2 citations · 5 across the 5 of their papers we have counts for
6 papers
PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters
Isabelly Rocha, Nathaniel Morris, Lydia Y. Chen +3
DNN learning jobs are common in today's clusters due to the advances in AI driven services such as machine translation and image recognition. The most critical phase of these jobs…
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke +1
Robustness to label noise is a critical property for weakly-supervised classifiers trained on massive datasets. Robustness to label noise is a critical property for weakly-supervis…
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Amirmasoud Ghiassi, Robert Birke, Rui Han +1
Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant por…
QActor: On-line Active Learning for Noisy Labeled Stream Data
Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi +2
Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regu…
RAD: On-line Anomaly Detection for Highly Unreliable Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
Differential Approximation and Sprinting for Multi-Priority Big Data Engines
Robert Birke, Isabelly Rocha, Juan Perez +3
Today's big data clusters based on the MapReduce paradigm are capable of executing analysis jobs with multiple priorities, providing differential latency guarantees. Traces from pr…