Testing RESTful API is increasingly important in quality assurance of cloud-native applications. Recent advances in machine learning (ML) techniques have demonstrated that various testing activities can be performed automatically by large language mo...
While large machine learning models have shown remarkable performance in various domains, their training typically requires iterating for many passes over the training data. However, due to computational and memory constraints and potential privacy c...
This paper considers single-machine scheduling problems in which a given solution, i.e. an ordered set of jobs, has to be improved as much as possible by re-sequencing the jobs. The need for rescheduling may arise in different contexts, e.g. due to c...
In this paper, we explore the utility of translationese as synthetic data created using machine translation for pre-training language models (LMs) for low-resource languages (LRLs). Our simple methodology consists of translating large amounts of web-...
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models. (⭐ 592)
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator to monitor. However, explanation aspects have come to attention withi...
Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit the full potential of such methods, a representative number of experimen...
windmill is a machine operated by the force of wind acting on vanes or sails to mill grain (gristmills), pump water, generate electricity, or drive other machinery
https://x.com/asummerof44/status/1937186041049280887?s=46 ...
Machine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been proven to be a simple yet effective cornerstone in existing test-time ada...
Algorithmic Trading Challenge implemented as part of the term project for Foundations of Machine Learning at NYU Courant in Fall 2016 (http://cs.nyu.edu/courses/fall16/CSCI-GA.2566-001/index.html/) (⭐ 27)
Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing distributed machine learning techniques. However, its distributive and iterative nature makes FL inher...
Feature selection is an important and active research area in statistics and machine learning. The Elastic Net is often used to perform selection when the features present non-negligible collinearity or practitioners wish to incorporate additional kn...
Differential privacy (DP) has become a prevalent privacy model in a wide range of machine learning tasks, especially after the debut of DP-SGD. However, DP-SGD, which directly perturbs gradients in the training iterations, fails to mitigate the negat...
Machine learning (ML) applications that learn from data are increasingly used to automate impactful decisions. Unfortunately, these applications often fall short of adequately managing critical data and complying with upcoming regulations. A technica...
Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angular expansions, limiting flexibility in weighting local geometric inter...
Cory grew up at the foot of a sewing machine. Everyday she would watch her mother break down design ideas into pattern pieces that fit together into 3d creations. Cory embraces this method of creation to …
Mechanical advantage is a measure of the force amplification achieved by using a tool, mechanical device or machine system. The device trades off input
A high-performance topological machine learning toolbox in Python (⭐ 974)
Points: 176 | Comments: 27 | Author: johmathe