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.
Benchmark tests LLMs on discovering physics laws in unconventional worlds.
We show that the theory of isothermic surfaces in $\E^3$ -- one of the oldest branches of differential geometry -- can be reformulated within the modern theory of completely integrable (soliton) systems. This enables one to study the geometry of isothermic surfaces in $\E^3$ by means of powerful spectral methods availa…
New models analyze how ECB's unconventional policies affect stock market volatility.
We suggest an original physical approach to describe the mechanism of market pricing. The core of our approach is to consider pricing at different time scales separately, using independent equations of motion. Such an approach leads to a pricing model that not only allows estimating the volatility of future market pric…
The topological properties of interbank networks have been discussed widely in the literature mainly because of their relevance for systemic risk. Here we propose to use the Stochastic Block Model to investigate and perform a model selection among several possible two block organizations of the network: these include b…
Equations for minimal surfaces from rigid motions in high dimensions.
Monte Carlo method trains deep neural networks without gradients.
The Laplace-Beltrami operator in the curved Möbius strip is investigated in the limit when the width of the strip tends to zero. By establishing a norm-resolvent convergence, it is shown that spectral properties of the operator are approximated well by an unconventional flat model whose spectrum can be computed explici…
The gauge principle is at the heart of a good part of fundamental physics: Starting with a group G of so-called rigid symmetries of a functional defined over space-time Sigma, the original functional is extended appropriately by additional Lie(G)-valued 1-form gauge fields so as to lift the symmetry to Maps(Sigma,G). P…
Deep networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more…
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids and spin nematics. However, discerning their existence and the correct characterization is usually challenging. Here we introduce a machine-…
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of r…
In this expository note we describe a surprising phenomenon in overparameterized linear regression, where the dimension exceeds the number of samples: there is a regime where the test risk of the estimator found by gradient descent increases with additional samples. In other words, more data actually hurts the estimato…
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates …
Cross-validation is one of the most popular model selection methods in statistics and machine learning. Despite its wide applicability, traditional cross validation methods tend to select overfitting models, due to the ignorance of the uncertainty in the testing sample. We develop a new, statistically principled infere…
We propose a consistent polynomial-time method for the unseeded node matching problem for networks with smooth underlying structures. Despite widely conjectured by the research community that the structured graph matching problem to be significantly easier than its worst case counterpart, well-known to be NP-hard, the …
Enhances image classification by integrating semantic hierarchy into CNN models.
The study classifies policy announcements' impact on stock market volatility.
A longstanding problem for Deep Neural Networks (DNNs) is understanding their puzzling ability to generalize well. We approach this problem through the unconventional angle of \textit{cognitive abstraction mechanisms}, drawing inspiration from recent neuroscience work, allowing us to define the Cognitive Neural Activat…
The paper examines how background risk affects portfolio selection and optimal reinsurance design.
Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random …
A new method boosts exploration in bandit algorithms, reducing regret.
Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.
Hyperinflation and price volatility in virtual economies has the potential to reduce player satisfaction and decrease developer revenue. This paper describes intuitive analytical methods for monitoring volatility and inflation in virtual economies, with worked examples on the increasingly popular multiplayer game Old S…
The higher order singular value decomposition (HOSVD) of tensors is a generalization of matrix SVD. The perturbation analysis of HOSVD under random noise is more delicate than its matrix counterpart. Recently, polynomial time algorithms have been proposed where statistically optimal estimates of the singular subspaces …
Study links in knitted textiles using knot theory.
Under mild regularity assumptions, the transport problem is stable in the following sense: if a sequence of optimal transport plans converges weakly to a transport plan , then is also optimal (between its marginals). Alfonsi, Corbetta and Jourdain asked whether the same property is true for th…
Improved GAN training speed and quality with FastGAN.
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.
L2R learns to denoise images without needing noise distribution knowledge.
Innovative ball bearing converts rotary to reciprocating motion.
New model captures fast price excursions in finance.
For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a limited amount of memory and computational resources in a timely manner for decision making. This often is coupled with the missing data pro…
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
Deep learning improves OFDFT for molecular systems.
An unconventional approach for optimal stopping under model ambiguity is introduced. Besides ambiguity itself, we take into account how ambiguity-averse an agent is. This inclusion of ambiguity attitude, via an -maxmin nonlinear expectation, renders the stopping problem time-inconsistent. We look for subgame perfect…
Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any ad…
PICN learns physical fields from shallow neural networks, improving AI in multi-physical systems.
Develops a new GLM framework for claims reserving with adaptive estimation.
Paper introduces a method to generate physically feasible dynamics with physical priors.
The paper modifies interest rate models to better fit current economic conditions.
The reservoir computing paradigm is employed to classify heartbeat anomalies online based on electrocardiogram signals. Inspired by the principles of information processing in the brain, reservoir computing provides a framework to design, train, and analyze recurrent neural networks (RNNs) for processing time-dependent…
Higher topos theory applied to physics.
Survey of integrating physics knowledge into machine learning models.
Discussing AI's difficulty and physics' simplicity, suggesting AI benefits from physics principles.
Proposes a physics-informed VAE for disentangling physics from confounding influences.
Special issue on understanding physical processes from unusual diffusion patterns.
Physics-informed kernel learning integrates physical priors into machine learning models.