Datasets:
Victor
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Add description for isnnn_400.seed01 dataset.
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README.md
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@@ -15,6 +15,11 @@ configs:
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path: isnnn_400/seed_01/d_train.parquet
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- split: test
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path: isnnn_400/seed_01/d_test.parquet
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- config_name: isnnn_400.seed01.gt_fim
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data_files:
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- split: test
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path: isnnn_400/seed_10/gt_fim.parquet
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---
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# FIM-Estimation
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In a FIM-Estimation task, the input consists of the following components:
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1. A dataset of the form $\mathcal{D}_ {train} = \{(\mathbf{\lambda}_ i, x_ i)\}_ {i=1}^{\left|\mathcal{D}\right|}$, where $\vlambda_ i$ is a point in the parameter space and $x_ i$ is a sample.
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2. The structure of the samples $x_ i$. For example, if $x_ i\in\{0,1\}^n$ is a bitstring representing a measurement of a quantum system on a lattice with $n$ sites, then the said structure is the lattice and the correspondence between the bitstring bits and the lattice sites.
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3. Optionally, additional information about the statistical manifold. This could involve, e.g., the symmetries of the system used to generate the statistical manifold.
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The task is to estimate Fisher Information Metric as a matrix-valued function of parameter $\mathbf{\lambda}$.
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We (TODO:plan to) present datasets corresponding to 6 statistical manifolds: `XXZ300_Z`, `FIL24`, etc.
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The data is stored as follows:
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`data/xxz300_z` contains the data for `XXZ300_Z` datasets. Within that directory, there are subdirectories of the form `seed_??` each containing a dataset describing the same statistical manifold, but generated using a different seed (e.g. `seed_05`). We have `seed_05/d_train.parquet`, which is the dataset as described in #1 above. `seed_05/d_test.parquet` is a hold-out dataset which should not be used in estimation of $\mathbf{\lambda}$ (not even for hyperparameter tuning).
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path: isnnn_400/seed_01/d_train.parquet
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- split: test
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path: isnnn_400/seed_01/d_test.parquet
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description: >-
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Samples for IsNNN400 statistical manifold generated with seed=1.
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`sample` column is formatted as bytearray of length 50 bytes (400 bits)
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and represents a spin configuration of 20x20 lattice
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with bit 0 corresponding to Z=+1 and bit 1 corresponding to Z=-1.
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- config_name: isnnn_400.seed01.gt_fim
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data_files:
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- split: test
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path: isnnn_400/seed_10/gt_fim.parquet
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---
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# Dataset Card for FIM-Estimation
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## Dataset Summary
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In a FIM-Estimation task, the input consists of the following components:
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1. A dataset of the form $\mathcal{D}_ {train} = \{(\mathbf{\lambda}_ i, x_ i)\}_ {i=1}^{\left|\mathcal{D}\right|}$, where $\vlambda_ i$ is a point in the parameter space and $x_ i$ is a sample.
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2. The structure of the samples $x_ i$. For example, if $x_ i\in\{0,1\}^n$ is a bitstring representing a measurement of a quantum system on a lattice with $n$ sites, then the said structure is the lattice and the correspondence between the bitstring bits and the lattice sites.
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3. Optionally, additional information about the statistical manifold. This could involve, e.g., the symmetries of the system used to generate the statistical manifold.
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The task is to estimate Fisher Information Metric (FIM) as a matrix-valued function of parameter $\mathbf{\lambda}$.
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In this dataset we present multiple such tasks (parameterised by task name and random seed). E.g. in order to solve the IsNNN400 task with seed=4 one would download config `isnnn_400.seed04`, split `train`, estimate the FIM, and compare the results with `isnnn_400.seed04.gt_fim`.
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## Additional information
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We (TODO:plan to) present datasets corresponding to 6 statistical manifolds: `XXZ300_Z`, `FIL24`, etc.
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The data is stored as follows:
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`data/xxz300_z` contains the data for `XXZ300_Z` datasets. Within that directory, there are subdirectories of the form `seed_??` each containing a dataset describing the same statistical manifold, but generated using a different seed (e.g. `seed_05`). We have `seed_05/d_train.parquet`, which is the dataset as described in #1 above. `seed_05/d_test.parquet` is a hold-out dataset which should not be used in estimation of $\mathbf{\lambda}$ (not even for hyperparameter tuning).
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