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.
MONET debiases graph embeddings by training on metadata-orthogonal dimensions.
Adding metadata abruptly changes network inference outcomes.
Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we i…
Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.
The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ul…
We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships…
Enhances topic-metadata relationship modeling using Bayesian methods.
Paper presents LLM-enhanced contract metadata extraction.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate …
LLMs learn to recommend models and hyperparameters from dataset metadata.
Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customizatio…
The study uses supervised learning to classify research data by discipline.
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network, geographic location of nodes in the Internet, or cellular function of nodes in a gene regulatory network. Here we demonstrate how th…
Study improves ECG analysis accuracy using state space models, self-supervised learning, and patient metadata.
AutoML uses dataset and algorithm descriptions to improve performance.
Meta-learning improves model performance by optimizing data acquisition.
Effectively modelling hidden structures in a network is very practical but theoretically challenging. Existing relational models only involve very limited information, namely the binary directional link data, embedded in a network to learn hidden networking structures. There is other rich and meaningful information (e.…
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-de…
Improved speaker verification with condition-aware backend.
Improved 3D MRI classification using contrastive learning with continuous proxy metadata.
SEMASIA provides a large dataset of latent representations for model comparison.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
New feature mapping approach improves recommendation accuracy and explainability.
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and pati…
In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Ty…
The paper examines how NFT valuations correlate with market data and social trends.
Survey classifies Clustered Federated Learning into three types of approaches.
FairGround offers a diverse dataset corpus for fair ML research.
New method uses LLMs to extract financial insights from Q&A sections of reports.
Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the resear…
A novel multilayer network approach for text analysis.
Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian m…
Zero-shot understanding of accidents from surveillance videos using vision-language models
SAVeD detects dataset versions without metadata, improving accuracy and separation.
We describe a novel neural network architecture for the prediction of ventricular tachyarrhythmias. The model receives input features that capture the change in RR intervals and ectopic beats, along with features based on heart rate variability and frequency analysis. Patient age is also included as a trainable embeddi…
Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies which addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properti…
The study extends Jacobi-orthogonality to indefinite scalar product spaces.
New characterization of Osserman tensors using Jacobi-orthogonality.
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely…
Study isotropy groups for complex orthogonal and skew-symmetric matrices.
New findings on Kähler manifolds restrict orthogonal coordinates existence.
Constructs orthogonal coordinates in curved spaces.
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano's symbolic comput…
OPT framework improves neural network generalization by learning an orthogonal transformation.
The paper studies surfaces in a bounded domain with orthogonal boundaries and proves curvature estimates.
A typical way in which network data is recorded is to measure all the interactions among a specified set of core nodes; this produces a graph containing this core together with a potentially larger set of fringe nodes that have links to the core. Interactions between pairs of nodes in the fringe, however, are not recor…
Orthogonal random features approximate a Bessel kernel, offering sharper bounds than random Fourier features.