Hierarchical video model learns actions at coarse and fine levels.
problem Understanding complex video actions at different levels of detail.
method A hierarchical architecture that learns coarse-grained to fine-grained actions.
result Models that learn at multiple levels of granularity perform best.
Study identifies key brain regions and model architectures for ASD diagnosis.
problem Subjective and time-consuming ASD diagnosis by clinicians.
method Comparative analysis of model architectures and atlas granularities.
result High-performing models use 2-4 hidden layers and 16-64 neurons per layer, and cerebellum is predictive of ASD.
Paper proposes a classifier using fuzzy sets for data simplification.
problem Data simplification and handling uncertainty in real-world applications.
method Multi-resolution hierarchical granular representation (MRHGRC) using hyperbox fuzzy sets.
result High accuracy at low granularity with reduced data size.
New method learns fusion rules from few images using granular ball priors.
problem Challenges in supervised learning for image fusion with limited data.
method Introduces incomplete priors and Granular Ball Pixel Computation (GBPC) algorithm.
result Lightweight neural network learns effective fusion rules from few images.
Reduces high granularity and dimensionality in hierarchical categorical variables.
problem Overfitting and estimation issues in predictive models due to high granularity and dimensionality.
method Entity embedding and top-down clustering algorithm to reduce granularity and dimensionality.
result The reduced hierarchy improves model fit and complexity balance.
Tree++ graph kernel captures similarities at multiple granularities.
problem Lack of scale-adaptivity in existing graph kernels.
method Tree++ uses truncated BFS trees and super paths to represent graphs at different granularities.
result Tree++ achieves best classification accuracy on real-world graphs.
Proposes element-level differential privacy for better privacy and utility in statistical learning.
problem Challenges of strong differential privacy in statistical learning applications.
method Introduces element-level differential privacy, extending classical DP to protect specific user elements.
result Provides better utility and more robust results compared to classical DP by allowing finer privacy protections.
The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…
GACAN combines multi-granularity time series for traffic forecasting.
problem High dynamics and complex spatial-temporal dependency of road networks in traffic forecasting.
method Graph Attention-Convolution-Attention Networks (GACAN) with Att-Conv-Att (ACA) block.
result GACAN outperforms state-of-the-art baselines in traffic forecasting.
Nested learning improves model performance on multi-granular tasks.
problem Overconfident models and lack of fine-grained confidence in predictions.
method Introducing nested learning with a sequence of nested feature embeddings and explicit combination of outputs.
result Nested learning outperforms standard end-to-end training on various datasets.
Model refines coarse spatial data using diverse auxiliary data sets.
problem Tackles the challenge of refining coarse spatial data with varying auxiliary data granularities.
method Proposes a probabilistic model using Gaussian processes to hierarchically incorporate auxiliary data sets of various granularities.
result Can effectively refine coarse-grained spatial data using auxiliary data sets of different granularities.
Paper proposes PRR network for better experience reuse in reinforcement learning.
problem Efficient experience reuse in reinforcement learning across multiple granularities.
method Proposes PRR network trained on multi-level architecture to extract and store experience.
result PRR network leads to better experience reuse and improved performance.
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.
Paper tackles ASC with coarse-to-fine task transfer for AT-level sentiment classification.
problem Small AT-level corpora and limited data for fine-grained AT task.
method Coarse-to-fine task transfer using MGAN with Coarse2Fine attention and contrastive feature alignment.
result MGAN improves ASC performance on AT-level sentiment classification.
This work improves interpretability in deep learning models by introducing a two-level concept discovery framework.
problem High complexity and lack of interpretability in deep learning models, especially for safety-critical tasks.
method Concept Bottleneck Models (CBMs) framework combining vision-language models and data-driven coarse-to-fine concept selection.
result The proposed framework outperforms recent CBM approaches and provides a principled interpretability.
Tool visualizes Transformer model attention for better understanding.
problem Understanding complex attention mechanisms in deep learning models.
method Developed an open-source tool to visualize attention at three levels.
result Visualization helps interpret and analyze Transformer models.
Improved online AED models with multi-stage training and multi-task learning.
problem Enhance performance of online attention-based encoder-decoder models.
method Three-stage training with character encoder, BPE encoder, and attention decoder; multi-task learning at character and BPE levels; transfer learning from bidirectional encoder.
result 35% and 10% relative improvement over baselines for smaller and bigger models, respectively.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
SGQuant reduces GNN memory usage without significant accuracy loss.
problem High memory consumption in GNNs limits their applicability on memory-constrained devices.
method Proposes a specialized GNN quantization scheme (SGQuant) with a quantization algorithm, fine-tuning scheme, and multi-granularity strategy.
result SGQuant reduces GNN memory footprint from 4.25x to 31.9x with minimal accuracy loss.
GBOC detects anomalies in time series data using granular-ball vectors.
problem Challenges in modeling normal behavior in dynamic, nonlinear time series data.
method Granular-ball Vector Data Description (GVDD) and Granular-ball One-Class Network (GBOC).
result GBOC improves anomaly detection in time series data.
Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular te…
Higher granularity in MoE models boosts expressivity exponentially.
problem Expressivity of Mixture-of-Experts models with varying granularity.
method Comparing models with different numbers of active experts (granularity).
result Exponential separation in network expressivity based on granularity.
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
Simultaneously estimates travel times and route choice model parameters.
problem Interdependent estimation of arc travel times and route choice model parameters.
method Maximum likelihood estimation for any differentiable route choice model.
result Strong performance in real-world data, even compared to arc travel time estimation methods.
New methods estimate heterogeneous causal effects at various levels.
problem Estimating causal effects at different levels of granularity.
method Modified Causal Forests approach for multiple treatment models.
result New estimators outperform existing methods in empirical studies.
New method for reworking music pieces using conditional autoregressive modeling.
problem Reimagining existing musical compositions while maintaining structure.
method Conditional autoregressive pipeline for efficient music recomposition.
result Diverse and structured generation of new content conditioned on chord sequence annotations.
We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine lea…
Chaos in cerebellar cells enhances complexity of neural patterns.
problem Understanding how cerebellar granular layer represents complex information.
method Constructed a model of cerebellar granular layer with gap junctions, evaluated using reservoir computing.
result Chaotic dynamics in the cerebellar granular layer produce complex and diverse output patterns.
New method detects anomalies in computing centers' logs.
problem Anomaly detection in continuously changing log data for predictive maintenance.
method Evolving granular classifiers using Fuzzy-set-Based evolving Modeling and evolving Granular Neural Network.
result Classification model prioritizes maintenance based on anomaly severity.
Dynamic segmentation algorithm improves NMT performance by favoring character-level processing.
problem Suboptimal static segmentation choices in NMT systems.
method Adaptive Computation Time algorithm for dynamic segmentation, trainable end-to-end.
result The model prefers character-level processing when given the freedom to navigate different segmentation levels.
Paper proposes a new method for hourly load forecasting using smart meter data.
problem Challenges in short-term load forecasting at fine granularity.
method Forecasting using Matrix Factorization (fmf) for hourly load forecasting.
result Significantly outperforms state-of-the-art methods in load forecasting.
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.
Machine learning uncovers hidden correlations in granular material behavior.
problem Predicting mechanical behavior of granular materials from particle size distributions.
method Used Discrete Element Method to generate packings, trained artificial Neural Network.
result Artificial Neural Network revealed hidden correlations between particle size distributions and mechanical behavior.
Unsupervised deep learning detects image quality issues without annotations.
problem Anomaly detection in images with quality issues.
method Use of deep generative models, specifically normalizing flows, for anomaly detection.
result Accurate semantic labeling and quality trends observed in images.
We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity g) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume ω and g. The dependence …
INGB improves oversampling for noisy imbalanced datasets.
problem Imbalanced, noisy, and complex datasets in classification problems.
method INGB uses granular balls to simulate spatial distribution and informed entropy for optimization, followed by nonlinear oversampling.
result INGB outperforms traditional linear sampling frameworks and algorithms on complex datasets.
Study active learning for multi-level user preferences in recommendation systems.
problem Efficiently learning user preferences through active querying in recommendation systems.
method Proposes a theoretically optimal active learning strategy based on Fisher information matrix for collective matrix factorization.
result Demonstrates strong improvements over active learning methods in personalized, cold-start, and noisy data settings.
Revisits granular models explaining firm growth rates and sizes.
problem Understanding the relationship between firm size and growth rate statistics.
method Developed new theoretical insights linking firm size and growth rate statistics within granular models.
result Growth volatility distribution is size-independent but fat-tailed, challenging granular models.
Graph Neural Networks model 3D granular flow simulations.
problem Accurate modeling of complex 3D granular flow processes.
method Graph Neural Networks approach to simulate 3D granular flow using LIGGGHTS.
result Machine learning trajectories match physical granular flow processes.
New method predicts bankruptcy by imputing missing data with granular semantics.
problem Missing data, high dimensional data, and class imbalance in bankruptcy prediction.
method Granular computing for missing data imputation with feature semantics and AI-driven pipeline.
result Efficient solution for big datasets with high imputation rates.
Study on systemic risk in European insurance sector, showing insurer connections during stress.
problem Understanding systemic risk connectedness in European insurance sector.
method Common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall.
result Insurers are a significant component of systemic risk connectedness, especially during stress episodes.
JIMA uses multi-level preference data to recommend composite items.
problem Recommending composite items efficiently with multi-level preference information.
method Joint Interaction Modeling (JIMA) approach that integrates multi-level preference data and interactions.
result JIMA outperforms advanced baselines in offline and online settings.
The paper breaks down AUC into cluster-level components for better model diagnostics.
problem Global AUC masks weaknesses in specific subpopulations, leading to financial or operational risks.
method Formal decomposition of AUC into intra- and inter-cluster components, comparing with other performance metrics.
result Allows practitioners to evaluate and diagnose model performance within and across clusters.
ELUQuant quantifies uncertainties in DIS events using BNNs and MNFs.
problem Uncertainty quantification in Deep Inelastic Scattering (DIS) events.
method Physics-informed Bayesian Neural Network with flow approximated posteriors.
result Effective extraction of kinematic variables x, Q2, and y with detailed event-level uncertainty. Network analysis reveals distinct financial relationships among Euro Area banks.
problem Understanding complex interbank relationships in the Euro Area.
method Multi-layer network approach using granular financial data.
result A more complete picture of the Euro Area interbank market topology.
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
problem Measuring place function similarity at fine spatial granularity.
method Trajectory embedding to reduce dimensions and measure similarity of place functions.
result Embedding similarity can be a metric proxy for place functions at fine spatial granularity.
Paper tackles cross-granularity few-shot learning with meta-embedder.
problem Few-shot learning with coarse labels and fine-grained testing.
method Meta-embedder that optimizes visual and semantic discrimination across coarse and fine classes.
result Meta-embedder achieves effective cross-granularity few-shot classification.
Develops MgCSL for discovering causal structures in high-dimensional data.
problem Discovering causal relationships from high-dimensional data with complex interplay of variables.
method MgCSL uses sparse auto-encoders for coarse-graining and multi-layer perceptrons for detailed analysis, introducing simplified acyclicity constraints.
result MgCSL outperforms existing methods and finds explainable causal connections in fMRI datasets.