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CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. -
Rapid detection of rare events from in situ X-ray diffraction data using mach...
High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk... -
Dataset for ℓp subspace approximation
The dataset used in this paper is a set of points in d-dimensional space, with n points in total. -
Normal Linear Model
The dataset used in the paper is a normal linear model: Y = Xβ + (cid:15), (cid:15) ∼ N (0, σ2I), where Y is an N dimensional response vector, X is an N ×D dimensional design... -
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine L...
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines -
Millionsong
The dataset used in the paper is a ridge regression problem (Millionsong) and a support vector machine (RCV1) and a matrix completion problem (Netflix) on a large-scale... -
Therapeutics Data Commons
Therapeutics Data Commons (TDC) is a machine learning dataset and task for drug discovery and development. -
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Opt...
Recent studies on diffusion-based sampling methods have shown that Langevin Monte Carlo (LMC) algorithms can be beneficial for non-convex optimization, and rigorous theoretical... -
Rastrigin function and Griewank function
The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function. -
Bayesian Optimization for Industrial Process Optimization
The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used a Bayesian Optimization algorithm and a dataset for industrial process... -
Covfefe: A Computer Vision Approach For Estimating Force Exertion
The dataset used in this paper for predicting force exertion levels using computer vision and machine learning. -
Two-Sample Testing Using Projected Wasserstein Distance
Two-sample test using projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine... -
Learning Generalized Reactive Policies using Deep Neural Networks
A new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. -
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the... -
Linear Frequency-Principle (LFP) model for two-layer neural networks
The dataset used in this paper is a collection of training data and target functions for two-layer neural networks. The dataset is used to test the performance of the Linear... -
Multi-Objective Hyperparameter Optimization in Machine Learning – An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding... -
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Co...
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated... -
DeepComfort Dataset
The dataset is used for thermal comfort prediction in naturally ventilated classrooms. It contains data from field experiments and surveys involving 512 unique primary school... -
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Sto...
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems
