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.
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.
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.
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.
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
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.
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.
Employs granular data to create a multilayer network for euro area banks, revealing distinct risk patterns.
problem Lack of comprehensive, granular data integration for systemic risk assessment in euro area banks.
method Constructs an empirically grounded multilayer network integrating various supervisory and statistical datasets, each layer representing a distinct transmission channel.
result Cross-layer heterogeneity in connectivity and centrality reveals economically relevant structure and misidentifies systemically important institutions.
Reducing ICD-10 code granularity improves cost model accuracy and stability.
problem High-dimensional regression with ICD-10 codes leads to unstable coefficient estimates.
method Log-linear analytics approach to cost model regularization through diagnostic code merging.
result Reducing ICD-10 code granularity from 7 characters to 6 or fewer improves model interpretability and consistency.
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.
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.
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries…
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.
BGNNs model particle-boundary interactions efficiently.
problem Efficiently modeling geometric boundaries in 3D simulations.
method Introduce Boundary Graph Neural Networks (BGNNs) to dynamically modify graph structures.
result BGNNs accurately reproduce 3D granular flows without handcrafted conditions.
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.
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.
We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can he…
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.
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.
Paper tackles overestimation bias in continuous control, improving performance by 25%.
problem Overestimation bias in off-policy learning.
method Truncated Quantile Critics (TQC) combines distributional representation, truncation, and ensembling of critics.
result TQC outperforms state-of-the-art methods by 25% on the Humanoid environment.
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.
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.
We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is train…
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.
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.
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.
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…
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.
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.
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.
OMBA learns product and user representations for better online market basket analysis.
problem Limited ability to uncover rarely occurring and temporal associations in MBA.
method Jointly learns product and user representations, captures temporal dynamics, scalable online method.
result OMBA outperforms state-of-the-art methods by 21% on real-world datasets.
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.
In information retrieval, a fundamental goal is to transform a document into concepts that are representative of its content. The term "representative" is in itself challenging to define, and various tasks require different granularities of concepts. In this paper, we aim to model concepts that are sparse over the voca…
Paper offers guidelines for using ML in cyber security, focusing on botnet detection.
problem Lack of public benchmark datasets for evaluating ML-based cyber security systems.
method Provided concrete guidelines and recommendations for using supervised ML in cyber security, focusing on botnet detection.
result Ensemble models are well-suited to handle class imbalance in botnet detection.
We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account. The model relies on the concept of ordinal nature of utility, which better corresponds to actual user perception. In addition to that, unlike the majority of h…
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
Paper tackles sparse, unstructured data tensors by optimizing adaptive aggregation granularity.
problem Sparse, unstructured data tensors are hard to analyze.
method Introduces IceBreaker, a greedy algorithm that optimizes adaptive tensor aggregation.
result IceBreaker constructs tensors with high structure quality from various datasets.
We introduce a multiscale supervised dimension reduction method for SPatial Interaction Network (SPIN) data, which consist of a collection of spatially coordinated interactions. This type of predictor arises when the sampling unit of data is composed of a collection of primitive variables, each of them being essentiall…
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are…
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
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.
Money analyzed as a multidimensional tensor for better economic policy.
problem Economic complexity and policy responsiveness.
method Tensor analysis of money dynamics.
result Enhanced economic policy design and resilience.
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.
Interpretable representations improve explainable AI by translating complex data into understandable concepts.
problem Many explainers use interpretable representations but overlook their full potential and assumptions.
method An in-depth analysis of interpretable representations for tabular, image, and text data, identifying strengths, weaknesses, and desiderata.
result Linear model quantifies interpretable concepts' influence on black-box predictions, revealing their explanatory properties and manipulability.
BitPruning learns optimal bitlengths for neural networks to balance accuracy and efficiency.
problem Finding the minimum bitlength for neural network accuracy.
method A training method that penalizes large bitlengths and minimizes other quantifiable criteria.
result The method learns efficient representations while maintaining accuracy, reducing bitlengths by 3.76 bits on average per layer.
A method uses Wasserstein clustering to simplify financial data analysis.
problem Processing and analyzing granular financial data with missing values and identifying clusters.
method Variant of Lloyd's algorithm applied to probability distributions, using Wasserstein barycenters.
result Demonstrated usefulness in financial regulation context.