23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
- University of WashingtonUS19 papers
- Georgia Institute of TechnologyUS17 papers
159 papers · 1 filter
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
Abhilash Neog, Sepideh Fatemi, Medha Sawhney +9
Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs. While machine learning methods have been rece…
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
Sudheer Tubati, Amit Goyal
Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and l…
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
Luan Pham, Victor Nicolet, Joey Dodds +2
Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving ev…
ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration
Mengting Ai, Tianxin Wei, Yifan Chen +7
Mixture-of-Experts (MoE) Transformer, the backbone architecture of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for eac…
Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
Naveen Edapurath Vijayan
This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data fro…
LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
Peiyao Xiao, Chaosheng Dong, Shaofeng Zou +1
Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced sol…