60 citations · 547 across the 40 of their papers we have counts for
8 papers · 1 filter
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Mandeep Rathee, Thorben Funke, Avishek Anand +1
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with gra…
Learnt Sparsification for Interpretable Graph Neural Networks
Mandeep Rathee, Zijian Zhang, Thorben Funke +2
Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as ind…
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Thorben Funke, Megha Khosla, Mandeep Rathee +1
With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. E…
Valid Explanations for Learning to Rank Models
Jaspreet Singh, Zhenye Wang, Megha Khosla +1
Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread applicatio…
Boilerplate Removal using a Neural Sequence Labeling Model
Jurek Leonhardt, Avishek Anand, Megha Khosla
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, t…
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
Maximilian Idahl, Megha Khosla, Avishek Anand
In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representa…
A Comparative Study for Unsupervised Network Representation Learning
Megha Khosla, Vinay Setty, Avishek Anand
There has been appreciable progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization ob…
Asynchronous Training of Word Embeddings for Large Text Corpora
Avishek Anand, Megha Khosla, Jaspreet Singh +2
Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over hug…