Environments.

Explainable Image Classification: The Journey So Far and the Road Ahead

N. C. Krishnan, V. Kamakshi, 2023, AI.

EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models

N. C. Krishnan, S. Deb, C. Clements, 2023, Ecography.

Handling Class Overlap and Imbalance to Detect Prompt Situations in Smart Homes

Diane J. Cook, Narayanan Chatapuram Krishnan, Barnan Das, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Leveraging Task Variability in Meta-learning

N. C. Krishnan, Aroof Aimen, Bharat Ladrecha, 2023, SN Computer Science.

Efficient algorithms for fair clustering with a new notion of fairness

Shweta Jain, N. C. Krishnan, Ganesh Ghalme, 2021, Data Mining and Knowledge Discovery.

Efficient Algorithms For Fair Clustering with a New Fairness Notion

N. C. Krishnan, Ganesh Ghalme, Shweta Jain, 2021, ArXiv.

Stress Testing of Meta-learning Approaches for Few-shot Learning

Narayanan C. Krishnan, Aroof Aimen, Sahil Sidheekh, 2021, MetaDL@AAAI.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

Diane J. Cook, Narayanan Chatapuram Krishnan, Maureen Schmitter-Edgecombe, 2016, IEEE Journal of Selected Topics in Signal Processing.

Evaluation of Saliency-based Explainability Method pdf

Vidhya Kamakshi, Narayanan C Krishnan, Sam Zabdiel Sunder Samuel, 2021, ArXiv.

Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset

Diane J. Cook, Barnan Das, Narayanan C. Krishnan, 2014 .

Automated Activity Interventions to Assist with Activities of Daily Living

Diane J. Cook, Narayanan Chatapuram Krishnan, Barnan Das, 2012, Agents and Ambient Intelligence.

Digital screening and brief intervention for alcohol misuse in college students: A pilot, mixed‐methods, cluster randomized controlled trial from a low‐resourced setting

N. C. Krishnan, Anil Kumar, S. Kathirvel, 2023, Asia-Pacific psychiatry : official journal of the Pacific Rim College of Psychiatrists.

Implicit Discriminator in Variational Autoencoder

Narayanan C. Krishnan, Prateek Munjal, Akanksha Paul, 2019, 2020 International Joint Conference on Neural Networks (IJCNN).

Activity gesture spotting using a threshold model based on Adaptive Boosting

Sethuraman Panchanathan, Narayanan Chatapuram Krishnan, Prasanth Lade, 2010, 2010 IEEE International Conference on Multimedia and Expo.

Detecting and classifying frontal, back and profile views of humans

Baoxin Li, Sethuraman Panchanathan, Narayanan Chatapuram Krishnan, 2007, VISAPP.

On Duality Gap as a Measure for Monitoring GAN Training pdf

Narayanan C. Krishnan, Aroof Aimen, Sahil Sidheekh, 2020, 2021 International Joint Conference on Neural Networks (IJCNN).

Characterizing GAN Convergence Through Proximal Duality Gap

N. C. Krishnan, Aroof Aimen, Sahil Sidheekh, 2021, ArXiv.

Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

Makarand Tapaswi, N. C. Krishnan, Aroof Aimen, 2023, ArXiv.

Supervised heterogeneous transfer learning using random forests

Deepak Kumar, Narayanan Chatapuram Krishnan, Sanatan Sukhija, 2018, COMAD/CODS.

Pho(SC)-CTC—a hybrid approach towards zero-shot word image recognition

N. C. Krishnan, S. Chanda, Anuj Rai, 2021, International Journal on Document Analysis and Recognition (IJDAR).

Understanding Calibration of Deep Neural Networks for Medical Image Classification

Deepti R. Bathula, N. C. Krishnan, Usma Niyaz, 2023, Comput. Methods Programs Biomed..

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

Deepti R. Bathula, N. C. Krishnan, A. Sambyal, 2021, 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

Recognition of hand movements using wearable accelerometers

Sethuraman Panchanathan, Narayanan Chatapuram Krishnan, Dirk Colbry, 2009, J. Ambient Intell. Smart Environ..

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

D. Cook, M. Schmitter-Edgecombe, N. C. Krishnan, 2016, IEEE Journal of Selected Topics in Signal Processing.