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.
The paper argues for better experimental reporting to avoid misinterpretation of model performance.
New method prevents cherry-picking in machine learning reports.
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state…
We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a…
Study improves risk evaluation timing with right-censored reporting delays.
CLARA generates clinical reports from raw inputs, improving accuracy and efficiency.
Framework integrates financial and annual report data for better corporate credit ratings.
The report analyzes Legendre decomposition for tensor data.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
Paper improves privacy-preserving measurement of advertising incrementality.
The paper explores clustering methods using Bregman divergences.
Improved AI model predicts construction safety outcomes from incident reports.
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness is set at maximum, KES co…
A new method reduces computational cost for gene expression inference in large microarray data sets.
This paper analyzes financial sentiment using LLMs and FinBERT, improving accuracy with few-shot examples.
Machine learning forecasts show bias at long horizons, contrary to standard tests.
We give a construction of hyperbolic 3-manifolds with rank two fundamental groups and report an experimental search to find such manifolds. Our manifolds are all surface bundles over the circle with genus two surface fiber. For the manifolds so obtained, we then examine whether they are of Heegaard genus two or not. As…
We report a general technique to study a given experimental time series with superstatistics. Crucial for the applicability of the superstatistics concept is the existence of a parameter that fluctuates on a large time scale as compared to the other time scales of the complex system under consideration. The propose…
Study predicts factuality and bias of news media sources.
This project compares MCMC and VI for Bayesian PMF on MovieLens.
We present a system that enables rapid model experimentation for tera-scale machine learning with trillions of non-zero features, billions of training examples, and millions of parameters. Our contribution to the literature is a new method (SA L-BFGS) for changing batch L-BFGS to perform in near real-time by using stat…
Paper optimizes fiber optic communication constellations using machine learning.
Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables im…
L-Perceptron improves breast cancer diagnosis and survival prediction.
Extracts patterns from mobile network data for better resource management.
The analysis of data sets arising from multiple sensors has drawn significant research attention over the years. Traditional methods, including kernel-based methods, are typically incapable of capturing nonlinear geometric structures. We introduce a latent common manifold model underlying multiple sensor observations f…
Interpretable semi-supervised classifier for black-box models with two self-labeling strategies.
Paper presents a data-driven method for option pricing.
liquidSVM is a package written in C++ that provides SVM-type solvers for various classification and regression tasks. Because of a fully integrated hyper-parameter selection, very carefully implemented solvers, multi-threading and GPU support, and several built-in data decomposition strategies it provides unprecedented…
While several approaches to face emotion recognition task are proposed in literature, none of them reports on power consumption nor inference time required to run the system in an embedded environment. Without adequate knowledge about these factors it is not clear whether we are actually able to provide accurate face e…
Researchers measure distances between quantum states to speed up machine learning.
Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative filtering techniques -- both classic and recent state-of-the-…
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem…
Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an uncertainty evaluation strategy. However, the uncertainty evaluation always suffers from performance degeneration when the initial labeled …
The paper benchmarks OS with RCTs, accounting for right-censoring.
Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay an…
In this paper, the task-related fMRI problem is treated in its matrix factorization formulation, focused on the Dictionary Learning (DL) approach. The new method allows the incorporation of a priori knowledge associated both with the experimental design as well as with available brain Atlases. Moreover, the proposed me…
We address feature interpretation and reproducibility issues in dense nets, proposing a modified loss function.
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individ…
In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hyp…
This paper improves domain adaptation methods using graph embedding.
Study classifies pathology reports using TF-IDF features and machine learning.
This study uses NLP to predict stock performance based on analyst reports.
The paper explores deep image priors for solving inverse problems.
New experiments show Gauss diagrams not all as simple as previously thought.
Deep learning model improves corporate distress prediction using text data.
A new deep generative model uses BSDEs for high-dimensional data generation.
Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that has gained substantial popularity due to their effectiveness, ease of use and potential for compact hardware implementation. An ESN contains the three network layers input, reservoir and readout where the reservoir is the truly recurrent netw…