Novel neural network solves PDEs with multi-scale resolution.
problem Solving time-dependent PDEs with varying spatial and temporal scales.
method Multi-scale message passing neural network with temporal and spatial gating modules.
result Outperforms baselines on PDEs with diverse scales.
New method compresses images quickly and accurately.
problem Efficiently compressing natural images without significant loss of quality.
method Multi-scale lossy autoencoder and parallel lossless coder.
result Comparable performance to state-of-the-art models on test datasets.
Paper introduces multi-scale methods to improve CATE estimation from EO data.
problem Challenges in balancing fine-grained and contextual information in EO-based causal inference.
method Multi-Scale Representation Concatenation, combining Vision Transformer and Causal Forests.
result Multi-scale approach captures effect heterogeneity better than single-scale models.
Proposes MscaleDNN for solving high-dimensional PDEs efficiently.
problem Solving high-dimensional PDEs efficiently.
method Radial scaling in frequency domain and compact support activation functions.
result Increased power in multi-scale resolution and high frequency capturing.
Boosting theory explains why multi-scale GNNs work.
problem Over-smoothing in graph neural networks.
method Gradient boosting and transductive learning analysis.
result Test error bound decreases with more node aggregations.
TMSCD detects multi-scale communities in temporal networks automatically.
problem Discovering multi-scale communities in large, evolving networks.
method Spectral multilayer formulation of MM method with automatic parameter selection.
result Automatic detection of multi-scale communities without manual parameter selection.
CAST improves spectral clustering for multi-scale data by integrating reachability similarity.
problem Applying spectral clustering to multi-scale data where clusters vary in size and density.
method CAST integrates reachability similarity with distance-based similarity to derive a coefficient matrix, then applies trace Lasso regularization.
result CAST provides excellent performance and robustness across various multi-scale data test cases.
Proposes a new multi-scale architecture for generative flows to improve log-likelihood and sampling quality.
problem Challenges of high-dimensional latent space in flow models.
method Data-dependent dimension factorization based on likelihood contribution heuristic.
result Improvements in log-likelihood score and sampling quality on image benchmarks.
A new multi-scale vector quantization method for unsupervised data.
problem Efficiently reconstructing unsupervised data with minimal distortion.
method Reconstruction trees, inspired by decision trees, explore data in a multi-scale fashion.
result Analysis of expected distortion under fixed unknown distribution, with asymptotic and finite sample results.
New GCNs improve graph classification with deeper multi-scale information.
problem Limited expressive power of existing GCNs.
method Generalized spectral graph convolution and deep GCN architectures, showing equivalence under certain conditions.
result Two new architectures achieve better performance on node classification tasks.
Embed nodes with multi-scale attributes for robust network analysis.
problem Capturing complex node attributes across different scales.
method Multi-scale attributed node embedding (AE & MUSAE) using Skip-gram approach.
result Proves node-feature mutual information is implicitly factorized by embeddings.
New map from convex functions to domain distributions solves open bandit problem.
problem Adversarial bandit convex optimization
method Multi-scale exploration of convex functions, information ratio analysis, Bayesian maximin regret
result Minimax regret bound of i l d e O ( p o l y ( n ) T ) ilde{O}(\mathrm{poly}(n) \sqrt{T}) i l d e O ( poly ( n ) T ) for adversarial bandit convex optimization Framework for multi-scale clustering using phase transitions.
problem Clustering datasets with multi-scale structures.
method Cascade of phase transitions in simulated annealing of Expectation-Maximisation algorithm with weighted local covariance.
result Approximation of the number and size of clusters at different scales.
EvoMSN tackles time series forecasting under distribution shifts by evolving multi-scale normalization.
problem Accurate long-term time series forecasting under complex distribution shifts.
method EvoMSN framework with multi-scale statistics prediction and adaptive ensembling for collaborative updating.
result Improves forecasting performance of five mainstream methods on benchmark datasets.
AAANE embeds networks by learning attention weights for multi-scale structure.
problem Existing methods ignore the role of different scales in network embedding.
method AAANE uses an attention-based adversarial autoencoder to learn robust representations.
result AAANE outperforms existing methods on real-world networks.
New PINN architectures learn high-frequency features using Fourier features.
problem PINNs struggle with high-frequency or multi-scale features.
method Employ spatio-temporal and multi-scale random Fourier features.
result Effective PINN models for multi-scale PDEs.
Compactifies moduli spaces of abelian differentials with specific zeroes and poles.
problem Constructing a compactification of moduli spaces of abelian differentials.
method Using a blowup of the incidence variety compactification, defining families of projectivized multi-scale differentials, and performing a real oriented blowup.
result The moduli space of multi-scale differentials is a complex orbifold with normal crossing boundary.
Improved online auction pricing with multi-scale learning.
problem Maximizing revenue in online auctions with new buyers each period.
method Generalized learning from experts and multi-armed bandit problems to multi-scale versions.
result Regret bounds that scale with the best fixed price and are almost scale-free.
Proposes OC4Seq for detecting anomalies in discrete event sequences.
problem Challenges in detecting anomalies in discrete event sequences, including data imbalance, discrete events, and sequential nature.
method Integrates anomaly detection with recurrent neural networks (RNNs) to embed sequences into latent spaces and designs a multi-scale RNN framework to capture multi-scale sequential patterns.
result OC4Seq consistently outperforms various baselines on three benchmark datasets.
Paper tackles leverage effect estimation from noisy data.
problem Estimating leverage effect from high-frequency data with microstructure noise.
method Holistic multi-scale framework operating directly on leverage effect, using Subsampling-and-Averaging Leverage Effect (SALE) and Multi-Scale Leverage Effect (MSLE) estimators.
result Holistic multi-scale framework achieves substantial efficiency gains over existing benchmarks.
MSNet uses high frequency residual learning for efficient multi-scale image classification.
problem Efficient multi-scale image classification for mobile and embedded devices.
method Two network architecture: low resolution for low frequency, high resolution for high frequency residuals.
result MSNet achieves significant accuracy improvements over different base networks.
New method improves robustness of large models without sacrificing accuracy.
problem Improving robustness of large pre-trained models without accuracy loss.
method Multi-scale diffusion denoised smoothing, selectively applying smoothing at multiple noise scales.
result Strong certified robustness at high noise levels with accuracy close to non-smoothed classifiers.
DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.
problem Forecasting long-term time series data across diverse scales.
method Dynamic tokenizer, multi-scale transformer, dynamic sparse learning, rotary position encoding.
result DRFormer outperforms existing methods in forecasting accuracy.
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
problem Anomaly detection in time series data is challenging due to varying patterns at different scales and fixed window sizes.
method CrossAD incorporates cross-scale reconstruction and a query library to capture dynamic cross-scale associations and comprehensive context.
result CrossAD achieves state-of-the-art performance in anomaly detection across multiple real-world datasets.
Mod-DeepESN improves echo state networks for complex, multi-scale tasks.
problem Efficiency in solving complex, multi-scale temporal tasks.
method Incorporates intrinsic plasticity into a modular deep echo state network architecture.
result Significantly outperforms state-of-the-art for time series prediction tasks.
Space2Vec learns multi-scale spatial representations from grid cell insights.
problem Encoding spatial features with varying scales from GIS data.
method Proposes Space2Vec, a multi-scale representation learning model using grid cell insights.
result Space2Vec outperforms baselines in predicting POI types and image classification with geo-locations.
Novel framework for systemic risk analysis in financial markets.
problem Systemic risk in financial markets.
method Multi-scale network dynamics, transfer entropy networks, agent-based modeling, wavelet decomposition, Model Context Protocol (MCP).
result Multi-scale approach reveals hidden systemic risk patterns.
This paper reviews some of the phenomenological models which have been introduced to incorporate the scaling properties of financial data. It also illustrates a microscopic model, based on heterogeneous interacting agents, which provides a possible explanation for the complex dynamics of markets' returns. Scaling and m…
Develops a new model for multi-scale nonlinear dynamics.
problem Interpretable descriptions vs. accurate predictions in modeling nonlinear systems.
method Tree-structured recurrent switching linear dynamical system with Bayesian inference.
result Models offer both interpretability and accuracy in predicting complex dynamics.
Federated learning improves CRC grading accuracy and privacy.
problem Inter-observer variability and data privacy in CRC grading.
method Multi-scale federated learning framework integrating ResNetRS50.
result Framework achieves 83.5% accuracy, outperforming centralized models.
MCFNet recovers spatial detail and fuses it with semantic information for real-time segmentation.
problem Recovering spatial detail information and fusing it with semantic information in real-time.
method Proposes a new architecture (MCFNet) with feature refinement and fusion modules, and a gating unit.
result Achieves competitive performance with high speed (75.5% mIOU, 151.3 FPS on Cityscapes).
Langevin Dynamics speeds up mixing time with manifold hypothesis and multi-scale approach.
problem Langevin Dynamics struggles in high dimensions and nonconvex landscapes.
method Utilizes manifold hypothesis to reduce mixing time and employs multi-scale approach to improve image generation quality.
result Mixing time depends on intrinsic dimension rather than ambient dimension, significantly reducing computational complexity.
Empirical analysis shows multi-scaling in North American electricity prices.
problem Analyzing volatile components of electricity price time series.
method Empirical analysis using generalized Hurst exponent (GHE) and rolling time windows.
result GHE can be used as a predictive tool for electricity price time series.
Improves speaker verification for variable-duration utterances using a feature pyramid module.
problem Improving robustness for variable-duration utterances in speaker verification.
method Integrates a feature pyramid module into multi-scale aggregation to enhance speaker-discriminative information from multiple layers.
result Improves performance for both short and long utterances compared to state-of-the-art approaches.
Locally adaptive clustering for tree delineation.
problem Tree delineation from distance data.
method Locally adaptive hierarchical cluster termination.
result Multi-scale alternative to conventional termination criteria.
Model for directed synthesis of audio textures using multi-scale RNNs.
problem Challenges in modeling complex audio textures with traditional methods.
method Combining multi-scale RNNs with a conditioning strategy for user-directed synthesis.
result Demonstrated improved performance on various audio texture datasets.
Neural HMM with AGA captures multi-scale dynamics in financial markets.
problem Capturing multi-scale temporal dynamics in financial markets.
method Parallel multi-resolution encoders, adaptive gating, and multi-head attention.
result Outperforms fixed-resolution baselines in predicting price movements and liquidity shocks.
Improved recurrent neural networks learn long-term dependencies through multi-scale memory.
problem Capturing long-term dependencies in recurrent neural networks.
method Incremental training of a modular RNN architecture with multi-scale hidden states.
result Incremental training and multi-scale memory enhance RNNs' ability to learn long-term dependencies.
Automated spectral clustering algorithm discovers clusters without parameter tuning.
problem Automatic spectral clustering for multi-scale data.
method Heuristic iterative eigengap search with global and local scaling.
result Discover different patterns with accuracy >90% in most cases.
Enhances DenseNets with multi-scale convolutions and stochastic feature reuse.
problem Overfitting in DenseNets with dense feature reuse.
method Multi-scale Convolution Aggregation module and Stochastic Feature Reuse.
result Significant improvement in model accuracy with fewer parameters.
Proposes MSTD-RCNN for improved financial time-series classification.
problem Combining Multi-Scale and Temporal Dependency for better financial time-series classification.
method Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN).
result Achieves state-of-the-art performance in trend classification and simulated trading.
Paper proposes MSSDDPG for better financial trading strategies.
problem Extracting accurate features from noisy, non-stationary financial time series.
method Multi-scale stroke deep deterministic policy gradient reinforcement learning model (MSSDDPG).
result MSSDDPG outperforms other strategies in China's CSI 300 and SSE Composite.
Study abelian varieties' Weil-Petersson metric asymptotics.
problem Asymptotic behavior of Weil-Petersson metric on abelian varieties.
method Linking asymptotic with multi-scale collapsing limits of parametrized flat tori.
result Refined description of Weil-Petersson metric on abelian varieties.
Backdrop uses dropout-like masking in backpropagation for multi-scale data.
problem Improving generalization in multi-scale data.
method Inserting masking layers after convolutional layers to mask backward gradients.
result Backdrop leads to significant improvements in generalization.
New method disentangles sources of different timescales in planetary seismic data.
problem Unsupervised source separation of multi-scale seismic data from planetary missions.
method Wavelet scattering spectra for multi-scale clustering and variational autoencoder for source separation.
result Disentangles sources with different timescales in InSight mission seismic data.
SHAKE-GNN scales GNNs for large graphs with multi-scale representations.
problem Scaling Graph Neural Networks (GNNs) to large graphs.
method SHAKE-GNN uses a hierarchy of Kirchhoff Forests for stochastic multi-resolution graph decompositions.
result SHAKE-GNN achieves competitive performance on large-scale graph classification benchmarks.
LanczosNet uses Lanczos algorithm for efficient graph convolution.
problem Efficiently capturing multi-scale information in graph data.
method Lanczos algorithm for low-rank graph Laplacian approximation, learnable spectral filters.
result LanczosNet achieves state-of-the-art performance on citation networks and QM8.
Preformer improves Transformer for long-term time series forecasting.
problem Transformer's quadratic complexity and lack of context-awareness for long-term forecasting.
method Introduces Multi-Scale Segment-Correlation mechanism for efficient time series segmentation and context-aware attention.
result Preformer outperforms other Transformer-based methods in long-term time series forecasting.