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..
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Authors: Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations on synthetic and real-world datasets demonstrate significant improvements over existing baselines, highlighting the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2Mohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | No | Our framework builds on VAE [34, 35] architecture, incorporating dedicate modules to handle nonstationarity. It enforces the conditions discussed in Sec. 3 as constraints. As shown in Fig. 2, the framework consists of three primary components: (1) Sparse Transition, (2) Prior Network, and (3) Encoder-Decoder. |
| Open Source Code | Yes | Our code is also available via https://github.com/xiangchensong/ctrlns. |
| Open Datasets | Yes | Our evaluation used two datasets: Hollywood Extended [38], which includes 937 videos with 16 daily action categories, and Cross Task [39], focusing on 14 of 18 primary tasks related to cooking [40], comprising 2552 videos across 80 action categories. |
| Dataset Splits | Yes | In the Hollywood dataset, we used the default 10-fold dataset split setting and calculated the mean and standard derivation from those 10 runs. |
| Hardware Specification | Yes | All experiments are performed on a GPU server with 128 CPU cores, 1TB memory, and one NVIDIA L40 GPU. |
| Software Dependencies | Yes | For synthetic experiments, the models were implemented in Py Torch 2.2.2. |
| Experiment Setup | Yes | We trained the VAE network using the Adam W optimizer with a learning rate of 5 10 4 and a mini-batch size of 64. |