23 citations · 146 across the 33 of their papers we have counts for
4 papers · 1 filter
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Zixuan Hu, Li Shen, Zhenyi Wang +3
Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing…
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
Zixuan Hu, Li Shen, Zhenyi Wang +3
The goal of data-free meta-learning is to learn useful prior knowledge from a collection of pre-trained models without accessing their training data. However, existing works only s…
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
Manyi Zhang, Xuyang Zhao, Jun Yao +2
Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues seriously hurt the generalization of trained models. It is hence significant to address th…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…