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.
Automated theorem prover proves non-orderability of groups.
System uses machine learning and automated reasoning to speed up PBE synthesis.
Survey on automating geometry problem solving with large models.
KM-GPT automates IPD reconstruction from KM plots with high accuracy and scalability.
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. The…
Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by en…
RACER optimizes LLM-as-judge accuracy with dynamic reasoning selection.
Generative AI agents improve ERP systems by automating complex financial tasks.
Bayesian interpolants explain neural network inferences concisely.
We explore the application of automated reasoning techniques to unknot detection, a classical problem of computational topology. We adopt a two-pronged experimental approach, using a theorem prover to try to establish a positive result (i.e. that a knot is the unknot), whilst simultaneously using a model finder to try …
Study uses LLMs to automate data insights discovery.
RAG-IT automates financial analysis using LLMs and specialized datasets.
While variable selection is essential to optimize the learning complexity by prioritizing features, automating the selection process is preferred since it requires laborious efforts with intensive analysis otherwise. However, it is not an easy task to enable the automation due to several reasons. First, selection techn…
LLMs excel at summarizing and repairing complex models without needing full models.
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical models. Designing automated methods that are also both computationally scalable and theoretically sound, however, remains a significant chall…
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can s…
The paper establishes axioms for AMMs to ensure fair pricing and fee structures.
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
Decentralized prediction markets use AMMs to pool and withdraw liquidity, improving financial properties.
Tensor logic aims to unify AI types with scalable and transparent features.
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.…
Unified scoring model improves efficiency and performance across multiple tasks.
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.
Paper formalizes anti-discrimination law in automated systems.
To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources req…
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficie…
VERAFI improves financial AI by verifying calculations and compliance.
While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are neural networks that can be exploited towards this goal because of…
DBOT uses AI to automate long-term stock valuation.
FinMaster benchmarks LLMs in financial tasks, revealing gaps in reasoning.
Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.
FinRobot AI agent for equity research provides comprehensive insights.
Research proposes a decentralized invoice discounting system using Kelly criterion.
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variables the models naturally counteract the overfitting problem ubiquitous …
A Python approach minimizes risk in decentralized exchanges.
Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial m…
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reason…
This paper optimizes liquidity provision in automated market makers using auction theory.
ALMANACS benchmarks explainability methods on simulatability.
SOCRATES uses LLMs to automate simulation optimization of complex systems.
Despite remarkable advances in automated visual recognition by machines, some visual tasks remain challenging for machines. Fleuret et al. (2011) introduced the Synthetic Visual Reasoning Test (SVRT) to highlight this point, which required classification of images consisting of randomly generated shapes based on hidden…
In this paper, we demonstrate how to do automated theorem proving in the presence of a large knowledge base of potential premises without learning from human proofs. We suggest an exploration mechanism that mixes in additional premises selected by a tf-idf (term frequency-inverse document frequency) based lookup in a d…
In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, etc. Since the robo-advisory model is in its early stages, we estimate tha…
AutoML-GPT uses GPT to automate AI model training.
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data. However, the performance of inference algorithms can be dramatically affected …
In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop predictive models and share them with domain experts. To assess the system's impact, we implemented 3 different interfaces for creating pre…
Future autonomous systems need reliable world models and complex action sequences.
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap bet…