7 citations · 13 across the 10 of their papers we have counts for
11 papers
Better Learning-Augmented Spanning Tree Algorithms via Metric Forest Completion
Nate Veldt, Thomas Stanley, Benjamin W. Priest +5
We present improved learning-augmented algorithms for finding an approximate minimum spanning tree (MST) for points in an arbitrary metric space. Our work follows a recent framewor…
Approximate Tree Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
Nate Veldt, Thomas Stanley, Benjamin W. Priest +4
Finding a minimum spanning tree (MST) for points in an arbitrary metric space is a fundamental primitive for hierarchical clustering and many other ML tasks, but this takes $Ω(…
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction
Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh +1
In this paper, we explore the potential of Vision-Language Models (VLMs), specifically CLIP, in predicting visual object relationships, which involves interpreting visual features…
The White-Box Adversarial Data Stream Model
Miklos Ajtai, Vladimir Braverman, T. S. Jayram +4
We study streaming algorithms in the white-box adversarial model, where the stream is chosen adaptively by an adversary who observes the entire internal state of the algorithm at e…
Transfer Learning in Visual and Relational Reasoning
T. S. Jayram, Vincent Marois, Tomasz Kornuta +3
Transfer learning has become the de facto standard in computer vision and natural language processing, especially where labeled data is scarce. Accuracy can be significantly improv…
On transfer learning using a MAC model variant
Vincent Marois, T. S. Jayram, Vincent Albouy +3
We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster. We evaluate bot…