Sen. Ted Cruz Stripped Weather Forecasting Funds From Trump’s Megabill. Then the Floods Ca…
Among other things, he removed $150 million slated to improve modeling, predictions, and public communications....
The age of secrecy and unfairness in recidivism prediction
In our current society, secret algorithms make important decisions about individuals. There has been substantial discussion about whether these algorithms are unfair to groups of individuals. While noble, this pursuit is complex and ultimately stagna...
Investigating Human + Machine Complementarity for Recidivism Predictions
When might human input help (or not) when assessing risk in fairness domains? Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPublica COMPAS data for risk of recidivism, and concluded that COMPAS pr...
Algorithmic Bias in Recidivism Prediction: A Causal Perspective
ProPublica's analysis of recidivism predictions produced by Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software tool for the task, has shown that the predictions were racially biased against African American defenda...
March Madness Tournament Predictions Model: A Mathematical Modeling Approach
This paper proposes a model to predict the outcome of the March Madness tournament based on historical NCAA basketball data since 2013. The framework of this project is a simplification of the FiveThrityEight NCAA March Madness prediction model, wher...
Facet and energy predictions in grain boundaries: lattice matching and molecular dynamics
Many material properties can be traced back to properties of their grain boundaries. Grain boundary energy (GBE), as a result, is a key quantity of interest in the analysis and modeling of microstructure. A standard method for calculating grain bound...
Deep Ordinal Regression for Pledge Specificity Prediction
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most po...
nelsonken/rodeo2
This isn't our first RODEO. The new and improved RODEO is written in Python and supports lasso peptide, class I lanthipeptide, sactipeptide and thiopeptide precursor prediction. (⭐ 3)
Comparative study of variational quantum circuit and quantum backpropagation multilayer pe…
There are numerous models of quantum neural networks that have been applied to variegated problems such as image classification, pattern recognition etc.Quantum inspired algorithms have been relevant for quite awhile. More recently, in the NISQ era,...
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos
Augmented reality (AR) overlays digital content onto the reality. In AR system, correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources on the areas...
Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow
Language models are known to absorb biases from their training data, leading to predictions driven by statistical regularities rather than semantic relevance. We investigate the impact of these biases on answer choice preferences in the Massive Multi...
DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Cli…
The ability to predict the future trajectory of a patient is a key step toward the development of therapeutics for complex diseases such as Alzheimer's disease (AD). However, most machine learning approaches developed for prediction of disease progre...
ankurtaly/Integrated-Gradients
Attributing predictions made by the Inception network using the Integrated Gradients method (⭐ 645)
Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, a…
How do language models learn to make predictions during pre-training? To study this, we extract learning curves from five autoregressive English language model pre-training runs, for 1M unseen tokens in context. We observe that the language models ge...
Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Wei…
Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data-2D RGB images from di...
Prediction of amino acid content in live black soldier fly larvae using near infrared spec…
Black soldier fly (Hermetia illucens) larvae are emerging as a sustainable protein source for animal feed and human nutrition. Ensuring consistent amino acid composition is crucial for quality control, necessitating rapid, non-destructive assessment...
Enhancing Deterministic Freezing Level Predictions in the Northern Sierra Nevada Through D…
Accurate prediction of the freezing level is essential for hydrometeorological forecasting systems, with direct implications for runoff generation and reservoir management. In this study, we develop a deep learning based postprocessing framework usin...
Online Prediction-Assisted Safe Reinforcement Learning for Electric Vehicle Charging Stati…
With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highl...
xinychen/transdim
Machine learning for transportation data imputation and prediction. (⭐ 1286)
Prediction Through Quantum Dynamics Simulations: Photo-excited Cyclobutanone
Quantum dynamics simulations are becoming a standard tool for simulating photo-excited molecular systems involving a manifold of coupled states, known as non-adiabatic dynamics. While these simulations have had many successes in explaining experiment...