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 market-making for CBDCs and stable coins on blockchain.
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training or for training a newly synthesized model, one would not know the best learning rate regime beforehand. We propose an automated algorithm for…
Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate at each iteration, we propose an approach that automates the most common hand-tun…
The paper analyzes how automated market makers can retain trading fees.
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…
AutoRec automates deep recommendation models using AutoML.
Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We prop…
Improves reliability of BBVI optimization methods.
We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel…
Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection, which is dominated by deep learning based methods, seeks to improve early detection of polyps. However, current efforts rely heavily on the …
SALR improves deep learning generalization by dynamically adjusting learning rates.
Deep Reinforcement Learning automates match-3 game testing.
Automated detection of MS lesions improves to 67% with 7T MRI.
This study optimizes trading and arbitrage in decentralized finance's CPMs, revealing convexity costs and developing efficient strategies.
In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on mod…
Improves synthetic-to-real generalization without real data.
AutoCP automates the construction of accurate prediction intervals.
QLAMMP optimizes fees on AMMs using Q-Learning.
Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community. Recent developments in deep learning has led to considerable research activity directed towards improving the efficacy of automated pavement distress identification and rating. Deep learning …
Novel AMM model for pegged cryptoassets using nested OU processes.
Feature Learning aims to extract relevant information contained in data sets in an automated fashion. It is driving force behind the current deep learning trend, a set of methods that have had widespread empirical success. What is lacking is a theoretical understanding of different feature learning schemes. This work p…
Auto-Ensemble automates deep learning model ensembling with adaptive learning rate scheduling.
Deep learning improves material recognition in construction monitoring.
DriveML automates machine learning tasks in R.
A method to improve LLMs by automating the construction of a mixture of expert prompts.
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without e…
This paper analyzes and compares different Automated Market Maker mechanisms.
Optimal design of automated market makers for decentralized exchanges.
Automates design of lightweight neural networks for image classification.
Paper optimizes liquidity provision in decentralized finance markets.
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…
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Fe…
FinRL automates trading in quantitative finance with deep reinforcement learning.
Improved AMM protocol supports diverse loan maturities in DeFi.
Automated method creates compact chemical models from detailed ones, reducing complexity and improving accuracy.
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
Job transitions and upskilling are common actions taken by many industry working professionals throughout their career. With the current rapidly changing job landscape where requirements are constantly changing and industry sectors are emerging, it is especially difficult to plan and navigate a predetermined career pat…
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…
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…
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…
This paper surveys scalable automated alignment methods for LLMs.
Interview study reveals considerations for designing semi-automated bias detection tools.
Paper proposes SCQ and P-TAMS for structured OOD testing in high-stakes ML.
FIRE PBT improves neural network training by focusing on long-term performance.
GPT-f uses language models to find new proofs in formal math.
Automates detecting problem statements in peer assessments.
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments out…
Deep-learning CNN automates Cu alloy grain size evaluation.