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.
Proposes joint LCA for multiview data to identify shared and view-specific components.
Paper reviews and compares NMF, PLSA, LBA, EMA, and LCA models.
Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into …
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product …
In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this s…
In order to face the expected increasing demand of energy crops without creating conflicts of land occupation sustainability, farmers need to find reliable alternatives in marginal agricultural areas where the production of food hardly ever is economically and environmentally sustainable. The purpose of this work was t…
Proposes HypCSE for enhanced hierarchical clustering.
The paper explores the relationship between joint mixability and negative dependence structures.
We discuss the general properties of the theory of joint invariants of a smooth Lie group action in a manifold. Many of the known results about differential invariants, including Lie's finiteness theorem, have simpler versions in the context of joint invariants. We explore the relation between joint and differential in…
FJS method improves multinomial classification accuracy.
Paper proposes a new method to evaluate joint risk under uncertainty.
Introduces joint Shapley values to measure feature importance in models.
Study proposes a new model for joint survival annuity valuation.
Estimates joint causal effects using single-variable interventions on nonlinear models.
Study joint invariants on symplectic spaces, extending group and space variations.
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Joint diffusion models improve data representation for both generation and prediction.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition.…
New Bayesian method for joint sparse parameter inference.
We discuss possible extensions of the classical Chern-Weil formalism to an infinite dimensional setup. This is based on joint work with Steven Rosenberg, joint work with Simon Scott and joint work with Jouko Mickelsson.
Unified theorem for deep and shallow joint-equivariant machines.
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i…
Paper uses non-Euclidean analysis to classify brain structure variations.
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We…
We employ the language of Cartan's geometry to present a model for studying vector spaces of Killing two-tensors defined in pseudo-Riemannian spaces of constant curvature under the action of the corresponding isometry group. We also discuss geometric properties of joint invariants of Killing two-tensors defined in the …
This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional dist…
Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the condi…
Proposes a new method for handling domain shift in samples with biases in both covariates and labels.
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
Develops a new framework for estimating joint probability distributions.
Researchers derived formulas for joint moments of elliptical distributions.
Surveying joint Gaussian graphical models to identify shared structures across domains.
Better signal detection in undersampled data using joint and cross covariances.
New method combines score lists using joint CDFs, improving computation.
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work l…
Introduces joint exclusivity (JE), a new form of negative dependence.
The paper presents a method to estimate joint interventional distributions from marginal interventional data.
Proposes method for eliciting non-parametric joint priors using normalizing flows.
Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.
Improved multimodal variational models capture more complex joint distributions.
We provide a distribution-free test that can be used to determine whether any two joint distributions and are statistically different by inspection of a large enough set of samples. Following recent efforts from Long et al. [1], we rely on joint kernel distribution embedding to extend the kernel two-sample test…
We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that…
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domai…
A statistical test of independence may be constructed using the Hilbert-Schmidt Independence Criterion (HSIC) as a test statistic. The HSIC is defined as the distance between the embedding of the joint distribution, and the embedding of the product of the marginals, in a Reproducing Kernel Hilbert Space (RKHS). It has …