Generative model learns spatial memory for predicting agent's future in complex environments.
problem Training generative and temporal models in partially observed, 3D environments is challenging.
method Action-conditioned generative model with non-parametric spatial memory and state-space model for dynamics.
result Scalable architecture capable of coherent predictions over hundreds of time steps in 2D and 3D environments.
New neural network model learns to generalize spatial knowledge from memories.
problem Understanding how the brain generalizes structural knowledge from past experiences.
method Inspired by hippocampal-entorhinal system, proposes separating entity and structural representations.
result Artificial neural networks can learn structural knowledge and generalize it to new situations.
New CTRW model with memory explains long-term return autocorrelation.
problem Explaining long-term autocorrelation in financial returns.
method Proposed a Directed Continuous-Time Random Walk (CTRW) model with memory, considering only positive jumps and dependence on previous jumps.
result Bid-ask bounce explains only a small fraction of the long-term autocorrelation in financial returns.
MIP framework improves urban flow prediction by adapting to distribution shifts.
problem Distribution shifts in urban flow data make prediction models unreliable.
method Memory-enhanced Invariant Prompt learning with learnable memory bank.
result MIP ensures robust predictions by focusing on invariant features.
Product Kanerva Machines dynamically combine smaller models for better memory organization.
problem Limited organization in the Kanerva Machine.
method Introducing Product Kanerva Machines that dynamically combine multiple smaller Kanerva Machines.
result Product Kanerva Machines can discover spatial tunings that approximately factorize simple images by object.
A new method for faster spatial modeling on exascale computers.
problem Scalable, memory-efficient machine learning for spatially distributed data.
method Partitioned Sparse Variational Gaussian Process (PSVGP) with decentralized communication.
result Improved spatial predictions and better model fit with minimal overhead.
Novel graph model forecasts urban traffic with reduced spatial complexity.
problem Challenges in traffic forecasting due to spatio-temporal complexity, especially in urban environments.
method MW-TGC network model that combines spatial and temporal dependencies using multi-weighted adjacency matrices and graph convolution operations.
result MW-TGC network outperforms other models in urban-core and urban-mix sites, reducing variance in heterogeneous environments.
New method speeds up sparse Gaussian processes for large datasets.
problem Efficiently modeling large datasets with many inducing variables.
method Projecting a GP onto B-spline basis functions for sparse linear algebra.
result Efficiently models fast-varying spatial phenomena with tens of thousands of inducing variables.
Proposes a multi-level learning approach for 3D object recognition.
problem Improving 3D object recognition accuracy through multi-scale spatial features.
method End-to-end multi-level learning on a multi-level voxel grid.
result Comparable object recognition performance with lower memory usage.
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatia…
We extend the theory of combinatorial link Floer homology to a class of oriented spatial graphs called transverse spatial graphs. To do this, we define the notion of a grid diagram representing a transverse spatial graph, which we call a graph grid diagram. We prove that two graph grid diagrams representing the same tr…
A deep historical LSTM model improves tennis shot recognition from RGB videos.
problem Improving action recognition from RGB video input for sports analysis.
method Convolutional Neural Network (CNN) and Weighted Long Short-Term Memory (LSTM) for 3D tennis shot recognition.
result The method achieves better performance than state-of-the-art baselines for tennis shot recognition.
Proposes TEMN for better POI recommendations.
problem Challenges in capturing user preferences and spatio-temporal POI relationships.
method Integrates topic model and memory network, incorporating geographical module.
result Improves POI recommendation effectiveness by 3.25% and 29.95%.
PipeMare enables efficient DNN training with minimal memory and pipeline sacrifices.
problem Sacrificing hardware efficiency to maintain statistical efficiency in pipeline parallel DNN training.
method PipeMare is a simple yet robust training method that tolerates asynchronous updates during pipeline parallelism without sacrificing pipeline utilization or memory.
result PipeMare achieves up to 2.7x less memory usage or 4.3x higher pipeline utilization compared to state-of-the-art synchronous PP training techniques.
New neural networks model for spatio-temporal data.
problem Building a mapping from spatially encoded time series covariates to real-valued response data.
method Proposed two novel extensions of Functional Neural Network (FNN) for spatio-temporal regression.
result Demonstrated effectiveness in handling varying spatial correlations through comprehensive simulation studies.
Stacked LSTM improves weather forecasting accuracy by incorporating spatial information.
problem Improving temperature prediction accuracy in weather forecasting.
method 2-layer spatio-temporal stacked LSTM model with independent LSTM models per location in the first layer and combined hidden states in the second layer.
result The stacked LSTM model outperforms single LSTM models in most cases by utilizing spatial information.
Study compares tessellation strategies for taxi demand-supply forecasting models.
problem Improving taxi demand-supply forecasting using neural networks.
method Compared Voronoi tessellation and Geohash tessellation for LSTM models.
result Variable-sized polygon tessellation yields superior performance in LSTM models.
New framework models complex spatial data with basis functions and graphical vectors.
problem Modeling highly-multivariate spatial processes with varying resolutions.
method Extends graphical lasso to multivariate Gaussian processes with independent graphical vectors at different resolutions, using an orthogonal basis and fusion penalty.
result Linear complexity and parsimonious conditional independence structure in multilevel graphical model.
Area attention allows dynamic area-based attention in memory.
problem Fixed attention granularity limits model performance.
method Area attention dynamically determines area shape and size via learning.
result Area attention improves performance on neural machine translation and image captioning.
DRLViz interprets deep RL agent memory for better understanding.
problem Understanding complex deep RL agent memory.
method Visual analytics interface to reduce and interpret memory vectors.
result Experts can better understand and investigate agent decisions.
New GPU algorithm speeds up Gaussian Process analysis.
problem Reducing computational complexity for large spatial datasets.
method Implemented three GPU methods for Vecchia Approximation.
result New GPU method outperforms existing methods.
Paper predicts spatial variation data from few samples using tensor methods.
problem Predict spatial variation data from limited samples in high-dimensional data.
method Bayesian tensor completion exploiting hidden low-rank property.
result Predicts spatial variation data efficiently from few samples.
Meta-learning approach for spatial-temporal prediction across cities.
problem Spatial-temporal prediction for cities with limited data.
method Meta-learning paradigm with spatial-temporal network and pattern-based memory.
result Meta-learning model improves prediction accuracy over multiple tasks.
Graph Element Networks adaptively model spatial processes without prior graph structure.
problem Modeling spatial processes without a priori graphical structure.
method Assign nodes to spatial locations, optimize their connectivity, and use GNNs as a computational substrate.
result Optimized GNN nodes focus on complex parts of the space, allowing generalization and varying precision.
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
problem High computational cost of Gaussian processes for large data sets.
method Systematic comparison of different Gaussian process approximations.
result Vecchia approximations consistently provide the best accuracy-runtime trade-off.
Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image…
A neuromemristive HTM architecture boosts robustness and performance.
problem Improving robustness and performance of HTM algorithms.
method Developed a neuromemristive crossbar architecture for HTM, incorporating memristors and neurogenesis.
result Enhanced robustness and performance of HTM through neuromemristive architecture.
New training algorithm enhances SNNs for temporal signal processing.
problem Lack of robust training algorithms for large-scale SNNs.
method Formulated SNN as IIR filters, proposed training algorithm for optimal synapse filter kernels and weights.
result Model and training algorithm outperform state-of-the-art approaches in accuracy.
A new method reduces memory requirements for sorting high-dimensional data.
problem Efficiently sorting and organizing high-dimensional data with low memory usage.
method Iteratively shuffles N indices and applies SoftSort optimization steps.
result Significantly improves sorting quality for multidimensional data.
A novel RNN uses stigmergy for temporal input encoding.
problem Temporal input encoding in RNNs.
method Stigmergy-based computational memory in RNN design.
result Stigmergy enables coherent temporal input encoding.
A new tensor decomposition method for fMRI data captures both spatial and temporal variability.
problem Challenges in modeling shared and subject-specific structure in multisubject spatiotemporal data, especially in neuroimaging.
method Introduces a spatiotemporal variational tensor decomposition (ST-VTD) framework combining tensor factorization with structured priors for flexible representation of spatial and temporal dynamics.
result Significantly improves latent factor recovery in fMRI data compared to classical and probabilistic decomposition benchmarks.
MIM networks predict non-stationary spatiotemporal dynamics using differential signals.
problem Predicting non-stationary spatiotemporal processes with high-order variations.
method Memory In Memory (MIM) networks with cascaded memory modules.
result Achieved state-of-the-art results on four spatiotemporal prediction tasks.
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurem…
ATFM predicts traffic flow using attention mechanism and neural networks.
problem Predicting traffic flow with diverse factors integration.
method Unified neural network (ATFM) with attention mechanism and ConvLSTM units.
result ATFM outperforms in predicting citywide short-term/long-term traffic flow.
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memo…
Improved taxi demand-supply forecasts using graph-based LSTM.
problem Accurate taxi demand-supply forecasting with complex spatial and temporal patterns.
method Investigated impact of spatial partitioning techniques (Voronoi vs. Geohash) on LSTM network performance.
result GraphLSTM offers competitive performance against ConvLSTM, at lower complexity, across real-world data sets.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
problem Challenges in forecasting spatial-temporal traffic patterns.
method In-depth comparative study of four deep neural network models with different basic elements.
result Graph attention improves long-term predictions in traffic forecasting models.
CSTN predicts taxi demand between all regions, overcoming origin-only approaches.
problem Predicting taxi demand between all regions, not just origins.
method Contextualized Spatial-Temporal Network (CSTN) with LSC, TEC, and GCC modules.
result CSTN outperforms other methods in taxi origin-destination demand prediction.
Intelligence emerges from stabilizing invariant cycles in memory.
problem Understanding the nature of intelligence and its emergence.
method Structural-dynamical account rooted in a topological closure law: \(\partial^2=0\).
result Memory-amortized inference (MAI) mechanism that implements SbS \(
ightarrow\) CCUP.
Paper proposes a deep learning model for air quality forecasting.
problem Air quality forecasting as a key problem for pollution management.
method Hybrid deep learning architecture combining 1D-CNNs and Bi-LSTM.
result Model achieves satisfactory accuracy in PM2.5 forecasting.
New method improves neural decoding accuracy and reveals latent memory organization.
problem Improving neural decoding of temporal memory organization.
method Bayesian neural decoding using a diversity-encouraging latent representation learning method.
result Substantially higher accuracy in neural decoding and clear latent representation.
A new framework combines CNN and GRU for better structural damage detection.
problem Improving damage detection in structural engineering using machine learning.
method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.
result The proposed HCG framework significantly outperforms existing methods for structural damage detection.
Spatio-temporal data compression method reduces memory usage.
problem Efficiently storing and analyzing large spatio-temporal datasets.
method Adaptive sampling of tensor slices to compress and preserve structure.
result SkeTenSmooth outperforms other sampling methods in retaining patterns.
Airlines optimize fuel loading with better flight time predictions.
problem Flight time uncertainties and their impact on fuel consumption.
method Developed a spatial weighted recurrent neural network model.
result The model provides more accurate flight time predictions, reducing fuel consumption.
COCO-GAN generates images by parts using spatial coordinates, achieving state-of-the-art quality.
problem Generating full images from partial observations due to computational constraints.
method COCO-GAN uses conditional coordination to generate images by parts based on spatial coordinates, with a generator and discriminator learning to justify realism.
result COCO-GAN produces state-of-the-art-quality full images during inference, enabling novel applications like beyond-boundary generation and panorama generation.
New BNN method reduces training time and model size.
problem Overconfident predictions in deep learning models.
method Designing STF-BNN for efficient scaling of BNNs.
result Significantly reduces training time and model size compared to vanilla BNNs.
Two methods for fast Gaussian process inference on large datasets with gaps.
problem Efficient Gaussian process inference for large datasets with missing values.
method Two novel approaches using Kronecker matrix algebra and low-rank preconditioning.
result The second method is greatly advantageous for problems with gaps.
First-order scattering reduces CNN input size while maintaining classification accuracy.
problem Reducing the input size of CNNs for faster inference and lower memory usage.
method First-order scattering transform applied to CNN inputs.
result Cascaded CNN with scattering transform achieves similar ImageNet classification performance.