183 citations · 509 across the 28 of their papers we have counts for
28 papers
-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences
Tirtharaj Dash, Gunja Sachdeva
We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines -adic numbers with topological data analysis. Each DN…
BIRDNet: Mining and Encoding Boolean Implication Knowledge Graphs as Interpretable Deep Neural Networks
Tirtharaj Dash
Tabular data in knowledge-rich domains often carries a latent prior in the form of Boolean implication relationships (BIRs) between pairs of features. We mine such relationships wi…
Symbolic Neural Generation with Applications to Lead Discovery in Drug Design
Ashwin Srinivasan, Tirtharaj Dash, A Baskar +3
We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to construct data generators meeting formal c…
IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification
Shreyas Bhat Brahmavar, Rohit Rajesh, Tirtharaj Dash +6
Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%. However, the following aspects remain unaddressed: State-of-the-art mo…
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash +5
Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and…
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
Ramya Hebbalaguppe, Rishabh Patra, Tirtharaj Dash +2
Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model…
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces
Vishwa Shah, Aditya Sharma, Gautam Shroff +3
Analogical Reasoning problems challenge both connectionist and symbolic AI systems as these entail a combination of background knowledge, reasoning and pattern recognition. While s…
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches
Abhinav Lalwani, Aman Saraiya, Apoorv Singh +2
Machine Learning has become an integral part of engineering design and decision making in several domains, including sports. Deep Neural Networks (DNNs) have been the state-of-the-…
Composition of Relational Features with an Application to Explaining Black-Box Predictors
Ashwin Srinivasan, A Baskar, Tirtharaj Dash +1
Relational machine learning programs like those developed in Inductive Logic Programming (ILP) offer several advantages: (1) The ability to model complex relationships amongst data…
Solving Visual Analogies Using Neural Algorithmic Reasoning
Atharv Sonwane, Gautam Shroff, Lovekesh Vig +2
We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to ana…
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems
Atharv Sonwane, Sharad Chitlangia, Tirtharaj Dash +3
The ability to recognise and make analogies is often used as a measure or test of human intelligence. The ability to solve Bongard problems is an example of such a test. It has als…
A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja +1
We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special inter…
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
Tirtharaj Dash, Ashwin Srinivasan, A Baskar
We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse ent…
Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
Gunjan Chhablani, Abheesht Sharma, Harshit Pandey +1
Superpixels are higher-order perceptual groups of pixels in an image, often carrying much more information than the raw pixels. There is an inherent relational structure to the rel…
Incorporating Domain Knowledge into Deep Neural Networks
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja +1
We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not…
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting
Abheesht Sharma, Harshit Pandey, Gunjan Chhablani +2
In this article, we present our methodologies for SemEval-2021 Task-4: Reading Comprehension of Abstract Meaning. Given a fill-in-the-blank-type question and a corresponding contex…
Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays
Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash +2
In this paper, our focus is on constructing models to assist a clinician in the diagnosis of COVID-19 patients in situations where it is easier and cheaper to obtain X-ray data tha…
Incorporating Symbolic Domain Knowledge into Graph Neural Networks
Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig
Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3…
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility
Rohit Kaushik, Shikhar Jain, Siddhant Jain +1
The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the…
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
Wazeer Zulfikar, Sebastin Santy, Sahith Dambekodi +1
Face frontalization is the process of synthesizing frontal facing views of faces given its angled poses. We implement a generative adversarial network (GAN) with spherical linear i…
Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data
Siddharth Dinesh, Tirtharaj Dash
This paper presents a systematic evaluation of Neural Network (NN) for classification of real-world data. In the field of machine learning, it is often seen that a single parameter…
A Fuzzy MLP Approach for Non-linear Pattern Classification
Tirtharaj Dash, H. S. Behera
In case of decision making problems, classification of pattern is a complex and crucial task. Pattern classification using multilayer perceptron (MLP) trained with back propagation…
A Novel Approach for Intelligent Robot Path Planning
Tirtharaj Dash, Goutam Mishra, Tanistha Nayak
Path planning of Robot is one of the challenging fields in the area of Robotics research. In this paper, we proposed a novel algorithm to find path between starting and ending posi…
Non-Correlated Character Recognition using Artificial Neural Network
Tirtharaj Dash, Tanistha Nayak
This paper investigates a method of Handwritten English Character Recognition using Artificial Neural Network (ANN). This work has been done in offline Environment for non correlat…