46 citations · 75 across the 10 of their papers we have counts for
6 papers · 1 filter
Scaling Laws for Behavioral Foundation Models over User Event Sequences
Rickard Brüel Gabrielsson
Foundation models are increasingly trained on sequences of user actions in recommendation, payments, fraud, and commerce, but these models still lack the kind of compute calibratio…
Asymmetry in Low-Rank Adapters of Foundation Models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi +6
Parameter-efficient fine-tuning optimizes large, pre-trained foundation models by updating a subset of parameters; in this class, Low-Rank Adaptation (LoRA) is particularly effecti…
Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning
Rickard Brüel-Gabrielsson, Tongzhou Wang, Manel Baradad +1
Despite dropout's ubiquity in machine learning, its effectiveness as a form of data augmentation remains under-explored. We address two key questions: (i) When is dropout effective…
Rewiring with Positional Encodings for Graph Neural Networks
Rickard Brüel-Gabrielsson, Mikhail Yurochkin, Justin Solomon
Several recent works use positional encodings to extend the receptive fields of graph neural network (GNN) layers equipped with attention mechanisms. These techniques, however, ext…
A Topology Layer for Machine Learning
Rickard Brüel-Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath +3
Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning. We present a differentiable topolog…
Topological Approaches to Deep Learning
Gunnar Carlsson, Rickard Brüel Gabrielsson
We perform topological data analysis on the internal states of convolutional deep neural networks to develop an understanding of the computations that they perform. We apply this u…