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.
Interactive DR framework for comparing datasets.
Surrogate-based analysis of interactions via local effect smooths
A new method detects interactions in neural networks using topological analysis.
New method aggregates GDS analyses of randomly selected interaction models to identify important factors in screening experiments.
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide …
Interactive EMA combines multiple explainability methods to improve model understanding.
Improved sentiment analysis with multimodal data.
New models for analyzing microbiome data with interactions.
We describe and extract time-ordered multibody interactions from complex systems.
This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original -dimensional feature vector is propose…
In this paper, we propose a hybrid bankcard response model, which integrates decision tree based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions. Then in the second stage, t…
Bayesian method selects interacting regions in Markov models.
Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the in…
MoReL models multi-omics data to find hidden molecular interactions.
Study generalizes non-interaction theorems for relativistic systems.
Modeling continuous movement of entities in latent space for interaction timing.
Methodology for learning sparse models using all multiplicative interactions efficiently.
Study improves LLMs for PPI analysis by addressing uncertainty.
Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics. The analysis rev…
Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms…
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.
Develops an efficient method for real-time data analysis and visualization.
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
Automates finding interactions in GLMs using neural networks.
The paper proposes a method to learn from both simulation and real-world data.
We apply the recently developed reduced Google matrix algorithm for the analysis of the OECD-WTO world network of economic activities. This approach allows to determine interdependences and interactions of economy sectors of several countries, including China, Russia and USA, properly taking into account the influence …
In this work the system of agents is applied to establish a model of the nonlinear distributed signal processing. The evolution of the system of the agents - by the prediction time scale diversified trend followers, has been studied for the stochastic time-varying environments represented by the real currency-exchange …
Study identifies cancer genes through graph anomaly analysis of protein interactions.
Emulator speeds up landslide run-out modeling sensitivity analysis.
Study reveals self-attention's role in learning and generalizing interactions.
We develop a general framework to analyze the distribution functions of wealth and income. Within this framework we study wealth distribution in a society by using a model which turns on two-party trading for poor people while for rich people interaction with wealthy entities (huge reservoir) is relevant. At equilibriu…
Feature interactions can contribute to a large proportion of variation in many prediction models. In the era of big data, the coexistence of high dimensionality in both responses and covariates poses unprecedented challenges in identifying important interactions. In this paper, we suggest a two-stage interaction identi…
New measures and tests for high-order interactions in complex data.
iConViz helps banks manage default contagion risk in networked loans.
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo…
2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. G…
Gaussian process framework learns interaction kernels in multi-species particle systems.
Graphon game model simplifies stochastic interactions among agents.
For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because it can vary in the domain of the variables. Importance can be assessed locally with sensitivity analysis using general methods that rely on the model's predictions or their deri…
In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation…
Two-stream model recognizes affect from audio and video.
Improved item recommendations for repeat interactions using sequence analysis.
i-cNRL learns network differences with interpretability.
New algorithm solves complex mean-field Schrödinger bridge problem.
Analysis of Vlasov plasma dynamics using matched pair Lie-Poisson formulation.
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyz…
CBNNs model survival with time-varying interactions, outperforming other methods.