14 papers · ranked by Valyu relevance
Muhammad Bilal Shahid, Prajwal Koirla, Cody Fleming
Learned time-series models, whether continuous- or discrete-time, are widely used to forecast the states of a dynamical system. Such models generate multi-step forecasts either directly, by predicting the full horizon at once, or iteratively, by feeding back their own predictions at each step. In both cases, the…
Panagiotis Chrysinas, Changyou Chen, Rudiyanto Gunawan
Predicting the cell response to chemical compounds is central to drug discovery, drug repurposing, and personalized medicine. To this end, large datasets of drug response signatures have been curated, most notably the Connectivity Map (CMap) from the Library of Integrated Network-based Cellular Signatures (LINCS)…
Yuta Hirabayashi, Daisuke Matsuoka
- A mesoscale weather prediction model that combines Swin-Unet is proposed as a deterministic Predictor and a diffusion model as a probabilistic Corrector. - Both the Predictor and Corrector were trained independently, enabling flexible updates to the deterministic Predictor without retraining the Corrector. - The…
Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Often, the underlying process that drives the distribution shift is continuous in nature. We exploit this underlying continuity by developing…
Ximei Yang, Yinkui Zhang
In this paper, we propose a Mizuno-Todd-Ye predictor-corrector infeasible-interior-point method for symmetric optimization using the arc-search strategy. The proposed algorithm searches for optimizers along the ellipses that approximate the central path and ensures that the duality gap and the infeasibility have the…
Alexander N. Gorban, Bogdan Grechuk, Evgeny M. Mirkes, Sergey V. Stasenko + 2 more
This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors. These corrections should be quick and non-iterative. To solve this problem without modification of a legacy AI system, we propose special ‘external’ devices, correctors. Elementary correctors consist of two parts, a…
Linde Schoenmaker, Olivier Béquignon, Willem Jespers, Gerard van Westen
Generative deep learning models have emerged as a powerful approach for de novo drug design, as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors.…
Lu Hong, PJ Lamberson, Scott E Page
An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis…
Jasmijn F. Timp, Sigrid K. Braekkan, Willem M. Lijfering, Astrid van Hylckama Vlieg + 4 more
In [pmed.1003612.t001], the coefficients of the prediction model are incorrect for rows 3-5 of the clinical factors/environmental predictor variables, and for some of the coefficients of model B). The correct table is below. Table 2
Francesco Giannelli, Nicola Molinaro
We investigated how native language experience shapes prediction mechanisms. Two groups of bilinguals (either Spanish or Basque natives) performed a word matching task (WMT) and a picture matching task (PMT). They indicated whether the stimuli they perceived matched with the noun they heard. Spanish noun endings were…
Spencer Greenberg
In situations where forecasters are scored on the quality of their probabilistic predictions, it is standard to use 'proper' scoring rules to perform such scoring. These rules are desirable because they give forecasters no incentive to lie about their probabilistic beliefs. However, in the real world context of…
Rasmus M. Borup, Nicolai Ree, Jan Jensen
Determining the pKa values of various C-H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemical (QM)-based workflow for automatic computations…
Zhaobo K. Zheng, John E. Staubitz, Amy S. Weitlauf, Johanna Staubitz + 8 more
Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer…
Mustafa Attallah
Pearson's correlation to select predictor variables for linear models Authors: ['Mustafa Attallah'] This article examines the limitations of Pearson's correlation in selecting predictor variables for linear models. Using mtcars and iris datasets from R, this paper demonstrates the limitation of this correlation measure…