We lifted 13 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| li-yun-chen/spd-ddpm | canonical | 11 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| adjust_learning_rate | Ran | li-yun-chen/spd-ddpm/condition/ddpm.py pointer only (licence: NONE) · get_code("1c854ffce06dbac4") |
| betas_for_alpha_bar | Ran | li-yun-chen/spd-ddpm/uncondition/ddpm.py pointer only (licence: NONE) · get_code("d07c1a93d2fa327d") |
| fro | Ran | li-yun-chen/spd-ddpm/exp2/conditional_dis.py pointer only (licence: NONE) · get_code("5ba4fbd489d1601f") |
| generate_init | Ran | li-yun-chen/spd-ddpm/uncondition/generate_train_sample_setting1.py pointer only (licence: NONE) · get_code("d5dd04635d4e6f5e") |
| remove_subtensors_with_nan | Ran | li-yun-chen/spd-ddpm/uncondition/ddpm.py pointer only (licence: NONE) · get_code("ceb7b714828f151f") |
| row_to_matrix | Ran | li-yun-chen/spd-ddpm/exp1/heatplot.py pointer only (licence: NONE) · get_code("2c79ca7b063199ab") |
| select_rows_from_csv | Ran | li-yun-chen/spd-ddpm/exp1/heatplot.py pointer only (licence: NONE) · get_code("dccfa3edebee5d5b") |
| spd_dis | Ran | li-yun-chen/spd-ddpm/condition/support_function.py pointer only (licence: NONE) · get_code("a4413dcb7b30cc69") |
| spd_minus | Ran | li-yun-chen/spd-ddpm/condition/support_function.py pointer only (licence: NONE) · get_code("ccfceecb8fd4787a") |
| spd_plus | Ran | li-yun-chen/spd-ddpm/condition/support_function.py pointer only (licence: NONE) · get_code("52011d088aa4ca91") |
| tensor_power | Ran | li-yun-chen/spd-ddpm/exp2/conditional_dis.py pointer only (licence: NONE) · get_code("0578b08a27d3103e") |
| one_param | Not yet run | li-yun-chen/spd-ddpm/condition/SPD_net.py pointer only (licence: NONE) · get_code("4b602b0867bad430") |
| symmetric | Not yet run | li-yun-chen/spd-ddpm/condition/SPD_net.py pointer only (licence: NONE) · get_code("e9b42bb505335112") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Symmetric positive definite~(SPD) matrices have shown important value and applications in statistics and machine learning, such as FMRI analysis and traffic prediction. Previous works on SPD matrices mostly focus on discriminative models, where predictions are made directly on $E(X|y)$, where $y$ is a vector and $X$ is an SPD matrix. However, these methods are challenging to handle for large-scale data, as they need to access and process the whole data. In this paper, inspired by denoising diffusion probabilistic model~(DDPM), we propose a novel generative model, termed SPD-DDPM, by introducing Gaussian distribution in the SPD space to estimate $E(X|y)$. Moreover, our model is able to estimate $p(X)$ unconditionally and flexibly without giving $y$. On the one hand, the model conditionally learns $p(X|y)$ and utilizes the mean of samples to obtain $E(X|y)$ as a prediction. On the other hand, the model unconditionally learns the probability distribution of the data $p(X)$ and generates samples that conform to this distribution. Furthermore, we propose a new SPD net which is much deeper than the previous networks and allows for the inclusion of conditional factors. Experiment results on toy data and real taxi data demonstrate that our models effectively fit the data distribution both unconditionally and unconditionally and provide accurate predictions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.08200")
get_code_for_paper("2312.08200")
have("2312.08200")
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