torchgfn: A PyTorch GFlowNet library
Fuente:
arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910060310953984 |
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| author | Viviano, Joseph D. Younis, Omar G. Choi, Sanghyeok Schmidt, Victor Bengio, Yoshua Lahlou, Salem |
| author_facet | Viviano, Joseph D. Younis, Omar G. Choi, Sanghyeok Schmidt, Victor Bengio, Yoshua Lahlou, Salem |
| contents | The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library that facilitates the testing of new features (e.g., training losses and training policies) against standard benchmark implementations, or on a set of common environments. We present torchgfn, a PyTorch library that aims to address this need. Its core contribution is a modular and decoupled architecture which treats environments, neural network modules, and training objectives as interchangeable components. This provides users with a simple yet powerful API to facilitate rapid prototyping and novel research. Multiple examples are provided, replicating and unifying published results. The library is available on GitHub (https://github.com/GFNOrg/torchgfn) and on pypi (https://pypi.org/project/torchgfn/). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_14594 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | torchgfn: A PyTorch GFlowNet library Viviano, Joseph D. Younis, Omar G. Choi, Sanghyeok Schmidt, Victor Bengio, Yoshua Lahlou, Salem Machine Learning The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library that facilitates the testing of new features (e.g., training losses and training policies) against standard benchmark implementations, or on a set of common environments. We present torchgfn, a PyTorch library that aims to address this need. Its core contribution is a modular and decoupled architecture which treats environments, neural network modules, and training objectives as interchangeable components. This provides users with a simple yet powerful API to facilitate rapid prototyping and novel research. Multiple examples are provided, replicating and unifying published results. The library is available on GitHub (https://github.com/GFNOrg/torchgfn) and on pypi (https://pypi.org/project/torchgfn/). |
| title | torchgfn: A PyTorch GFlowNet library |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.14594 |