Prediction of Social Dynamic Agents and Long-Tailed Learning Challenges: A Survey
Authors: Divya Thuremella, Lars Kunze
JAIR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a survey titled "Prediction of Social Dynamic Agents and Long-Tailed Learning Challenges: A Survey". Survey papers primarily analyze and synthesize existing research rather than conducting new empirical studies or experiments. While it reviews and summarizes experimental results from other papers, its own contribution is conceptual and analytical, focusing on taxonomies, trends, and open challenges based on existing literature. |
| Researcher Affiliation | Academia | Divya Thuremella EMAIL Lars Kunze EMAIL Department of Engineering Science, University of Oxford, Parks Rd Oxford OX13PJ, UK |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm", nor does it present any structured code-like procedures. It describes methodologies in narrative text and uses diagrams for taxonomies. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology described in this work, nor does it include a direct link to a code repository. It mentions external resources like "AIcrowd Trajnet++ (A Trajectory Forecasting Challenge)" but these are not the authors' implementation code. |
| Open Datasets | Yes | The paper extensively cites and refers to numerous well-known public datasets used in the field, including "UCF-101 (Soomro et al., 2012)", "Human3.6M (Ionescu et al., 2014)", "ETH-UCY dataset (Pellegrini et al., 2010; Lerner et al., 2007)", "PIE (Rasouli et al., 2019)", "BDD100K (Yu et al., 2020)", and many others throughout Sections 2 and 3, which are all publicly available. |
| Dataset Splits | No | As a survey paper, this work analyzes and summarizes methodologies and results from other research. It does not conduct its own experiments and therefore does not define specific training/test/validation dataset splits. While it mentions how other papers or challenges use datasets (e.g., "most of the methods that use the ETH-UCY dataset... use N =20"), it does not provide split information for its own work. |
| Hardware Specification | No | This paper is a survey and does not conduct original experiments. Consequently, it does not provide any specific details regarding the hardware (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | As a survey paper, this work reviews methodologies developed by others rather than implementing new ones. Therefore, it does not specify any software dependencies (e.g., library names with version numbers like Python, PyTorch, or specific solvers) that would be needed to replicate its own experimental work. |
| Experiment Setup | No | This paper is a survey and focuses on reviewing and summarizing existing research. It does not contain an "Experimental Setup" section or provide specific hyperparameters, optimizer settings, or other system-level training configurations for its own experiments. |