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.
Detects systematic anomalies in consumer complaints using NLP.
Enhances price sentiment index using survey comments.
A new credit scoring method using Gaussian Mixture Models.
LDA-XGB1 balances fairness and accuracy in lending models.
Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels. Using semi-synthetic data fr…
Automatic detection of emotion has the potential to revolutionize mental health and wellbeing. Recent work has been successful in predicting affect from unimodal electrocardiogram (ECG) data. However, to be immediately relevant for real-world applications, physiology-based emotion detection must make use of ubiquitous …
A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…
Efficient deep learning model classifies fractures from X-rays.
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
Analyzes new economic paradigm for non-independent consumer choices.
Method detects multi-timescale consumer spending patterns from receipts.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…
Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria typ…
Study finds consumers are more price-sensitive before livestreams than after.
A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
The success of neural networks in image classification has inspired various hardware implementations on embedded platforms such as Field Programmable Gate Arrays, embedded processors and Graphical Processing Units. These embedded platforms are constrained in terms of power, which is mainly consumed by the Multiply Accu…
Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition. The separab…
The study identifies extremal dependence in financial markets using a bootstrap-based testing procedure.
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/…
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…
New method learns credit prices offline without interaction.
Paper proposes a method to estimate consumer valuations from bundle sales data.
Study predicts purchasing decisions of online food delivery customers.
Study improves retail demand forecasting by integrating macroeconomic data.
Paper uses GANs to simulate consumer transactions with SKU constraints.
System designs for analyzing and pricing non-performing consumer credit portfolios.
GBS uses machine learning to design products based on consumer preferences.
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited b…
Proposes a model to optimize feedback for content creators on social media.
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ra…
Understanding consumption dynamics and its impact on the whole economy and welfare within the present economic crisis is not an easy task. Indeed the level of consumer demand for different goods varies with the prices, consumer incomes and demographic factors. Furthermore crisis may trigger different behaviors which re…
Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal n…
Paper stabilizes generative model training with synthetic data.
New model uses symmetries and scaling laws to predict consumer advertising response.
Paper presents a novel time series clustering algorithm for financial inclusion.
We study the effects of introducing information inefficiency in a model for a random linear economy with a representative consumer. This is done by considering statistical, instead of classical, economic general equilibria. Employing two different approaches we show that inefficiency increases the consumption set of a …
We discuss the stationary states of a model economy in which heterogeneous adaptive consumers purchase commodity bundles repeatedly from sellers. The system undergoes a transition from an inefficient to an efficient state as the number of consumers increases. In the latter phase, however, price fluctuations may…
There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or `green' benefits. Some consumers might value these benefits while others do not. However, as evidenced by myriad failed attempts of environmental p…
AL2 progressively penalizes network activations to prevent overfitting.
Algorithms optimize fair portfolios for diverse risk-tolerant consumers.
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her…
Enhanced word embedding creates new consumer-friendly health terms.
Current auto loans converge to super-prime credit despite remaining underwater.
We introduce a fully probabilistic framework of consumer product choice based on quality assessment. It allows us to capture many aspects of marketing such as partial information asymmetry, quality differentiation, and product placement in a supermarket.
Automates design of lightweight neural networks for image classification.