21 citations · 44 across the 4 of their papers we have counts for
5 papers
XLA from the Trenches: Correctness on TPU Workloads at Scale
Ignacio Cano, Amir Saadat, Rishipal Singh Bhatia +19
Accelerating modern foundational models has pushed domain-specific compilers toward increasingly complex, multi-tiered optimization pipelines. However, this heavy optimization focu…
FitNets: An Adaptive Framework to Learn Accurate Traffic Distributions
Alexander Dietmüller, Albert Gran Alcoz, Laurent Vanbever
Learning precise distributions of traffic features (e.g., burst sizes, packet inter-arrival time) is still a largely unsolved problem despite being critical for management tasks su…
On Sample Selection for Continual Learning: a Video Streaming Case Study
Alexander Dietmüller, Romain Jacob, Laurent Vanbever
Machine learning (ML) is a powerful tool to model the complexity of communication networks. As networks evolve, we cannot only train once and deploy. Retraining models, known as co…
A new hope for network model generalization
Alexander Dietmüller, Siddhant Ray, Romain Jacob +1
Generalizing machine learning (ML) models for network traffic dynamics tends to be considered a lost cause. Hence for every new task, we design new models and train them on model-s…
pForest: In-Network Inference with Random Forests
Coralie Busse-Grawitz, Roland Meier, Alexander Dietmüller +2
When classifying network traffic, a key challenge is deciding when to perform the classification, i.e., after how many packets. Too early, and the decision basis is too thin to cla…