56 citations · 175 across the 29 of their papers we have counts for
10 papers · 1 filter
Grounding Computer Use Agents on Human Demonstrations
Aarash Feizi, Shravan Nayak, Xiangru Jian +14
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web…
Weak Supervision for Real World Graphs
Pratheeksha Nair, Reihaneh Rabbany
Node classification in real world graphs often suffers from label scarcity and noise, especially in high stakes domains like human trafficking detection and misinformation monitori…
PairBench: Are Vision-Language Models Reliable at Comparing What They See?
Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano +4
Understanding how effectively large vision language models (VLMs) compare visual inputs is crucial across numerous applications, yet this fundamental capability remains insufficien…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch +7
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning mod…
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau +1
How can we detect traffic disturbances from international flight transportation logs or changes to collaboration dynamics in academic networks? These problems can be formulated as…
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Shenyang Huang, Samy Coulombe, Yasmeen Hitti +2
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for…
Towards Better Evaluation for Dynamic Link Prediction
Farimah Poursafaei, Shenyang Huang, Kellin Pelrine +1
Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent e…
Graph Attention Networks with Positional Embeddings
Liheng Ma, Reihaneh Rabbany, Adriana Romero-Soriano
Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighbori…
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau +1
Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is…