Reusable Slotwise Mechanisms
Authors: Trang Nguyen, Amin Mansouri, Kanika Madan, Khuong Duy Nguyen, Kartik Ahuja, Dianbo Liu, Yoshua Bengio
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |