Meta-learning over time for destination prediction tasks

Tenzer, Mark; Rasheed, Zeeshan; Shafique, Khurram; Vasconcelos, Nuno

doi:10.1145/3557915.3560980

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…In addition, the contributions also raise many interesting GeoAI research questions such as: (1) How to combine deductive methods from symbolic GeoAI with the representations and induction from deep learning models used by subsymbolic GeoAI to build neuro‐symbolic GeoAI models? (2) To improve model generalizability across space and time, instead of using the FSTL method as Li et al (2022) did, can we directly learn a hypernetwork to simulate how the model's parameters change based on the location and time with meta‐learning method (Tenzer et al, 2022)? (3) Given the increasing popularity of foundation models (FMs) in the natural language and vision communities, such as GPT‐3 (Brown et al, 2020), CLIP (Radford et al, 2021), PaLM (Wei et al, 2022), and DALL∙E2 (Ramesh et al, 2022), could we build a FM for GeoAI which, after pretraining, can be easily adapted to multiple symbolic GeoAI and subsymbolic GeoAI tasks, involving the use of different data modalities (Mai et al, 2022a)?…”

Section: Conclusion and Next Stepsmentioning

confidence: 99%

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

Mai

Gao

et al. 2022

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The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic AI and Connectionist AI (or the so-called Subsymbolic AI). Symbolic AI focuses on research based on classical logic and higher-level symbolic (human-readable) knowledge representations. It posits the use of declarative knowledge in reasoning and learning as critical to producing intelligent behavior (Goel, 2022). Examples are logical inference, symbolic reasoning, ontol-

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Section: Conclusion and Next Stepsmentioning

confidence: 99%

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

Mai

Gao

et al. 2022

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MobilityDL: a review of deep learning from trajectory data

Graser,

Jalali,

Lampert

et al. 2024

Geoinformatica

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Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

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