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 suggestions help train technicians diagnose incidents faster.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…
Lean Copilot uses LLMs to assist theorem proving in Lean, improving efficiency and automation.
The paper analyzes how automated market makers can retain trading fees.
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
Paper uses LLMs for sector allocation, showing better returns.
Automates U.S. visa petition document classification and RFE response generation.
This paper uses deep reinforcement learning to automate electric transmission voltage control.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
Enhanced DQN model boosts trading performance with advanced techniques.
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-…
AMM finds optimal contract for LPs to maximize order flow.
The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-s…
As the number of publicly traded companies as well as the amount of their financial data grows rapidly, it is highly desired to have tracking, analysis, and eventually stock selections automated. There have been few works focusing on estimating the stock prices of individual companies. However, many of those have worke…
This paper automates tagging programming challenge descriptions.
Deep Reinforcement Learning automates match-3 game testing.
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds…
MACER accelerates error repair by modularly identifying and applying fixes.
Study automates feature selection and clustering for HFT stock price forecasting.
We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based information retrieval architecture, which consists of two parallel encoders to project messages and replies into a common feature representation…
AxCell automates results extraction from machine learning papers.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
Survey of AI in quant finance, from deep learning to LLMs.
Study evaluates five LLMs for financial report analysis, revealing performance differences and variability.
Optimal fees protect passive LPs in AMMs under varying market conditions.
Deep CNN monitors AM quality with high accuracy.
LLMs excel at summarizing and repairing complex models without needing full models.
AI tool automates blood segmentation from head CT scans after SAH.
Paper presents a GAN-based method to automate robust hedging.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
We use PDPs with confidence bands to explain HPO results.
Automated theorem prover proves non-orderability of groups.
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks. In this paper, we propose a powerful and efficient spectral kernel learning framework and learned ke…
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…
This paper argues for decolonizing AI alignment by incorporating open-source Hinduism concepts.
This paper surveys scalable automated alignment methods for LLMs.
A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is pro…
This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their ho…
One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work …
Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for uns…
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…
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…
New automated market makers for multi-asset trading.
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…
A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode…
DriveML automates machine learning tasks in R.