Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
In-Context Symmetries: Self-Supervised Learning through Contextual World Models
Authors: Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate significant performance gains over existing methods on equivariancerelated tasks, supported by both qualitative and quantitative evaluations. |
| Researcher Affiliation | Academia | Sharut Gupta*, Chenyu Wang*, Yifei Wang*, Tommi Jaakkola MIT CSAIL EMAIL Stefanie Jegelka TU Munich, MIT CSAIL EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm block found. The methods are described in prose. |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: We are working towards organizing the code base and will make it available by the rebuttal. |
| Open Datasets | Yes | We use the 3D Invariant Equivariant Benchmark (3DIEBench) [16] and CIFAR10 to test our approach. |
| Dataset Splits | Yes | We use the standard training, validation and test splits, made publicly available by the authors [16]. |
| Hardware Specification | Yes | Each experiment was conducted on 1 NVIDIA Tesla V100 GPUs, each with 32GB of accelerator RAM. The CPUs used were Intel Xeon E5-2698 v4 processors with 20 cores and 384GB of RAM. |
| Software Dependencies | No | All experiments were implemented using the Py Torch deep learning framework. |
| Experiment Setup | Yes | On all datasets, we train CONTEXTSSL with the Adam optimizer with a learning rate of 5e 5 and weight decay 1e 3. For baseline self-supervised approaches, in their original architecture, we use a learning rate of 1e 3 with no weight decay. We fix the maximum training context length to 128. |