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..
Reusable Slotwise Mechanisms
Authors: Trang Nguyen, Amin Mansouri, Kanika Madan, Khuong Duy Nguyen, Kartik Ahuja, Dianbo Liu, Yoshua Bengio
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superior performance of RSM compared to state-of-the-art methods across various future prediction and related downstream tasks |
| Researcher Affiliation | Collaboration | Trang Nguyen Mila Quebec AI Institute FPT Software AI Center Montreal, Canada |
| Pseudocode | Yes | Algorithm 1 Reusable Slotwise Mechanisms |
| Open Source Code | Yes | Video visualizations of our experiments are provided in our repository github.com/trangnnp/RSM |
| Open Datasets | Yes | OBJ3D [29] contains dynamic scenes of a sphere colliding with static objects. |
| Dataset Splits | Yes | Regarding the rollout frames K and the video length V in Table 5, we predict and consider K future frames from the last burn-in steps for training, whereas, we produce the total of V frames in the inference time, including T burn-in and V T rollout steps. |
| Hardware Specification | Yes | Each experiment is trained on 4 V100-GPUs with 12 CPUs, using a distributed data-parallel training strategy. |
| Software Dependencies | No | The paper mentions training on GPUs and CPUs but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Table 5: Summary of experiments configuration, including the configuration of datasets, training process, and RSM. |