8 papers
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Sahil Mishra, Srinitish Srinivasan, Sourish Dasgupta +1
Real-world knowledge is often organized as hierarchies such as product taxonomies, medical ontologies, and label trees, yet learning hierarchical representations is challenging due…
TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion
Sahil Mishra, Srinitish Srinivasan, Srikanta Bedathur +1
Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce and semantic search. Yet, manual taxonomy expan…
Can we ease the Injectivity Bottleneck on Lorentzian Manifolds for Graph Neural Networks?
Srinitish Srinivasan, Omkumar CU
While hyperbolic GNNs show promise for hierarchical data, they often have limited discriminative power compared to Euclidean counterparts or the WL test, due to non-injective aggre…
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
Jaskaran Singh Walia, Aarush Sinha, Naman Saraswat +2
Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framew…
Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning
Srinitish Srinivasan, Omkumar CU
Graph representation learning has emerged as a cornerstone for tasks like node classification and link prediction, yet prevailing self-supervised learning (SSL) methods face challe…
Enhancing IoT based Plant Health Monitoring through Advanced Human Plant Interaction using Large Language Models and Mobile Applications
Kriti Agarwal, Samhruth Ananthanarayanan, Srinitish Srinivasan +1
This paper presents the development of a novel plant communication application that allows plants to "talk" to humans using real-time sensor data and AI-powered language models. Ut…
Autoencoder based approach for the mitigation of spurious correlations
Srinitish Srinivasan, Karthik Seemakurthy
Deep neural networks (DNNs) have exhibited remarkable performance across various tasks, yet their susceptibility to spurious correlations poses a significant challenge for out-of-d…
Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
Srinitish Srinivasan, Varenya Pathak, Abirami S
Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist. Many researchers have made attempts to study abstract ar…