62 citations · 117 across the 12 of their papers we have counts for
13 papers
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
Hannah Lin, Martin Maas, Maximilian Roquemore +17
This paper introduces ECO, a system that automatically modifies source code to improve performance at scale. ECO overcomes the localization problem by combining fleet-wide continuo…
Adaptive Conditional Quantile Neural Processes
Peiman Mohseni, Nick Duffield, Bani Mallick +1
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predic…
MoReL: Multi-omics Relational Learning
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield +1
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of…
Bayesian Graph Contrastive Learning
Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali +3
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods…
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +2
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying…
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki +4
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The p…