The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we be...
Empirical studies of scientific discovery---so-called Eurekometrics---have indicated that the output of exploration proceeds as a logistic growth curve. Although logistic functions are prevalent in explaining population growth that is resource-limite...
The codes, datasets, and results of our paper titled "A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity" (⭐ 0)
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration
End-to-end guide design for CRISPR/Cas9 with machine learning (⭐ 138)
Efficiently solving sparse linear systems $Ax=b$, where $A$ is a large, sparse, symmetric positive semi-definite matrix, is a core challenge in scientific computing, machine learning, and optimization. A major bottleneck in Gaussian elimination for t...
Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree. (⭐ 1150)
A vehicle (from Latin vehiculum) is a machine designed for self-propulsion, usually to transport people, cargo, or both. The term "vehicle" typically refers
Whether for work or play, a snowmobile (aka a “snow machine”) is the right vehicle for getting around in snowy, icy conditions. Browse a broad selection of new and used trail, performance, touring, …
Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are well-behaved---that...
Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from the clients. Unfortunately, this prevents verification of the well-forme...
The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applications in various scenarios such as film poster translation and everyday...
How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitatio...
Arize AI is a machine learning observability platform built to unpack the proverbial AI black box. Meet our team, founders, and learn more about us here.
We study universal aspects of fluctuations in an ensemble of noninteracting continuous quantum thermal machines in the steady state limit. Considering an individual machine, such as a refrigerator, in which relative fluctuations (and high order cumul...
Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an...
Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine tra...
The convergence of humans and artificial intelligence systems introduces new dynamics into the cultural and intellectual landscape. Complementing emerging cultural evolution concepts such as machine culture, AI agents represent a significant techno-s...
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas (⭐ 8967)
We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learnin...