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.
This work analyzes fairness-accuracy trade-offs using causal methods.
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attri…
Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between th…
Value selection reduces model size while maintaining accuracy.
New method selects data points for better model performance.
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with guidance from specialized loss-functions. First, we utilize a modified per-point loss that addresses missing LiDAR point measurements. Secon…
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a …
Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree…
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
CoroNet detects COVID-19 from chest X-rays with high accuracy.
The study reduces a personality measurement instrument to 10 features with minimal loss of accuracy.
This paper presents a novel unifying framework of bilinear LSTMs that can represent and utilize the nonlinear interaction of the input features present in sequence datasets for achieving superior performance over a linear LSTM and yet not incur more parameters to be learned. To realize this, our unifying framework allo…
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
Robust cancer screening model using pre-trained ensembles for biomarkers.
RS-NN predicts belief functions for classification, improving accuracy and uncertainty estimation.
The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and…
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavi…
Paper designs optimal ECOCs using IP for robust multiclass classification.
New linear algorithms improve wSVMs for multiclass probability estimation.
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how…
Bayesian method improves few-shot classification accuracy.
A new framework for robust transfer learning that avoids negative transfer in domains with unequal information.
Any given classification problem can be modeled using multi-class or One-vs-All (OVA) architecture. An OVA system consists of as many OVA models as the number of classes, providing the advantage of asynchrony, where each OVA model can be re-trained independent of other models. This is particularly advantageous in setti…
Study shows financial value of weak information converges in discrete vs continuous markets.
Hybrid quantum-classical model boosts S&P 500 prediction accuracy to 60.14%.
Deep learning classifies autism vs controls with high accuracy using large fMRI dataset.
In recent years, the number of papers on Alzheimer's disease classification has increased dramatically, generating interesting methodological ideas on the use machine learning and feature extraction methods. However, practical impact is much more limited and, eventually, one could not tell which of these approaches are…
A common method of generalizing binary to multi-class classification is the error correcting code (ECC). ECCs may be optimized in a number of ways, for instance by making them orthogonal. Here we test two types of orthogonal ECCs on seven different datasets using three types of binary classifier and compare them with t…
Study reveals how initialization scale affects training accuracy in linear networks.
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles origin…
Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
Quantitative CT predicts ILD patterns and prognosis.
Proposes a new adversarial model to avoid accuracy vs. adversarial accuracy tradeoff.
Single deep model detects out-of-distribution data with single forward pass.
VR game data for P300 BCI with raccoon vs demon stimuli.
This paper studies the optimal risk-averse timing to sell a risky asset. The investor's risk preference is described by the exponential, power, or log utility. Two stochastic models are considered for the asset price -- the geometric Brownian motion and exponential Ornstein-Uhlenbeck models -- to account for, respectiv…
Study finds discrepancies in model explanations for clean vs. adversarial inputs.
This paper proposes a method for multi-class classification problems, where the number of classes K is large. The method, referred to as Candidates vs. Noises Estimation (CANE), selects a small subset of candidate classes and samples the remaining classes. We show that CANE is always consistent and computationally effi…
FinCARE combines financial data and AI reasoning to improve causal analysis of financial performance.
New methods show robustness and accuracy can coexist.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of was achieved using a …
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
Emotions play a crucial role in human interaction, health care and security investigations and monitoring. Automatic emotion recognition (AER) using electroencephalogram (EEG) signals is an effective method for decoding the real emotions, which are independent of body gestures, but it is a challenging problem. Several …
The paper analyzes RfQ processes on MD2C platforms using probabilistic models.
Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured d…