Search Results for author:
Found 5 papers, 1 papers with code
Learning Graph Search Heuristics
At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process.
How stable are Transferability Metrics evaluations?
Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target dataset, without fine-tuning them all.
Transferability Estimation using Bhattacharyya Class Separability
Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task.
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
Unsupervised Path Regression Networks
We demonstrate that challenging shortest path problems can be solved via direct spline regression from a neural network, trained in an unsupervised manner (i. e. without requiring ground truth optimal paths for training).
