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Found 17 papers, 7 papers with code
Resolution-Agnostic Transformer-based Climate Downscaling
Understanding future weather changes at regional and local scales is crucial for planning and decision-making, particularly in the context of extreme weather events, as well as for broader applications in agriculture, insurance, and infrastructure development.
PACER: Physics Informed Uncertainty Aware Climate Emulator
Climate models serve as critical tools for evaluating the effects of climate change and projecting future climate scenarios.
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.
XXLTraffic: Expanding and Extremely Long Traffic Dataset for Ultra-Dynamic Forecasting Challenges
To bridge this gap, we present XXLTraffic, the largest available public traffic dataset with the longest timespan and increasing number of sensor nodes over the multiple years observed in the data, curated to support research in ultra-dynamic forecasting.
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation
Trajectory similarity computation is an essential technique for analyzing moving patterns of spatial data across various applications such as traffic management, wildlife tracking, and location-based services.
Spectraformer: A Unified Random Feature Framework for Transformer
We identify the need for a systematic comparison of different combinations of weight matrices and component functions for attention learning in Transformer.
STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting
STC-ViT incorporates the continuous time Neural ODE layers with multi-head attention mechanism to learn the continuous weather evolution over time.
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images.
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field.
Multimodal Trajectory Prediction: A Survey
Trajectory prediction is an important task to support safe and intelligent behaviours in autonomous systems.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity
While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences.
GCCN: Global Context Convolutional Network
These global contextual features are defined as local maxima pixels with high visual sharpness in each patch.
Signature-Graph Networks
SGN constructs unique undirected graphs for each image based on the CNN feature maps.
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems
Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics.
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average.
