3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.DC2025
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Rickard Brüel-Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj +4
Fine-tuning large language models (LLMs) with low-rank adaptations (LoRAs) has become common practice, often yielding numerous copies of the same LLM differing only in their LoRA u…