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Griewank function
The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function. -
Human Activity Recognition (HAR) dataset
The dataset used in this paper is a multiclass classification task where the goal is to correctly predict which of the 7 activities is being performed by the user. The... -
Part VI: combining compressions
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. -
Low-rank compression of neural nets: Learning the rank of each layer
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. -
Part V: combining compressions
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. -
Model compression as constrained optimization
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. -
Soft-failure evolution dataset
The dataset is used for modeling soft-failure evolution for triggering timely repair with low QoT margins. -
Machine Learning and Factor-Based Portfolio Optimization
The dataset consists of monthly total individual stock returns from the Center for Research in Security Prices (CRSP) starting on January 1960 to December 2019, for a period of... -
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Cu...
The dataset is a stream of attributed graphs, where each graph is a 3-tuple of matrices (A, X, E) representing the topology and attributes of the graph. -
Boosting the performance of anomalous diffusion classifiers with the proper c...
Understanding and identifying different types of single molecules' diffusion that occur in a broad range of systems (including living matter) is extremely important, as it can... -
Identification of Anomalous Diffusion Sources by Unsupervised Learning
Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which the memory effects of the stochastic transport result in the mean squared particle displacement... -
Functional Priors and Posteriors from Data and Physics
The dataset used in this paper is a collection of historical data for learning functional priors and posteriors from data and physics. -
Noisy wheel dataset
The dataset used in the paper is a noisy wheel dataset, which is a 2-D Euclidean space with inner circles that regulate the class proportions. -
UCI datasets
The dataset used in the paper is a set of UCI datasets, including adult, census, covertype, financial, joke, mushroom, and statlog. -
WIT: Wikipedia-based image text dataset for multimodal multilingual machine l...
A multimodal dataset for machine learning tasks, focusing on Wikipedia-based image text datasets. -
Simulated AT-TPC dataset
The dataset used in this paper is a collection of simulated 2D projections of particle tracks from a resonant proton scattering experiment on 46Ar. -
AT-TPC dataset
The dataset used in this paper is a collection of 2D projections of particle tracks from a resonant proton scattering experiment on 46Ar. -
UEFA EURO 2020 and 2022 location data
The dataset used in this study is the open-source location data of all players in broadcast video frames in football games of men’s Euro 2020 and women’s Euro 2022 competitions. -
ε-greedy Thompson Sampling
Two benchmark functions (2d Ackley and 6d Rosenbrock) and a steel cantilever beam dataset -
High-Dimensional Linear Composites
The dataset used in this paper is a collection of high-dimensional linear composites with outer function f and general data-covariance.
