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 [1].
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
Authors: Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report our results on the Interaction multi-agent prediction challenge and rank 1st on the online test leaderboard. (Abstract) 4 EXPERIMENTS (Section title) 4.3 COMPARISON WITH STATE-OF-THE-ART (Section title) 4.4 ABLATION STUDIES (Section title) |
| Researcher Affiliation | Collaboration | 1Io V team, Paris Research Center, Huawei Technologies France 2Center for robotics, MINES Paris Tech |
| Pseudocode | No | The paper includes a detailed architecture diagram (Figure 8) but no explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The reproducibility statement mentions the public availability of the dataset but does not state that the code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We use the publicly available Interaction 1.2 dataset (Zhan et al., 2019) available at http://challenge.interaction-dataset.com/dataset/download. |
| Dataset Splits | Yes | We use the training/validation split provided in Interaction 1.2. |
| Hardware Specification | No | The paper discusses training and inference times (e.g., 'Training 7.5 hours', 'Inference 20 ms') but does not specify the hardware used (e.g., GPU model, CPU type, or memory). |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer', 'ReLU activation', and 'Layer Normalization', but does not specify version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | We train all models with Adam optimizer and batchsize 32. We initialize the learning rate at 1e 3 and divide it by 2 at epochs 3, 6, 9 and 13, before stopping the training at epoch 16. We use Re LU activation after every linear layer unless specified otherwise, and Layer Normalization after every attention and graph convolution layer. |