Statistical Learning
Combining Observational Data and Language for Species Range Estimation Max Hamilton 1 Christian Lange 2 Elijah Cole 3 Alexander Shepard 4
Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.
Banded Square Root Matrix Factorization for Differentially Private Model Training
However, these methods suffer from high computational overhead because they require numerically solving a demanding optimization problem to determine an approximately optimal factorization prior to the actual model training. In this work, we present a new matrix factorization approach, BSR, which overcomes this computational bottleneck. By exploiting properties of the standard matrix square root, BSR allows to efficiently handle also large-scale problems.
ESPACE: Dimensionality Reduction of Activations for Model Compression
ESP ACE projects activations onto a pre-calibrated set of principal components.
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching.
A Scalable Module for Solving Top-k Problems
The cost of ranking becomes significant in the new stage of deep learning.