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.
A new traffic signal control method using phase competition.
We develop a mean-field theory for multi-component ICA in high dimensions.
We explain how neural networks learn to solve modular addition tasks.
High-dimensional random geometry shows phase transitions in various problems.
Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and …
Paper proposes a new method for sparse phase retrieval with fewer measurements.
A neural network learns phase space properties for time series analysis.
Study improves communication efficiency in RIS-assisted downlink communication.
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper present…
In the early phases of the product life cycle, the costs controls became a major decision tool in the competitiveness of the companies due to the world competition. After defining the problems related to this control difficulties, we will present an approach using a concept of cost entity related to the design and real…
SGD shows distinct phases in learning single-index models, achieving optimal sample complexity and regret.
DEVDAN adapts to changing data streams by dynamically adding and removing hidden units.
Often the challenge associated with tasks like fraud and spam detection[1] is the lack of all likely patterns needed to train suitable supervised learning models. In order to overcome this limitation, such tasks are attempted as outlier or anomaly detection tasks. We also hypothesize that out- liers have behavioral pat…
We study the competitive equilibrium of large random economies with linear activities using methods of statistical mechanics. We focus on economies with commodities, firms, each running a randomly drawn linear technology, and one consumer. We derive, in the limit with fixed, a complete de…
In this study, we propose the integration of competitive learning into convolutional neural networks (CNNs) to improve the representation learning and efficiency of fine-tuning. Conventional CNNs use back propagation learning, and it enables powerful representation learning by a discrimination task. However, it require…
New method uses neural networks for accurate angle estimation in noisy conditions.
Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical-mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic descriptio…
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel mul…
New method accelerates Pommerman training with imitation and reinforcement learning.
We propose a simple quantitative model of Schumpeterian economic dynamics. New goods and services are endogenously produced through combinations of existing goods. As soon as new goods enter the market they may compete against already existing goods, in other words new products can have destructive effects on existing …
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e.g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data. The key insight of our optimization scheme for reconstructing the time-frequency information is that when a nonparame…
Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresse…
Deep RL optimizes compiler passes for better performance.
VSE estimates complex processes from noisy measurements without a model.
GTBO uses group testing to optimize high-dimensional functions efficiently.
Agents trained with reinforcement learning deviate from Nash equilibrium in optimal execution game.
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-the-art DL methods have additional constraints included in the learning stages. These various constraints, however, lead to additional computa…
Paper improves GNN inference speed and memory usage.
Agents learn sophisticated tool use and coordination in hide-and-seek.
Neural networks learn spectral representations for group composition.
In statistical learning for real-world large-scale data problems, one must often resort to "streaming" algorithms which operate sequentially on small batches of data. In this work, we present an analysis of the information-theoretic limits of mini-batch inference in the context of generalized linear models and low-rank…
CycleQSM uses deep learning to accurately map tissue magnetic susceptibility without needing paired data.
Solves Dirichlet problem for Lagrangian phase equation with critical and supercritical phase.
Early stopping improves generalization in overparameterized diffusion models.
Study coevolutionary trading-agent dynamics in continuous strategies.
A major issue in harmonic analysis is to capture the phase dependence of frequency representations, which carries important signal properties. It seems that convolutional neural networks have found a way. Over time-series and images, convolutional networks often learn a first layer of filters which are well localized i…
Paper uses SGLD to recover signals from generative models, proving convergence under mild conditions.
By incorporating market impact and asymmetric sensitivity into the evolutionary minority game, we study the coevolutionary dynamics of stock prices and investment strategies in financial markets. Both the stock price movement and the investors' global behavior are found to be closely related to the phase region they fa…
New algorithms handle phase retrieval with rank d measurements, revealing phase transitions.
We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision, and signal processing. In these contexts, "noise" generically refers to any contribution to the data that is not captured by the low-rank m…
New estimators improve efficiency in two-phase designs with coarsened data.
Paper develops estimates for Lagrangian phase changes in 2D.
Historical documents present many challenges for offline handwriting recognition systems, among them, the segmentation and labeling steps. Carefully annotated textlines are needed to train an HTR system. In some scenarios, transcripts are only available at the paragraph level with no text-line information. In this work…
CVAE detects weak complex signals in maritime radar, improving detection over classical methods.
Machine learning predicts phase behavior in active matter suspensions.
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respect…
Machine learning approximates phase transitions using Fisher information.
UPR hybrid model improves phase retrieval performance.