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.
Hierarchical NMF organizes COVID-19 literature into a searchable tree.
HIRM models noisy, sparse, heterogeneous relational data using hierarchical clustering and Dirichlet processes.
Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have hierarchical relations, and classifiers exploiting …
Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.
Advances combinatorial complexes for better modeling of hierarchical and set-type relations.
A new drug embedding method using hierarchical drug relations and chemical structures.
This work improves KG embeddings by integrating hyperbolic and attention mechanisms.
Proposes a method to improve hierarchical clustering using set-level structural priors.
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues. However, multi-relational knowledge graphs often exhibit multiple simultaneous hierarchies, which current hyperbolic models do not c…
Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, …
Proposes a new Bayesian score for learning network structure from related datasets.
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical…
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchi…
Bayesian models forecast COVID-19 hospitalizations at single sites.
We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previous…
Researchers identify latent variables and causal structures from nonlinear hierarchical models.
Bayesian algorithm improves word representations using semantic taxonomy.
A simple guide to understanding hierarchical causality in complex systems.
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations.…
Random quotients preserve hyperbolic properties in groups.
Proposes a hierarchical curriculum loss to improve model accuracy and interpretability.
Translating renderings (e. g. PDFs, scans) into hierarchical document structures is extensively demanded in the daily routines of many real-world applications. However, a holistic, principled approach to inferring the complete hierarchical structure of documents is missing. As a remedy, we developed "DocParser": an end…
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance sem…
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the source-specific data models are complex, or if the number of sources is very large. To facilitate computation, we propose…
BoRA finetunes multi-task LLMs by sharing information through hierarchical priors.
Paper uses HGNN to predict stock types from relationships and temporal data.
Algorithm refines matrix ratings using hierarchical graph clustering.
The paper reviews and extends calibration concepts for classification and regression.
Randomized hierarchical clustering tests for stability and detects clusters.
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hi…
This work tackles posterior collapse in conditional and hierarchical VAEs.
Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals. In order to infer imagined speech from active thoughts, we propose a novel hierarchical deep learning BCI system for subject-indepen…
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language…
New measure shows how LSTM models compose hierarchical representations.
The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctu…
Extends linear representation hypothesis to categorical and hierarchical concepts in LLMs.
Paper improves Bayesian network learning from related data sets.
Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…
This paper presents theory for Normalized Random Measures (NRMs), Normalized Generalized Gammas (NGGs), a particular kind of NRM, and Dependent Hierarchical NRMs which allow networks of dependent NRMs to be analysed. These have been used, for instance, for time-dependent topic modelling. In this paper, we first introdu…
A novel unsupervised domain adaptation method using hierarchical optimal transport.
We prove that all hierarchically hyperbolic spaces have finite asymptotic dimension and obtain strong bounds on these dimensions. One application of this result is to obtain the sharpest known bound on the asymptotic dimension of the mapping class group of a finite type surface: improving the bound from exponential to …
Method preserves order in hierarchical clustering of ordered data.
The study reveals the hierarchical structure of the international FOREX market using currency fluctuation distribution similarities.
CoHiRF extends clustering methods to handle high-dimensional data efficiently.
Formalizes concepts as latent variables in hierarchical models for high-dimensional data.
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typ…
Tail-GNNs improve protein function prediction using relational reinforcement.
New hierarchical model improves on standard practice for high-dimensional data.