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transferlearning

jindongwang / transferlearning

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Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

machine-learning deep-learning paper survey style-transfer theory transfer-learning papers representation-learning unsupervised-learning tutorial-code domain-adaptation generalization transferlearning meta-learning few-shot few-shot-learning self-supervised-learning domain-generalization domain-adaption
  • Updated Jul 5, 2022
  • Python

automl / auto-sklearn

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[ERROR] asyncio.exceptions.CancelledError

8
xmyhhh
xmyhhh commented Jun 27, 2022

The program throws an error at runtime, how can this error be solved?

[ERROR] [2022-06-27 14:13:51,391:asyncio.events] 
Traceback (most recent call last):
  File "/home/xumaoyuan/.virtualenvs/lib/python3.8/site-packages/distributed/utils.py", line 761, in wrapper
    return await func(*args, **kwargs)
  File "/home/xumaoyuan/.virtualenvs/lib/python3.8/site-packages/distributed/clie
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guan-yuan / awesome-AutoML-and-Lightweight-Models

Star 774

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

tensorflow pytorch hyperparameter-optimization awesome-list quantization nas automl model-compression neural-architecture-search meta-learning architecture-search quantized-training model-acceleration automated-feature-engineering quantized-neural-network
  • Updated Jun 19, 2021

omerbsezer / Reinforcement_learning_tutorial_with_demo

Star 555

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

machine-learning tutorial reinforcement-learning deep-reinforcement-learning q-learning pomdps policy-gradient sarsa a3c dynamic-programming imitation-learning dyna td-learning actor-critic meta-learning
  • Updated Jan 22, 2019
  • Jupyter Notebook

openml / openml-python

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Star 243
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Report minimal evaluation results in run printout

joaquinvanschoren
joaquinvanschoren commented Jun 24, 2022

Description

When doing print(run), the result is only some basic info like the task type ID. It shows no aggregated evaluation information. It would be nice if that could be added to the printout.

Alternatively, add a function that returns a dictionary with the local evaluation results.
If you currently run a run locally (not downloaded from OpenML), run.evaluations is None.
`run.fold

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