Machine learning predicts US will win most Olympic medals in 2020.
problem Improve Olympic medal forecasting accuracy.
method Two-stage Random Forest machine learning model.
result Model outperforms traditional forecasts for three previous Olympics.
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14 results for “Olympic medals”
Olympic Games consistently exceed budgets, leading to unpredictable costs.
problem High costs and unpredictability of the Olympic Games.
method Statistical analysis of historical data to explain cost risks.
result Olympic costs follow a power-law distribution with infinite mean and variance.
MEDAL converts manifold embeddings into models for rigorous validation.
problem Challenges in validating manifold embeddings without held-out validation.
method Develops MEDAL framework that distills embeddings into autoencoder models.
result Enables rigorous validation of manifold embeddings and hyperparameters.
Paper tackles AI driving competition challenges with mixed simulation and real-world data.
problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.
method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.
result Trained algorithms performed well in both simulated and real-world environments.
Olympic Games remain costly and overrun, despite reforms.
problem High costs and cost overruns in Olympic Games.
method Analysis of past and current Olympic Games costs and overruns, assessment of IOC reforms, and comparison of reuse vs. new construction.
result Cost overruns are increasing, and reuse of venues is ineffective.
Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maxi…
This paper describes the work of Jesse Douglas on the Plateau problem, work for which he was awarded a Fields Medal in 1936, and considers the contributions Tibor Rado made in the 1930s.
Barcelona in the face of globalization, how to think of the city through the organization and evaluation of major events?physics.soc-ph
Barcelona evaluates major events for economic and social impact.
problem Evaluating the economic and social impact of major events in Barcelona.
method Analyzes the economic and social dimensions of Barcelona's major events from 1888 to 2004.
result Develops a rational argument for communicating the economic benefits of major events.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
We discuss a possible solution to an unintended consequence of having grades, certificates, rankings and other diversions in the act of transferring knowledge; and zoom in specifically to the topic of having grades, on a curve. We conduct a thought experiment, taking a chapter (and some more?) from the financial market…
Two methods use simulation to improve anomaly detection in particle physics.
problem Artificial bumps in invariant mass spectra from machine learning classifiers.
method Simulation-assisted decorrelation techniques.
result Both methods are robust to correlations in the data and improve anomaly detection.
The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.
problem Point and probabilistic forecasting of hierarchical time series.
method A blending methodology of gradient boosted trees and neural networks, with feature engineering and diverse model selection.
result Ranked within the gold medal range in both Accuracy and Uncertainty tracks of the M5 Competition.
ANODE uses neural density estimation for anomaly detection in physics.
problem Detecting localized anomalies in signal regions with limited background information.
method Estimate data and background densities, construct likelihood ratio, and enhance significance.
result ANODE enhances dijet bump hunt significance by up to 7x with 10% background accuracy.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
problem Machine-learned anomaly detection in new-physics searches
method Conformal prediction layer
result Calibrated significance with distribution-free guarantees