Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels

1 code implementation • 3 Sep 2022 • Zhongchen Ma, Lisha Li, Qirong Mao, Songcan Chen

However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning.

Contrastive Learning Missing Labels +3

Massively Parallel Hyperparameter Tuning

no code implementations • ICLR 2018 • Lisha Li, Kevin Jamieson, Afshin Rostamizadeh, Katya Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs.

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

17 code implementations • 21 Mar 2016 • Lisha Li, Kevin Jamieson, Giulia Desalvo, Afshin Rostamizadeh, Ameet Talwalkar

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters.

Bayesian Optimization Hyperparameter Optimization