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Found 7 papers, 6 papers with code
Task-Distributionally Robust Data-Free Meta-Learning
TDS leads to a biased meta-learner because of the skewed task distribution towards newly generated tasks.
Exploring Model Transferability through the Lens of Potential Energy
The experimental results on 10 downstream tasks and 12 self-supervised models demonstrate that our approach can seamlessly integrate into existing ranking techniques and enhance their performances, revealing its effectiveness for the model selection task and its potential for understanding the mechanism in transfer learning.
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
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.
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
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.
Modeling Uncertain Feature Representation for Domain Generalization
In this paper, we improve the network generalization ability by modeling domain shifts with uncertainty (DSU), i. e., characterizing the feature statistics as uncertain distributions during training.
mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning.
Qudits and high-dimensional quantum computing
Qudit is a multi-level computational unit alternative to the conventional 2-level qubit.
Quantum Physics
