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.
CVAE improves stock volume forecasting with advanced input variables.
Novel mutual information bound improves statistical inference rates.
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based …
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient clinical information and administrative healthcare tasks, many researchers have found secondary use of these records for various clinical informatics tasks…
Physics-informed machine learning models improve biomolecular system simulations.
Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to a wide variety of threats that could …
Technical proofs for Radon-Nikodym derivative identities.
Study examines the training process of an unsupervised learning model for detecting gravitational-wave transient noise.
Deep learning models improve financial price forecasting accuracy.
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, which is a critical bottleneck particularly for high-dimensional data. This pr…
Advances in collaborative filtering and ranking methods.
A new method uses heat diffusion to efficiently solve combinatorial optimization problems.
Interest prohibition theory concerns theoretical aspects of interest prohibition. We attempt to lay down some aspects of interest prohibition theory wrapped in a larger framework of informal logic. The reason for this is that interest prohibition theory has to deal with a variety of arguments which is so wide that a li…
Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor's outputs to its inputs. This paper uses a recent advance in the foundations of information theory to study the properties of local processors that u…
FinAgent tackles financial trading with multimodal data and advanced AI.
Long-term lane change prediction model predicts maneuvers with 75% accuracy.
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of…
Advanced ML/DL models predict stock prices using technical analysis.
Framework optimizes expensive manufacturing processes efficiently.
Estimates reliability of nuclear fuel using advanced modeling techniques.
TelePiT improves S2S forecasting by integrating physics and teleconnections.
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such …
Informer improves option pricing accuracy in volatile markets.
Framework evaluates quality of synthetic data generated with differential privacy.
Advances in asynchronous optimization methods for machine learning.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
Self-Distilled Disentanglement improves counterfactual predictions by separating variables.
High-dimensional statistics advances in complex data domains.
The study extracts market direction from transaction data.
The paper establishes theoretical foundations for low-rank knowledge distillation in LLMs.
An asymmetric information model is introduced for the situation in which there is a small agent who is more susceptible to the flow of information in the market than the general market participant, and who tries to implement strategies based on the additional information. In this model market participants have access t…
In financial markets valuable information is rarely circulated homogeneously, because of time required for information to spread. However, advances in communication technology means that the 'lifetime' of important information is typically short. Hence, viewed as a tradable asset, information shares the characteristics…
Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perception and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information process…
Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while ob…
Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep lea…
New MIF architecture improves posterior approximations in Bayesian models.
We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. …
Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned. We propose an information-theoretic approach to characterizing disentanglement and dependence in representation learning using mu…
Study on pricing rules for income streams with partial insider information.
We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative inversion models to express a distribution over input features conditioned on intermediate model representations. Insights into the invariance…
New quantum states capture more information, enabling advanced processing tasks.
Paper explores physics-informed deep learning for system reliability assessment.
High-frequency traders can act as either small informed traders or round-trippers, affecting price discovery and liquidity.
New method removes unwanted information from representations efficiently.
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has …
JIMA uses multi-level preference data to recommend composite items.
Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables. This manuscript goes beyond classical sparsity by proposing efficient algorithms for approximate inference via information projection that are applica…