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repository root. Lecture-specific commands, mathematical explanations, and
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references accompany the corresponding code.
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## Tests
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```bash
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python -m unittest discover -s tests -v
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python -m unittest discover -s lecture_4/tests -v
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python -m unittest discover -s lecture_5/tests -v
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python -m unittest discover -s lecture_6/tests -v
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python -m unittest discover -s lecture_7/tests -v
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```
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The Lecture 3 unit tests cover property annotations, scalarization weights, reward
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gradients, DDPM schedule indexing, and constrained decoding.
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Lecture 4 tests check reverse KL losses and guidance calculations. Lecture 5
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tests check the master equation, Fisher geometry, Gumbel path derivatives, and
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MH detailed balance. Lecture 6 checks map identities and JVPs, categorical
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teachers, GLASS conditioning, reward gradients, insertion clocks, and Brownian
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composition. Its end-to-end runner also verifies checkpoint reloads. Lecture 7 checks OT duality, bridge marginals and conditional paths, generator identities, matching targets, and control geometry, with 17 seeded checkpoint-reload checks.
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Run a short end-to-end check of every method from its
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lecture folder with `python run_all.py --quick`.
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## License
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The repository code is distributed under the
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[MIT License](https://huggingface.co/ChatterjeeLab/CIS6270/blob/main/LICENSE).
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The Lecture 2 U-Net checkpoint is included under the same license. The Lecture 3
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ESM-2 weights are downloaded from their original repository under the
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corresponding distribution terms.
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repository root. Lecture-specific commands, mathematical explanations, and
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references accompany the corresponding code.
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## License
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The repository code is distributed under the
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[MIT License](https://huggingface.co/ChatterjeeLab/CIS6270/blob/main/LICENSE).
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ESM-2 weights are downloaded from their original repository under the
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corresponding distribution terms.
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