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.
New training algorithm enhances SNNs for temporal signal processing.
A new method detects financial fraud using graph transformers.
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…
Proposes a GNN for multivariate time-series prediction with filtering.
Proposes KStar Diffuser for kinematics-aware bimanual robotic manipulation.
Proposes a method to forecast spatial-temporal data with limited training data.
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…
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
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…
A new method identifies critical transitions in high-dimensional data.
New model predicts travel demand uncertainty with high accuracy.
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…
MIP framework improves urban flow prediction by adapting to distribution shifts.
PSTN improves traffic condition forecasting with deep neural networks.
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
Proposes a non-autoregressive Transformer for time series forecasting.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
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…
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…
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…
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
MRA-BGCN improves traffic forecasting accuracy through complex graph interactions.
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques are de…
We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Ins…
A video-based re-identification method using attention mechanisms.
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…
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
New BNN method reduces training time and model size.
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…
Finite element method applied to Leland's model for option pricing with transaction costs.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
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.
Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction wit…
Improved motion prediction for self-driving cars using trajectory sets and auxiliary losses.
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of …
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.
Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage given a set of incidents is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice,…
A new autoencoder architecture captures multiscale data.
Develops a tensor decomposition method with side information.
kNN-MTS improves MTS forecasting by using nearest neighbor retrieval over a large dataset.