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.
Deep neural network approximates flow averages for rough walls in multiscale simulations.
New method uses small perturbations to improve representation learning from few labels.
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all…
Study investigates micro-event detection on FLOSS version releases from Stack Overflow.
Paper presents a novel neural network method for topic detection in micro-blogs.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
Micro-panel data are collected and analysed in many research and industry areas. Cluster analysis of micro-panel data is an unsupervised learning exploratory method identifying subgroup clusters in a data set which include homogeneous objects in terms of the development dynamics of monitored variables. The supply of cl…
The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward. This is limiting in applications where we are concerned with not only the expected performance, but also the distribution of the performance. In this paper, we introduce micro-objective reinforcement le…
In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sens…
Useful alpha returns vanished in modern stock markets.
Permutation approach is suggested as a method to investigate financial time series in micro scales. The method is used to see how high frequency trading in recent years has affected the micro patterns which may be seen in financial time series. Tick to tick exchange rates are considered as examples. It is seen that var…
Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopi…
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (…
Microdata improves inflation forecasts after major shocks, study finds.
A new model for defective media using two scales.
As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, w…
Credit Scores are ubiquitous and instrumental for loan providers and regulators. In this paper we showcase how micro-loan credit system can be developed in real setting. We show what challenges arise and discuss solutions. Particularly, we are concerned about model interpretability and data quality. In the final sectio…
L-modules are a combinatorial analogue of constructible sheaves on the reductive Borel-Serre compactification of a locally symmetric space. We define the micro-support of an L-module; it is a set of irreducible modules for the Levi quotients of the parabolic Q-subgroups associated to the strata. We prove a vanishing th…
A machine learning model captures non-Newtonian fluid dynamics from molecular details.
Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been consid…
Midicoth compresses online probability estimates by correcting prior smoothing biases.
In this paper we introduce a micro-clustering strategy for Functional Boxplots. The aim is to summarize a set of streaming time series splitted in non overlapping windows. It is a two step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-c…
We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t…
Exact solver speeds up Weston-Watkins SVM subproblem significantly.
Optimizes neural networks with blackbox solvers using Time-cost Regularization.
Six AI solutions accurately detect growth plate planes in mice bone scans.
Study analyzes 3,171 stocks to pick efficient portfolios using quantum and classical solvers.
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
The paper speeds up hyperparameter optimisation in Gaussian processes.
New solver MPLP++ outperforms existing solvers for dense graph models.
Study detects spoofing in high-frequency trading using micro-structural analysis.
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to…
NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN,…
CRA improves UL-based CO solvers by dynamically smoothing and enforcing discreteness.
Extends micro-price concept to RFQ markets for fair pricing.
Study compares 5 ODE solvers on 3 case studies, finding varying accuracy.
Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, computing resources are consolidated and shared by different applications. In this scenario, elasticity and proper load balancing are vital to…
A new method combines classical and machine learning PDE solvers efficiently.
Higher-order ODE solvers improve deep learning performance.
SA-Solver improves stochastic sampling from DPMs.
Cyclic Data Parallelism reduces memory usage and balances gradient communications.
mSAM improves generalization by making models flatter.
Accelerates data generation in score-based models.
Although optimization is the longstanding algorithmic backbone of machine learning, new models still require the time-consuming implementation of new solvers. As a result, there are thousands of implementations of optimization algorithms for machine learning problems. A natural question is, if it is always necessary to…
New taxonomy and improved solvers for discrete energy minimization.
End-to-end trainable graph matching using improved combinatorial solvers.
Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However,acquiring a sufficiently large training dataset remains a daunting task due t…
MIP-GNN uses graph neural networks to predict variable biases for MIP solvers.