24 papers · ranked by Valyu relevance
Yiqun Jiang, Wenli Zhang, Yu-Li Huang, Cameron MacKenzie + 2 more
The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography tests, challenges persist in efficiently prioritizing patients for…
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with highdimensional covariates, under the assumption of a partially linear model. The idea is to first fit on a subset of the samples two…
Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality…
Michael C. Knaus
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different…
Achim Ahrens, Christian Hansen, Mark E. Schaffer, Thomas Wiemann
This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: short-stacking exploits the cross-fitting…
Christine H. Lind, Angela J. Yu
Several recent papers have studied the double descent phenomenon: a classic U-shaped empirical risk curve when the number of parameters is smaller or equal to the number of data points, followed by a decrease in empirical risk (referred to as “second descent”) as the number of features is increased past the…
Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster, Carlos Bravo-Prieto + 3 more
'Jakob Spiegelberg' 'Evert van Nieuwenburg' 'Vedran Dunjko'] The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behavior has been observed in a variety of…
G. Pilania, A. Mannodi-Kanakkithodi, B. P. Uberuaga, R. Ramprasad + 2 more
'J. E. Gubernatis' 'T. Lookman'] The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high…
Christoph Küng, Olena Protsenko, Rosario Vanella, Michael A. Nash
Understanding the linkage between protein sequence and phenotypic expression level is crucial in biotechnology. Machine learning algorithms trained with deep mutational scanning (DMS) data have significant potential to improve this understanding and accelerate protein engineering campaigns. However, most machine…
Fuqiang Liu, Chenwei Deng, Fukun Bi, Yiding Yang
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic…
Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang + 1 more
'M. Natália D.S. Cordeiro'] The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising…
Seungjun Yu, Haneol Lee, Changyoung Ju, Haewook Han + 1 more
Modern optical systems are important components of contemporary electronics and communication technologies, and the design of new systems has led to many innovative breakthroughs. This paper introduces a novel application based on deep reinforcement learning, D3QN, which is a combination of the Dueling Architecture and…
Xuran Meng, Jianfeng Yao, Yuan Cao
Recent works have demonstrated a double descent phenomenon in over-parameterized learning. Although this phenomenon has been investigated by recent works, it has not been fully understood in theory. In this paper, we investigate the multiple descent phenomenon in a class of multi-component prediction models. We first…
Amit Kumar Gope, Yu-Shu Liao, Chung-Feng Jeffrey Kuo, Zina Vuluga + 1 more
'Mihai Cosmin Corobea'] Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality.…
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
Authors not listed
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Tengxiao Wang, Karen Chang, Matteo Tomasi, Chen-Yuan Lee + 3 more
The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay relies on a single behavioral metric, dwell time in the light or dark compartment, which may be influenced by factors other than vision, leading to unreliable assessment…
Alex Lee, Joshua Rackers, William Bricker
One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean Neural Network framework to obtain quantum-accurate electron densities for…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Alex Lee, Joshua Rackers, Shivesh Pathak, William Bricker
Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…
Michal Bozděch
Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to…
Michael Schneider, Lichao Wang, Carsten Marr
Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful…