A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a method to forecast spatial-temporal data with limited training data.
Proposes a GNN for multivariate time-series prediction with filtering.
A new method detects financial fraud using graph transformers.
New model predicts travel demand uncertainty with high accuracy.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the si…
MIP framework improves urban flow prediction by adapting to distribution shifts.
Proposes KStar Diffuser for kinematics-aware bimanual robotic manipulation.
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph…
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
GACAN combines multi-granularity time series for traffic forecasting.
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
PSTN improves traffic condition forecasting with deep neural networks.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
A deep learning model for traffic forecasting in telecommunication networks.
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities ma…
Model forecasts global stock market volatility using dynamic graphs and all trading days.
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different dom…
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications …
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
Proposes a non-autoregressive Transformer for time series forecasting.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
kNN-MTS improves MTS forecasting by using nearest neighbor retrieval over a large dataset.
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizi…
The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN mode…
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
New BNN method reduces training time and model size.
Flow prediction (e.g., crowd flow, traffic flow) with features of spatial-temporal is increasingly investigated in AI research field. It is very challenging due to the complicated spatial dependencies between different locations and dynamic temporal dependencies among different time intervals. Although measurements of …
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
Finite element method applied to Leland's model for option pricing with transaction costs.
Hybrid model improves COVID-19 case forecasting accuracy.
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses. The first method ignores the gaps using sparse selection matrices and a highly effective low-rank preconditioner is…
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as te…
Hybrid model predicts flow and pressure in water systems.
VisitHGNN predicts visit probabilities between neighborhoods and POIs using graph neural networks.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…
ProGen improves spatiotemporal forecasting with SDEs and diffusion models.
A new method identifies critical transitions in high-dimensional data.
Deep learning model predicts traffic flows across entire network for multiple steps ahead.
Predicts fine-grained OD matrices for ridesharing platforms to optimize supply-demand balance.
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which achieved high accuracy and large-scale predicti…