Search Results for author:
Found 9 papers, 6 papers with code
Explaining Indian Stock Market through Geometry of Scale free Networks
This paper presents an analysis of the Indian stock market using a method based on embedding the network in a hyperbolic space using Machine learning techniques.
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation
Recently, the fundamental problem of unsupervised domain adaptation (UDA) on 3D point clouds has been motivated by a wide variety of applications in robotics, virtual reality, and scene understanding, to name a few.
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering
Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG).
Ranked #8 on
Question Answering
on OpenBookQA
Person Re-Identification with a Locally Aware Transformer
At present, the majority of Person re-ID techniques are based on Convolutional Neural Networks (CNNs), but Vision Transformers are beginning to displace pure CNNs for a variety of object recognition tasks.
Ranked #1 on
Person Re-Identification
on CUHK03
Self-Supervised Few-Shot Learning on Point Clouds
We present a comprehensive empirical evaluation of our method on both downstream classification and segmentation tasks and show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods.
Simplicial Complex based Point Correspondence between Images warped onto Manifolds
Recent increase in the availability of warped images projected onto a manifold (e. g., omnidirectional spherical images), coupled with the success of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection.
Learning Representations using Spectral-Biased Random Walks on Graphs
Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex local graph properties, scalability, and interpretibility.
Solving Partial Assignment Problems using Random Clique Complexes
We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).
Ranked #1 on
Knowledge Graph Completion
on FB15k-237
