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.
Feature networks link ML features via graph structure for enhanced learning.
Feature selection has been proven a powerful preprocessing step for high-dimensional data analysis. However, most state-of-the-art methods tend to overlook the structural correlation information between pairwise samples, which may encapsulate useful information for refining the performance of feature selection. Moreove…
We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from p…
New insights on how weight structure affects generalization in deep Gaussian feature models.
The creation of social ties is largely determined by the entangled effects of people's similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of t…
Study predicts SGD test loss for structured features.
Paper proposes a new method for joint feature selection and graph learning.
Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information los…
Model uses LLM features to predict stock returns effectively.
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
This paper develops a geometric framework for SHM using feature bundles and gauge theories.
GISST interprets GNNs by combining attention and sparsity for graph structure and node feature importance.
Paper proposes learnable topological features for efficient phylogenetic inference.
Novel unsupervised feature selection method using multi-step Markov transition probability.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
Dropout as a common regularizer to prevent overfitting in deep neural networks has been less effective in convolutional layers than in fully connected layers. This is because Dropout drops features randomly, without considering local structure. When features are spatially correlated, as in the case of convolutional lay…
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previo…
Proposes a method for multi-view clustering that considers local structures and feature weights.
Fast feature selection for SHM using canonical correlation.
Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution op…
Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providi…
Paper presents a novel approach for global feature aggregation in Graph Neural Networks.
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…
This paper improves GNN robustness by aligning feature and adjacency matrix learning.
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on thei…
dGAP learns feature dependencies and predicts targets simultaneously.
GTDL methods fail to accurately model feature interactions in tabular data.
Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, fee…
Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selection methods usually cannot keep more local structure information. To address the defects previously mentioned, we propose a novel supervised o…
Characterizes test error in learning with deep, structured feature maps.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
New operator reveals unique features of sl(N) link homology.
Online algorithm detects and removes spurious features for strong generalization.
Pipeline learns topological features for protein stability prediction.
Proposes FSM-IRL to learn invariant network representations considering feature and structural shifts.
RTFN extracts robust temporal features for time series analysis.
Problems in machine learning (ML) can involve noisy input data, and ML classification methods have reached limiting accuracies when based on standard ML data sets consisting of feature vectors and their classes. Greater accuracy will require incorporation of prior structural information on data into learning. We study …
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of su…
A novel federated learning framework resolves structural misalignment in model fusion.
We present a method for finding high density, low-dimensional structures in noisy point clouds. These structures are sets with zero Lebesgue measure with respect to the -dimensional ambient space and belong to a dimensional space. We call them "singular features." Hunting for singular features corresponds to f…
In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before). We compare multiple models on different s…
Enhances graph neural networks by considering feature similarities in node aggregation.
GraphSTONE uses topic models to capture graph structures, improving GCN performance.
Sublinearly structured DNNs achieve feature learning consistency for compositional functions.
We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in ord…
Proposes SimPool for graph pooling using structural similarity features.
Proposes a new model to predict polymer properties by integrating various data types.