581 datasets found

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...

The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function.

Google’s state-of-the-art Edge TPU provides an optimized one-size-fits-all design for edge devices.

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...

The dataset used in this paper for predicting force exertion levels using computer vision and machine learning.

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...

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.

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...

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...

Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding...

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...

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 Stochastic Systems

A large, standardized dataset of encrypted and compressed fragments covering various popular file formats and fragment sizes.

The dataset used in this paper for building extraction from LiDAR data and aerial images.

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...

The dataset used in this paper is a large ensemble run of simulations for training deep surrogates.

A large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations.

Four datasets: synthetic, Fashion-MNIST, UK Biobank brain imaging, and Cancer Genome Atlas.

The dataset used in the paper is a memory contention side-channel attack and an application power side-channel attack.