Add xCEBRA implementation (AISTATS 2025) #225

gonlairo · 2025-02-17T20:20:56Z

xCEBRA

eXplainable CEBRA 🔎🦓

This PR adds the following features:

multiobjective solver -> fit multiple subspaces with a new API
attribution methods (via captum), including our new method, inverted neuron gradient
regularized contrastive learning using jacobian regularization (required for identifiable attribution maps, but also useful for regularizing training more generally); aka xCEBRA

This code supports the following paper:

https://openreview.net/forum?id=aGrCXoTB4P

@inproceedings{ schneider2025timeseries, title={Time-series attribution maps with regularized contrastive learning}, author={Steffen Schneider and Rodrigo Gonz{\'a}lez Laiz and Anastasiia Filippova and Markus Frey and Mackenzie W Mathis}, booktitle={The 28th International Conference on Artificial Intelligence and Statistics}, year={2025}, url={https://openreview.net/forum?id=aGrCXoTB4P} }

Abstract:

Gradient-based attribution methods aim to explain decisions of deep learning models, but so far lack identifiability guarantees. Here, we propose a method to generate attribution maps with identifiability guarantees by developing a regularized contrastive learning algorithm (RegCL) trained on time-series data. We show theoretically that RegCL has favorable properties for identifying the Jacobian matrix of the data generating process. Empirically, we demonstrate robust approximation of zero vs. non-zero entries in the ground-truth attribution map on synthetic datasets, and significant improvements across previous attribution methods based on feature ablation, Shapley values, and other gradient-based methods. Our work constitutes a first example of identifiable inference of time-series attribution maps, and opens avenues better understanding of time-series data, such as for neural dynamics and decision-processes within neural networks.

Outline of the Method:

Identifiable attribution maps for time-series data. Using time-series data (such as neural data
recorded during navigation, as depicted), our inference framework estimates the ground-truth Jacobian matrix
Jg (i.e., x is the observed neural data linked to latents z and c, where c is the explicit [auxiliary] behavioral
variable that would be linked to grid cells) by identifying the inverse data generation process up to a linear
indeterminacy L. Then, we estimate the Jacobian Jf of the encoder model (f) by minimizing a generalized
InfoNCE objective. Inverting this Jacobian J+f , which approximates Jg, allows us to construct the attributions.

* Add multiobjective solver and regularized training * Add example for multiobjective training * Add jacobian regularizer and SAM * update license headers * add api draft for multiobjective training * add all necessary modules to run the complete xcebra pipeline * add notebooks to reproduce xcebra pipeline * add first working notebook * add notebook with hybrid learning * add notebook with creation of synthetic data * add notebook with hybrid training * add plot with R2 for different parts of the embedding * add new API * update api wrapper with more checks and messages * add tests and notebook with new api * merge xcebra into attribution * separate xcebra dataset from cebra * some minor refactoring of cebra dataset * separate xcebra loader from cebra * remove xcebra distributions from cebra * minor refactoring with distributions * separate xcebra criterions from cebra * minor refactoring on criterion * separate xcebra models/criterions/layers from cebra * refactoring multiobjective * more refactoring... * separate xcebra solvers from cebra * more refactoring * move xcebra to its own package * move more files into xcebra package * more files and remove changes with the registry * remove unncessary import * add folder structure * move back distributions * add missing init * remove wrong init * make loader and dataset run with new imports * making it run! * make attribution run * Run pre-commit * move xcebra repo one level up * update gitignore and add __init__ from data * add init to distributions * add correct init for attribution pacakge * add correct init for model package * fix remaining imports * fix tests * add examples back to xcebra repo * update imports from graphs_xcebra * add setup.py to create a package * update imports of graph_xcebra * update notebooks * Formatting code for submission Co-authored-by: Rodrigo Gonzalez <gonlairo@gmail.com> * move test into xcebra * Add README * move distributions back to main package * clean up examples * adapt tests * Add LICENSE * add train/eval notebook again * add notebook with clean results * rm synthetic data * change name from xcebra to regcl * change names of modules and adapt imports * change name from graphs_xcebra to synthetic_data * Integrate into CEBRA * Fix remaining imports and make notebook runnable * Add dependencies, add version flag * Remove synthetic data files * reset dockerfile, move vmf * apply pre-commit * Update notice * add some docstrings * Apply license headers * add new scd notebook * add notebook with scd --------- Co-authored-by: Steffen Schneider <stes@hey.com>

* bump version * update dockerfile * fix progress bar * remove outdated test * rename models

MMathisLab · 2025-02-17T21:02:28Z

Let's move demo /examples to https://github.com/AdaptiveMotorControlLab/CEBRA-demos :D

MMathisLab

I left comments throughout, thank you!!

setup.cfg

tests/test_models.py

Dockerfile

NOTICE.yml

cebra/attribution/attribution_models.py

cebra/solver/util.py

examples/synthetic_data.pkl

examples/train_and_evaluate.ipynb

examples/train_and_evaluate_scd.ipynb

stes

I made a few edits:

The old MultiobjectiveSolver is now again accessible (important for the hybrid model in Fig 2 in CEBRA paper), it is now called LegacyMultiobjectiveSolver. For an end user using the sklearn API with hybrid=True, this can now still be used.
Reverted some changes from the research code base not important for release
added additional tests, incl. integration tests from the notebooks
(resolved some additional review comments)

tests/test_models.py

setup.cfg

cebra/solver/util.py

.github/workflows/build.yml

cebra/data/datasets.py

cebra/datasets/__init__.py

stes · 2025-02-19T01:17:39Z

next:

check docstring coverage and write docstrings
remove ratinabox==1.8 and ephysiopy==1.9.62 deps for xcebra? I think not needed for core functionality. when dropping, could be added to the docs somewhere
fix and consolidate naming of some of the newly added classes
cebra.distributions.DeltaVMFDistribution seems missing, check!

stes · 2025-02-19T01:19:28Z

tests build!

.github/workflows/docs.yml

cebra/models/multiobjective.py

stes · 2025-04-18T11:49:32Z

Merged #241 now, diff should be clean against main.

MMathisLab

a few more comments regarding naming and compatability

MMathisLab · 2025-04-19T13:59:57Z

.github/workflows/build.yml

 python -m pip install --upgrade pip setuptools wheel
 python -m pip install torch==${{ matrix.torch-version }} --extra-index-url https://download.pytorch.org/whl/cpu
- pip install '.[dev,datasets,integrations]'
+ pip install '.[dev,datasets,integrations,xcebra]'


xcebra should not be it's own sub-install

MMathisLab · 2025-04-19T14:00:28Z

Dockerfile

 WORKDIR /build
 COPY --from=wheel /build/dist/${WHEEL} .
-RUN pip install --no-cache-dir ${WHEEL}'[dev,integrations,datasets]'
+RUN pip install --no-cache-dir ${WHEEL}'[dev,integrations,datasets,xcebra]'


remove xcebra; should be in main cebra package

cebra/data/single_session.py

cebra/registry.py

cebra/solver/base.py

docs/source/api/pytorch/models.rst

MMathisLab · 2025-04-19T14:06:33Z

setup.cfg

+xcebra =
+ captum
+ cvxpy
+ ratinabox==1.8


remove xcebra and move to integrations

stes

fixed some/most of the review comments

stes · 2025-04-20T17:59:34Z

.github/workflows/build.yml

 python -m pip install --upgrade pip setuptools wheel
 python -m pip install torch==${{ matrix.torch-version }} --extra-index-url https://download.pytorch.org/whl/cpu
- pip install '.[dev,datasets,integrations]'
+ pip install '.[dev,datasets,integrations,xcebra]'


stes · 2025-04-20T18:00:33Z

Dockerfile

 WORKDIR /build
 COPY --from=wheel /build/dist/${WHEEL} .
-RUN pip install --no-cache-dir ${WHEEL}'[dev,integrations,datasets]'
+RUN pip install --no-cache-dir ${WHEEL}'[dev,integrations,datasets,xcebra]'


cebra/data/single_session.py

cebra/solver/base.py

stes · 2025-04-20T18:23:10Z

cebra/solver/single_session.py

 class SingleSessionHybridSolver(abc_.MultiobjectiveSolver):
 """Single session training, contrasting neural data against behavior."""

+ log: Dict = dataclasses.field(default_factory=lambda: ({


removed, unclear why added here

docs/source/api/pytorch/models.rst

stes · 2025-04-20T18:27:34Z

setup.cfg

+xcebra =
+ captum
+ cvxpy
+ ratinabox==1.8


stes · 2025-04-20T18:27:37Z

setup.cfg

+ cvxpy
+ ratinabox==1.8
+ scikit-image
+ ephysiopy==1.9.62


stes · 2025-04-20T20:22:28Z

ok, remaining thing is better/more docs. I'll make some progress on that tomorrow!
(the failing test is docs coverage)

- setting coverage threshold to 80% to not delay good code being made public. In the near future this can be fixed and raised again to 90%.

* Add multiobjective solver and regularized training (#783) * Add multiobjective solver and regularized training * Add example for multiobjective training * Add jacobian regularizer and SAM * update license headers * add api draft for multiobjective training * add all necessary modules to run the complete xcebra pipeline * add notebooks to reproduce xcebra pipeline * add first working notebook * add notebook with hybrid learning * add notebook with creation of synthetic data * add notebook with hybrid training * add plot with R2 for different parts of the embedding * add new API * update api wrapper with more checks and messages * add tests and notebook with new api * merge xcebra into attribution * separate xcebra dataset from cebra * some minor refactoring of cebra dataset * separate xcebra loader from cebra * remove xcebra distributions from cebra * minor refactoring with distributions * separate xcebra criterions from cebra * minor refactoring on criterion * separate xcebra models/criterions/layers from cebra * refactoring multiobjective * more refactoring... * separate xcebra solvers from cebra * more refactoring * move xcebra to its own package * move more files into xcebra package * more files and remove changes with the registry * remove unncessary import * add folder structure * move back distributions * add missing init * remove wrong init * make loader and dataset run with new imports * making it run! * make attribution run * Run pre-commit * move xcebra repo one level up * update gitignore and add __init__ from data * add init to distributions * add correct init for attribution pacakge * add correct init for model package * fix remaining imports * fix tests * add examples back to xcebra repo * update imports from graphs_xcebra * add setup.py to create a package * update imports of graph_xcebra * update notebooks * Formatting code for submission Co-authored-by: Rodrigo Gonzalez <gonlairo@gmail.com> * move test into xcebra * Add README * move distributions back to main package * clean up examples * adapt tests * Add LICENSE * add train/eval notebook again * add notebook with clean results * rm synthetic data * change name from xcebra to regcl * change names of modules and adapt imports * change name from graphs_xcebra to synthetic_data * Integrate into CEBRA * Fix remaining imports and make notebook runnable * Add dependencies, add version flag * Remove synthetic data files * reset dockerfile, move vmf * apply pre-commit * Update notice * add some docstrings * Apply license headers * add new scd notebook * add notebook with scd --------- Co-authored-by: Steffen Schneider <stes@hey.com> * Fix tests * bump version * update dockerfile * fix progress bar * remove outdated test * rename models * Apply fixes to pass ruff tests * Fix typos * Update license headers, fix additional ruff errors * remove unused comment * rename regcl in codebase * change regcl name in dockerfile * Improve attribution module * Fix imports name naming * add basic integration test * temp disable of binary check * Add legacy multiobjective model for backward compat * add synth import back in * Fix docstrings and type annot in cebra/models/jacobian_regularizer.py * add xcebra to tests * add missing cvxpy dep * fix docstrings * more docstrings to fix attr error * Improve build setup for docs * update pydata theme options * Add README for docs folder * Fix demo notebook build * Finish build setup * update git workflow * Move demo notebooks to CEBRA-demos repo See AdaptiveMotorControlLab/CEBRA-demos#28 * revert unneeded changes in solver * formatting in solver * further minimize solver diff * Revert unneeded updates to the solver * fix citation * fix docs build, missing refs * remove file dependency from xcebra int test * remove unneeded change in registry * update gitignore * update docs * exclude some assets * include binary file check again * add timeout to workflow * add timeout also to docs build * switch build back to sphinx for gh actions * pin sphinx version in setup.cfg * attempt workflow fix * attempt to fix build workflow * update to sphinx-build * fix build workflow * fix indent error * fix build system * revert demos to main * adapt workflow for testing * bump version to 0.6.0rc1 * format imports * docs writing * enable build on dev branch * fix some review comments * extend multiobjective docs * Set version to alpha * make tempdir platform independent * Remove ratinabox and ephysiopy as deps * Apply review comments * Update Makefile - setting coverage threshold to 80% to not delay good code being made public. In the near future this can be fixed and raised again to 90%. --------- Co-authored-by: Steffen Schneider <stes@hey.com> Co-authored-by: Steffen Schneider <steffen.schneider@helmholtz-munich.de> Co-authored-by: Mackenzie Mathis <mathis@rowland.harvard.edu>

@stes

* first proposal for batching in tranform method * first running version of padding with batched inference * start tests * add pad_before_transform to fit function and add support for convolutional models in _transform * remove print statements * first passing test * add support for hybrid models * rewrite transform in sklearn API * baseline version of a torch.Datset * move batching logic outside solver * move functionality to base file in solver and separate in functions * add test_select_model for single session * add checks and test for _process_batch * add test_select_model for multisession * make self.num_sessions compatible with single session training * improve test_batched_transform_singlesession * make it work with small batches * make test with multisession work * change to torch padding * add argument to sklearn api * add torch padding to _transform * convert to torch if numpy array as inputs * add distinction between pad with data and pad with zeros and modify test accordingly * differentiate between data padding and zero padding * remove float16 * change argument position * clean test * clean test * Fix warning * Improve modularity remove duplicate code and todos * Add tests to solver * Remove unused import in solver/utils * Fix test plot * Add some coverage * Fix save/load * Remove duplicate configure_for in multi dataset * Make save/load cleaner * Fix codespell errors * Fix docs compilation errors * Fix formatting * Fix extra docs errors * Fix offset in docs * Remove attribute ref * Add review updates * apply ruff auto-fixes * Concatenate last batches for batched inference (#200) * Concatenate last to batches for batched inference * Add test case * Fix linting errors in tests (#188) * apply auto-fixes * Fix linting errors in tests/ * Fix version check * Fix `scikit-learn` reference in conda environment files (#195) * Add support for new __sklearn_tags__ (#205) * Add support for new __sklearn_tags__ * fix inheritance order * Add more tests * fix added test * Update workflows to actions/setup-python@v5, actions/cache@v4 (#212) * Fix deprecation warning force_all_finite -> ensure_all_finite for sklearn>=1.6 (#206) * Add tests to check legacy model loading (#214) * Add improved goodness of fit implementation (#190) * Started implementing improved goodness of fit implementation * add tests and improve implementation * Fix examples * Fix docstring error * Handle batch size = None for goodness of fit computation * adapt GoF implementation * Fix docstring tests * Update docstring for goodness_of_fit_score Co-authored-by: Célia Benquet <32598028+CeliaBenquet@users.noreply.github.com> * add annotations to goodness_of_fit_history Co-authored-by: Célia Benquet <32598028+CeliaBenquet@users.noreply.github.com> * fix typo Co-authored-by: Célia Benquet <32598028+CeliaBenquet@users.noreply.github.com> * improve err message Co-authored-by: Célia Benquet <32598028+CeliaBenquet@users.noreply.github.com> * make numerical test less conversative * Add tests for exception handling * fix tests --------- Co-authored-by: Célia Benquet <32598028+CeliaBenquet@users.noreply.github.com> * Support numpy 2, upgrade tests to support torch 2.6 (#221) * Drop numpy constraint * Implement workaround for pytables * better error message * pin numpy only for python 3.9 * update dependencies * Upgrade torch version * Fix based on python version * Add support for torch.load with weights_only=True * Implement safe loading for torch models starting in torch 2.6 * Fix windows specs * fix docstring * Revert changes to loading logic * Release 0.5.0rc1 (#189) * Make bump_version script runnable on MacOS * Bump version to 0.5.0rc1 * fix minor formatting issues * remove commented code --------- Co-authored-by: Mackenzie Mathis <mathis@rowland.harvard.edu> * Fix pypi action (#222) * force packaging upgrade to 24.2 for twine * Bump version to 0.5.0rc2 * remove universal compatibility option * revert tag * adapt files to new wheel name due to py3 * Update base.py (#224) This is a lazy solution to #223 * Change max consistency value to 100 instead of 99 (#227) * Change text consistency max from 99 to 100 * Update cebra/integrations/matplotlib.py --------- Co-authored-by: Mackenzie Mathis <mackenzie.mathis@epfl.ch> Co-authored-by: Steffen Schneider <steffen@bethgelab.org> * Update assets.py --> force check for parent dir (#230) Update assets.py - mkdir was failing in 0.5.0rc1; attempt to fix * User docs minor edit (#229) * user note added to usage.rst - link added * Update usage.rst - more detailed note on the effect of temp. * Update usage.rst - add in temp to demo model - testout put thanks @stes * Update docs/source/usage.rst Co-authored-by: Steffen Schneider <stes@hey.com> * Update docs/source/usage.rst Co-authored-by: Steffen Schneider <stes@hey.com> * Update docs/source/usage.rst Co-authored-by: Steffen Schneider <stes@hey.com> --------- Co-authored-by: Steffen Schneider <stes@hey.com> * General Doc refresher (#232) * Update installation.rst - python 3.9+ * Update index.rst * Update figures.rst * Update index.rst -typo fix * Update usage.rst - update suggestion on data split * Update docs/source/usage.rst Co-authored-by: Steffen Schneider <stes@hey.com> * Update usage.rst - indent error fixed * Update usage.rst - changed infoNCE to new GoF * Update usage.rst - finx numpy() doctest * Update usage.rst - small typo fix (label) * Update usage.rst --------- Co-authored-by: Steffen Schneider <stes@hey.com> * render plotly in our docs, show code/doc version (#231) * Update layout.html (#233) * Update conf.py (#234) - adding link to new notebook icon * Refactoring setup.cfg (#228) * Home page landing update (#235) * website refresh * v0.5.0 (#238) * Upgrade docs build (#241) * Improve build setup for docs * update pydata theme options * Add README for docs folder * Fix demo notebook build * Finish build setup * update git workflow * add timeout to workflow * add timeout also to docs build * switch build back to sphinx for gh actions * attempt to fix build workflow * update to sphinx-build * fix build workflow * fix indent error * fix build system * revert demos to main * increase timeout to 30 * Allow indexing of the cebra docs (#242) * Allow indexing of the cebra docs * Fix docs workflow * Fix broken docs coverage workflows (#246) * Add xCEBRA implementation (AISTATS 2025) (#225) * Add multiobjective solver and regularized training (#783) * Add multiobjective solver and regularized training * Add example for multiobjective training * Add jacobian regularizer and SAM * update license headers * add api draft for multiobjective training * add all necessary modules to run the complete xcebra pipeline * add notebooks to reproduce xcebra pipeline * add first working notebook * add notebook with hybrid learning * add notebook with creation of synthetic data * add notebook with hybrid training * add plot with R2 for different parts of the embedding * add new API * update api wrapper with more checks and messages * add tests and notebook with new api * merge xcebra into attribution * separate xcebra dataset from cebra * some minor refactoring of cebra dataset * separate xcebra loader from cebra * remove xcebra distributions from cebra * minor refactoring with distributions * separate xcebra criterions from cebra * minor refactoring on criterion * separate xcebra models/criterions/layers from cebra * refactoring multiobjective * more refactoring... * separate xcebra solvers from cebra * more refactoring * move xcebra to its own package * move more files into xcebra package * more files and remove changes with the registry * remove unncessary import * add folder structure * move back distributions * add missing init * remove wrong init * make loader and dataset run with new imports * making it run! * make attribution run * Run pre-commit * move xcebra repo one level up * update gitignore and add __init__ from data * add init to distributions * add correct init for attribution pacakge * add correct init for model package * fix remaining imports * fix tests * add examples back to xcebra repo * update imports from graphs_xcebra * add setup.py to create a package * update imports of graph_xcebra * update notebooks * Formatting code for submission Co-authored-by: Rodrigo Gonzalez <gonlairo@gmail.com> * move test into xcebra * Add README * move distributions back to main package * clean up examples * adapt tests * Add LICENSE * add train/eval notebook again * add notebook with clean results * rm synthetic data * change name from xcebra to regcl * change names of modules and adapt imports * change name from graphs_xcebra to synthetic_data * Integrate into CEBRA * Fix remaining imports and make notebook runnable * Add dependencies, add version flag * Remove synthetic data files * reset dockerfile, move vmf * apply pre-commit * Update notice * add some docstrings * Apply license headers * add new scd notebook * add notebook with scd --------- Co-authored-by: Steffen Schneider <stes@hey.com> * Fix tests * bump version * update dockerfile * fix progress bar * remove outdated test * rename models * Apply fixes to pass ruff tests * Fix typos * Update license headers, fix additional ruff errors * remove unused comment * rename regcl in codebase * change regcl name in dockerfile * Improve attribution module * Fix imports name naming * add basic integration test * temp disable of binary check * Add legacy multiobjective model for backward compat * add synth import back in * Fix docstrings and type annot in cebra/models/jacobian_regularizer.py * add xcebra to tests * add missing cvxpy dep * fix docstrings * more docstrings to fix attr error * Improve build setup for docs * update pydata theme options * Add README for docs folder * Fix demo notebook build * Finish build setup * update git workflow * Move demo notebooks to CEBRA-demos repo See AdaptiveMotorControlLab/CEBRA-demos#28 * revert unneeded changes in solver * formatting in solver * further minimize solver diff * Revert unneeded updates to the solver * fix citation * fix docs build, missing refs * remove file dependency from xcebra int test * remove unneeded change in registry * update gitignore * update docs * exclude some assets * include binary file check again * add timeout to workflow * add timeout also to docs build * switch build back to sphinx for gh actions * pin sphinx version in setup.cfg * attempt workflow fix * attempt to fix build workflow * update to sphinx-build * fix build workflow * fix indent error * fix build system * revert demos to main * adapt workflow for testing * bump version to 0.6.0rc1 * format imports * docs writing * enable build on dev branch * fix some review comments * extend multiobjective docs * Set version to alpha * make tempdir platform independent * Remove ratinabox and ephysiopy as deps * Apply review comments * Update Makefile - setting coverage threshold to 80% to not delay good code being made public. In the near future this can be fixed and raised again to 90%. --------- Co-authored-by: Steffen Schneider <stes@hey.com> Co-authored-by: Steffen Schneider <steffen.schneider@helmholtz-munich.de> Co-authored-by: Mackenzie Mathis <mathis@rowland.harvard.edu> * start tests * remove print statements * first passing test * move functionality to base file in solver and separate in functions * add test_select_model for multisession * remove float16 * Improve modularity remove duplicate code and todos * Add tests to solver * Fix save/load * Fix extra docs errors * Add review updates * apply ruff auto-fixes * fix linting errors * Run isort, ruff, yapf * Fix gaussian mixture dataset import * Fix all tests but xcebra tests * Fix pytorch API usage example * Make xCEBRA compatible with the batched inference & padding in solver * Add some tests on transform() with xCEBRA * Add some docstrings and typings and clean unnecessary changes * Implement review comments * Fix sklearn test * Add name in NOTE Co-authored-by: Steffen Schneider <steffen@bethgelab.org> * Implement reviews on tests and typing * Fix import errors * Add select_model to aux solvers * Fix docs error * Add tests on the private functions in base solver * Update tests and duplicate code based on review --------- Co-authored-by: Rodrigo <gonlairo@gmail.com> Co-authored-by: Steffen Schneider <stes@hey.com> Co-authored-by: Mackenzie Mathis <mathis@rowland.harvard.edu> Co-authored-by: Steffen Schneider <steffen.schneider@helmholtz-munich.de> Co-authored-by: Ícaro <icarosadero@proton.me> Co-authored-by: Mackenzie Mathis <mackenzie.mathis@epfl.ch> Co-authored-by: Steffen Schneider <steffen@bethgelab.org> Co-authored-by: Rodrigo González Laiz <31796689+gonlairo@users.noreply.github.com>

* start tests * remove print statements * first passing test * move functionality to base file in solver and separate in functions * add test_select_model for multisession * remove float16 * Improve modularity remove duplicate code and todos * Add tests to solver * Fix save/load * Fix extra docs errors * Add review updates * apply ruff auto-fixes * fix linting errors * Run isort, ruff, yapf * Fix gaussian mixture dataset import * Fix all tests but xcebra tests * Fix pytorch API usage example * Make xCEBRA compatible with the batched inference & padding in solver * Add some tests on transform() with xCEBRA * Add some docstrings and typings and clean unnecessary changes * Implement review comments * Fix sklearn test * Initial pass at integrating unifiedCEBRA * Add name in NOTE * Implement reviews on tests and typing * Fix import errors * Add select_model to aux solvers * Fix tests * Add mask tests * Fix docs error * Remove masking init() * Remove shuffled neurons in unified dataset * Remove extra datasets * Add tests on the private functions in base solver * Update tests and duplicate code based on review * Fix quantized_embedding_norm undefined when `normalize=False` (#249) * Fix tests * Adapt unified code to get_model method * Update mask.py add headers to new files * Update masking.py - header * Update test_data_masking.py - header * Implement review comments and fix typos * Fix docs errors * Remove np.int typing error * Fix docstring warning * Fix indentation docstrings * Implement review comments * Fix circular import and abstract method * Add maskedmixin to __all__ * Implement extra review comments * Change masking kwargs as tuple and not dict in sklearn impl * Add integrations/decoders.py * Fix typo * minor simplification in solver --------- Note, some comments in this PR overlap with #168 and #225 which were developed in parallel.

gonlairo and others added 6 commits February 17, 2025 21:16

Fix tests

cbcb229

* bump version * update dockerfile * fix progress bar * remove outdated test * rename models

Apply fixes to pass ruff tests

97ad03f

Fix typos

36282d0

Update license headers, fix additional ruff errors

5fa5b42

remove unused comment

9ef548d

stes changed the title ~~Aistats2025~~ Add xCEBRA implementation (AISTATS 2025) Feb 17, 2025

MMathisLab requested changes Feb 17, 2025

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MMathisLab added the xCEBRA🦓 label Feb 17, 2025

stes assigned stes and gonlairo Feb 17, 2025

Merge branch 'main' into aistats2025

a9e7027

cla-bot bot added the CLA signed label Feb 18, 2025

stes added 9 commits February 19, 2025 00:27

rename regcl in codebase

a2f7117

change regcl name in dockerfile

761f373

Improve attribution module

2052ec9

Fix imports name naming

5e483d0

add basic integration test

4254173

temp disable of binary check

1d69957

Add legacy multiobjective model for backward compat

1d10668

add synth import back in

5e30829

Fix docstrings and type annot in cebra/models/jacobian_regularizer.py

458958f

stes reviewed Feb 19, 2025

View reviewed changes

stes added 3 commits February 19, 2025 01:52

add xcebra to tests

d906ad5

add missing cvxpy dep

6f91018

fix docstrings

df4f661

more docstrings to fix attr error

d81b93d

stes added 6 commits April 17, 2025 23:43

format imports

9462caf

docs writing

e4d717d

enable build on dev branch

a7c9562

fix some review comments

df6679d

extend multiobjective docs

f5dc743

Set version to alpha

7435d2f

stes reviewed Apr 17, 2025

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stes mentioned this pull request Apr 17, 2025

Update conf.py #237

Merged

make tempdir platform independent

ea37d02

stes deleted the branch AdaptiveMotorControlLab:main April 18, 2025 11:32

stes closed this Apr 18, 2025

Merge branch 'main' into aistats2025

90e9bbf

stes reopened this Apr 18, 2025

stes changed the base branch from stes/upgrade-docs-rebased to main April 18, 2025 11:49

Remove ratinabox and ephysiopy as deps

cadd612

MMathisLab requested changes Apr 19, 2025

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stes reviewed Apr 20, 2025

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Merge branch 'main' into aistats2025

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stes mentioned this pull request Apr 20, 2025

Add DeltaVMFDistribution #244

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MMathisLab mentioned this pull request Apr 22, 2025

more xCEBRA docstrings #245

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MMathisLab added 2 commits April 23, 2025 06:24

Merge branch 'main' into aistats2025

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Update Makefile

ec95857

- setting coverage threshold to 80% to not delay good code being made public. In the near future this can be fixed and raised again to 90%.

MMathisLab merged commit 92c8b1f into AdaptiveMotorControlLab:main Apr 23, 2025
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Add xCEBRA implementation (AISTATS 2025) #225

Add xCEBRA implementation (AISTATS 2025) #225

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gonlairo commented Feb 17, 2025 • edited by stes Loading Uh oh! There was an error while loading. Please reload this page.

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xCEBRA

eXplainable CEBRA 🔎🦓

This code supports the following paper:

Abstract:

Outline of the Method:

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stes commented Feb 19, 2025 • edited by MMathisLab Loading Uh oh! There was an error while loading. Please reload this page.

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stes commented Apr 20, 2025

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gonlairo commented Feb 17, 2025 •

edited by stes

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stes commented Feb 19, 2025 •

edited by MMathisLab

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