Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Learning diverse causally emergent representations from time series data
Authors: David McSharry, Christos Kaplanis, Fernando Rosas, Pedro A.M Mediano
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 23,867 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,231 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results show that causal emergence speeds up learning of more complex features of the data relative to pure mutual information maximisation, and we demonstrate the scalability of our method through an analysis of real-world brain activity data. |
| Researcher Affiliation | Collaboration | David Mc Sharry Department of Computing Imperial College London EMAIL Christos Kaplanis Google DeepMind EMAIL Fernando E. Rosas Sussex AI, University of Sussex EMAIL Pedro A.M. Mediano Department of Computing Imperial College London EMAIL |
| Pseudocode | Yes | For convenience, pseudocode for the algorithm used to train the model is provided in Supp. Sec. A. |
| Open Source Code | Yes | Code implementing our proposed architecture and reproducing our key results is available at https: //github.com/Imperial-MIND-lab/causally-emergent-representations |
| Open Datasets | Yes | Primate ECoG dataset. We evaluate our method on a dataset of electrocorticography (ECoG) brain activity data from a macaque monkey, originally reported by Chao et al. [10]. |
| Dataset Splits | No | Glider decoding b) Classification accuracy of the state of the glider (c.f. Supp. Fig. 7) on a held-out test set. Dashed black line represents chance level at 25%. |
| Hardware Specification | Yes | All experiments can be run on a single A10G GPU in less than two hours. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers. |
| Experiment Setup | Yes | Hyperparameters for the models trained on the synthetic and brain datasets can be seen in the below tables. The hyperparameters do not change when learning diverse sets of emergent features. For each training run the clip was fixed at 5 in the SMILE mutual information estimators and the optimizer used was Adam W. |