CARLA
ai-music
CARLA | ai-music | |
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2 | 5 | |
269 | 9 | |
2.2% | - | |
0.0 | 0.0 | |
8 months ago | over 2 years ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 only |
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CARLA
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[R] CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Abstract: Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favourable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available open–source implementations, the decision in favour of certain models is primarily based on what is readily available. Going forward – to guarantee meaningful comparisons across explanation methods – we present CARLA (Counterfactual And Recourse Library), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open sourced CARLA and our experimental results on GitHub, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.
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University of Tübingen Researchers Open-Source ‘CARLA’, A Python Library for Benchmarking Counterfactual Explanation Methods Across Data Sets and Machine Learning Models
4 Min Read| Paper | Github
ai-music
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I can't manage to make training work... please help me out
I m a newbie when it comes to deep learning, but I am trying to use a code from git and train the network on my own data. however, it takes forever..it took 80 minutes for 1 epoch, and the number of epoches is 1000. i also tried reducing batch size and using google collab.. please,i dont get what i am doing wrong... at first i tried running on cpu,then on gpu,but i get OOM error even when changing parameters.. any help is appreciated. This is the code : https://github.com/markusaksli/ai-music
- I made a machine learning Final Fantasy OST MIDI generator
- [Project] I made a Transformer-Decoder Final Fantasy OST MIDI generator
- I made a Transformer-Decoder Final Fantasy OST MIDI generator
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