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Found 16 papers, 8 papers with code
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.
BERTops: Studying BERT Representations under a Topological Lens
Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT can be a challenging task.
ALLWAS: Active Learning on Language models in WASserstein space
Language models have emerged as the prevalent choice of several natural language tasks due to the performance boost offered by these models.
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models
Despite significant improvements in natural language understanding models with the advent of models like BERT and XLNet, these neural-network based classifiers are vulnerable to blackbox adversarial attacks, where the attacker is only allowed to query the target model outputs.
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach
In this paper, we overcome these obstacles by capturing higher-order interactions succinctly as \textit{simplices}, model their neighborhood by face-vectors, and develop a nonparametric kernel estimator for simplices that views the evolving graph from the perspective of a time process (i. e., a sequence of graph snapshots).
Higher-order Structure Prediction in Evolving Graph Simplicial Complexes
In this paper, we overcome these obstacles by capturing higher-order interactions succinctly as simplices, model their neighborhood by face-vectors, and develop a nonparametric kernel estimator for simplices that views the evolving graph from the perspective of a time process (i. e., a sequence of graph snapshots).
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.
A Weighted Quiver Kernel using Functor Homology
Our model of such networks is a directed graph $Q$ equipped with a weight function $w$ on the set $Q_{1}$ of arrows in $Q$.
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
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.
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples.
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
Sharing Hash Codes for Multiple Purposes
In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities.
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost.
