171 citations · 234 across the 11 of their papers we have counts for
9 papers · 1 filter
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we re…
Leveraging Organizational Resources to Adapt Models to New Data Modalities
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut +7
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e.g., a new v…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann +5
Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to app…
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Justin Chen, Edward Gan, Kexin Rong +2
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful w…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…