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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 -
EnCoD: Distinguishing Compressed and Encrypted File Fragments
A large, standardized dataset of encrypted and compressed fragments covering various popular file formats and fragment sizes. -
MapAI: Precision in building segmentation
The dataset used in this paper for building extraction from LiDAR data and aerial images. -
Pendulum Control Dataset
The dataset used in the paper is a collection of data points from a pendulum system, where the pendulum is controlled using a policy computed via Algorithm 1. The dataset is... -
High Throughput Training of Deep Surrogates from Large Ensemble Runs
The dataset used in this paper is a large ensemble run of simulations for training deep surrogates. -
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of B...
A large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. -
Clusters in Explanation Space: Inferring disease subtypes from model explanat...
Four datasets: synthetic, Fashion-MNIST, UK Biobank brain imaging, and Cancer Genome Atlas. -
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation
The dataset used in the paper is a memory contention side-channel attack and an application power side-channel attack.
