3 citations · 3 across the 5 of their papers we have counts for
5 papers
ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models
Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji +1
Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what ha…
NetSSM: Multi-Flow and State-Aware Network Trace Generation using State Space Models
Andrew Chu, Xi Jiang, Shinan Liu +4
Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high…
Feasibility of State Space Models for Network Traffic Generation
Andrew Chu, Xi Jiang, Shinan Liu +4
Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility…
Beyond Data Points: Regionalizing Crowdsourced Latency Measurements
Taveesh Sharma, Paul Schmitt, Francesco Bronzino +2
Despite significant investments in access network infrastructure, universal access to high-quality Internet connectivity remains a challenge. Policymakers often rely on large-scale…
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation
Xi Jiang, Shinan Liu, Aaron Gember-Jacobson +4
Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concern…