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.
Framework for pricing data products in data-poor markets.
The study improves load forecasting for electricity consumers using advanced machine learning models.
New method improves sales forecasting accuracy using tensor factorization.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
A unique challenge for e-commerce recommendation is that customers are often interested in products that are more advanced than their already purchased products, but not reversed. The few existing recommender systems modeling unidirectional sequence output a limited number of categories or continuous variables. To mode…
One of the most exciting technology breakthroughs in the last few years has been the rise of deep learning. State-of-the-art deep learning models are being widely deployed in academia and industry, across a variety of areas, from image analysis to natural language processing. These models have grown from fledgling rese…
In this paper, the agent-based modeling is employed to model the effect of intellectual property policy at the speed of technological advancement. Every agent has inborn preferences towards investing their capital into independent technological development, innovation appropriation, and production. The relative cost of…
AI-enhanced product embeddings boost demand analysis accuracy.
Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More than 30 years ago Mehra and Prescott f…
AI agent predicts industry and product/service codes for companies.
We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (…
Since governments give stimulus to firms and expect the spillover effect by fiscal policies, it is important to know the effectiveness that they can control the economy. To clarify the controllability of the economy, we investigate a firm production network observed exhaustively in Japan and what firms should be direct…
The paper tackles imbalance in production data by proposing sampling methods to improve model performance on underrepresented observations.
PoGDiff improves diffusion models on imbalanced datasets.
Hedging methods to mitigate the exposure of variable annuity products to market risks require the calculation of market risk sensitivities (or "Greeks"). The complex, path-dependent nature of these products means these sensitivities typically must be estimated by Monte Carlo simulation. Standard market practice is to m…
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under…
Modeling tech transfer to explain convergence in Central and Eastern Europe.
We survey different classification results for surfaces with parallel mean curvature immersed into some Riemannian homogeneous four-manifolds, including real and complex space forms, and product spaces. We provide a common framework for this problem, with special attention to the existence of holomorphic quadratic diff…
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
Introduces challenges and techniques for creating machine translation for indigenous languages.
This article investigates the eigenspectrum of the inner product-type kernel matrix under a binary mixture model in the high dimensional regime where the number of data and their dimension are both large and comparable. Based on…
Generative AI boosts analyst reports but increases forecast errors.
This paper tackles hidden technical debts in fair ML systems for Fintech.
Quantum models learn unitary actions on entangled states from product states.
HessFormer enables distributed Hessian computation for large models.
Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outc…
We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time.…
Model predicts epileptic seizures with high accuracy using EEG signals.
This paper advances theory on the process of collaboration between entities and its implications on the quality of services, information, and/or products (SIPs) that the collaborating entities provide to each other. It investigates the scenario of outsourced IS projects (such as custom software development) where the e…
We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical…
This work develops a fast-running ROM for MOOSE-based AM model using OL.
Lecture note for data science students on machine learning basics and advanced topics.
The paper analyzes learning curves for kernel ridge regression with dot-product kernels.
Survey on privacy issues in deep learning and proposed solutions.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
Stella Nera accelerates matrix multiplications with a hash-based approach, achieving high energy efficiency and accuracy.
This paper proposes AI-based solutions for optimizing semiconductor manufacturing processes.
DeRisk improves credit risk prediction using deep learning.
New SPD metrics improve stability and efficiency in neural networks.
In this article we give a classification of three dimensional m-quasi Einstein manifolds with two distinct Ricci-eigen values. Our study provides explicit description of local and complete metrics and potential functions. We also describe the associated warped product Einstein manifolds in detail. For the proof we pres…
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz "productivity" function. In each round, the learner must decide how mu…
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weig…
We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as…
This paper simplifies ANS for statisticians, making it easier to use.
New limits found for training deep learning models efficiently.
Paper proposes new methods for improving interatomic potentials.
Recent advances in spoken language technologies and the introduction of many customer facing products, have given rise to a wide customer reliance on smart personal assistants for many of their daily tasks. In this paper, we present a system to reduce users' cognitive load by extending personal assistants with long-ter…
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks. This limitation motivates a need for scalable parameterizations of the noise generation process, in a manner t…