deep-kernel-transfer
FSL-Mate
| deep-kernel-transfer | FSL-Mate | |
|---|---|---|
| 1 | 1 | |
| 190 | 1,645 | |
| 1.6% | - | |
| 10.0 | 4.8 | |
| over 2 years ago | 2 months ago | |
| Python | Python | |
| - | MIT License |
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deep-kernel-transfer
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What approach to take predicting a simple data stream?
Interesting approach to small datasets. Here is an implementation I'll look at: https://github.com/BayesWatch/deep-kernel-transfer
FSL-Mate
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Anyone knows a good online material to understand and implement few shot learning?
In regards to understanding, I'm not entirely sure which part you need help with. A few shots learning simply means that instead of a lot of data per class (e.g. 1k+), you only have a few (e.g 8). You can take any ML algorithm that works and just train it with less examples to make it a few shot learning algorithm. Not all will work well, but it will still be a few shot learning algorithm. Try starting with a survey rather than a specific paper. Maybe this, or this
What are some alternatives?
ipme - An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer".
zshot - Zero and Few shot named entity & relationships recognition
fortuna - A Library for Uncertainty Quantification.
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
rcps - Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"
learn2learn - A PyTorch Library for Meta-learning Research
MLBox - MLBox is a powerful Automated Machine Learning python library.
DeepKE - [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
heinsen_tree - Reference implementation of "Tree Methods for Hierarchical Classification in Parallel" (Heinsen, 2022) in PyTorch.
Papers-in-100-Lines-of-Code - Implementation of papers in 100 lines of code.
Genome - Genome Network Ala Neural Network
PaddleViT - :robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+