36 citations · 105 across the 22 of their papers we have counts for
25 papers
A Probabilistic Circuit-Induced Pseudo-Metric for Out-of-Distribution Detection
Bhumika K, Vidhya S, Narayanan C Krishnan
Probabilistic Circuits (PCs) are tractable generative models whose internal nodes encode a hierarchy of probabilistic summaries over different variable scopes. Existing PC-based ou…
A Compositional Theory of Curvature in Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay +3
Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: th…
Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions
Anagha Sabu, Hrithik Suresh, Narayanan C. Krishnan
Algorithmic recourse seeks to help individuals reverse unfavorable automated decisions by recommending actionable changes that achieve a desired outcome. As an individual usually h…
PAR: Plausibility-aware Amortized Recourse Generation
Anagha Sabu, Vidhya S, Narayanan C Krishnan
Algorithmic recourse aims to recommend actionable changes to a factual's attributes that flip an unfavorable model decision while remaining realistic and feasible. We formulate rec…
Learning Regularizers: Learning Optimizers that can Regularize
Suraj Kumar Sahoo, Narayanan C Krishnan
Learned Optimizers (LOs), a type of Meta-learning, have gained traction due to their ability to be parameterized and trained for efficient optimization. Traditional gradient-based…
Tractable Sharpness-Aware Learning of Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P +2
Probabilistic Circuits (PCs) are a class of generative models that allow exact and tractable inference for a wide range of queries. While recent developments have enabled the learn…
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays
Aroof Aimen, Arsh Verma, Makarand Tapaswi +1
Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are der…
Understanding Calibration of Deep Neural Networks for Medical Image Classification
Abhishek Singh Sambyal, Usma Niyaz, Narayanan C. Krishnan +1
In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play…
Explainable Supervised Domain Adaptation
Vidhya Kamakshi, Narayanan C Krishnan
Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domai…
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks
Abhishek Singh Sambyal, Narayanan C. Krishnan, Deepti R. Bathula
In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation an…
Efficient Algorithms For Fair Clustering with a New Fairness Notion
Shivam Gupta, Ganesh Ghalme, Narayanan C. Krishnan +1
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every clust…
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining…
Deep Geospatial Interpolation Networks
Sumit Kumar Varshney, Jeetu Kumar, Aditya Tiwari +3
Interpolation in Spatio-temporal data has applications in various domains such as climate, transportation, and mining. Spatio-Temporal interpolation is highly challenging due to th…
Evaluation of Saliency-based Explainability Method
Sam Zabdiel Sunder Samuel, Vidhya Kamakshi, Namrata Lodhi +1
A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as…
Task Attended Meta-Learning for Few-Shot Learning
Aroof Aimen, Sahil Sidheekh, Narayanan C. Krishnan
Meta-learning (ML) has emerged as a promising direction in learning models under constrained resource settings like few-shot learning. The popular approaches for ML either learn a…
On Characterizing GAN Convergence Through Proximal Duality Gap
Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this diffic…
Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition
Ravi Bhatt, Anuj Rai, Narayanan C. Krishnan +1
Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life…
Stress Testing of Meta-learning Approaches for Few-shot Learning
Aroof Aimen, Sahil Sidheekh, Vineet Madan +1
Meta-learning (ML) has emerged as a promising learning method under resource constraints such as few-shot learning. ML approaches typically propose a methodology to learn generaliz…
On Duality Gap as a Measure for Monitoring GAN Training
Sahil Sidheekh, Aroof Aimen, Vineet Madan +1
Generative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging ta…
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
Rajat Sharma, Nikhil Reddy, Vidhya Kamakshi +2
The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-…
Wheat Crop Yield Prediction Using Deep LSTM Model
Sagarika Sharma, Sujit Rai, Narayanan C. Krishnan
An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reli…
MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks
Ashish Kumar, Karan Sehgal, Prerna Garg +2
Deep convolutional networks have been quite successful at various image classification tasks. The current methods to explain the predictions of a pre-trained model rely on gradient…
Implicit Discriminator in Variational Autoencoder
Prateek Munjal, Akanksha Paul, Narayanan C. Krishnan
Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generati…
Semantically Aligned Bias Reducing Zero Shot Learning
Akanksha Paul, Narayanan C. Krishnan, Prateek Munjal
Zero shot learning (ZSL) aims to recognize unseen classes by exploiting semantic relationships between seen and unseen classes. Two major problems faced by ZSL algorithms are the h…