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Found 11 papers, 1 papers with code
Signal processing after quadratic random sketching with optical units
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
Signal processing with optical quadratic random sketches
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
M$^2$M: A general method to perform various data analysis tasks from a differentially private sketch
In this paper, we introduce the generic moment-to-moment (M$^2$M) method to perform a wide range of data exploration tasks from a single private sketch.
ROP inception: signal estimation with quadratic random sketching
In a nutshell, the SPE shows that the scalar product of a signal sketch with the "sign" of the sketch of a given pattern approximates the square of the projection of that signal on this pattern.
Asymmetric compressive learning guarantees with applications to quantized sketches
The compressive learning framework reduces the computational cost of training on large-scale datasets.
When compressive learning fails: blame the decoder or the sketch?
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset.
Sketching Datasets for Large-Scale Learning (long version)
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e. g., clustering, classification, or regression) is performed.
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
Concretely, we introduce the general framework of asymmetric random periodic features, where the two signals of interest are observed through random periodic features: random projections followed by a general periodic map, which is allowed to be different for both signals.
Compressive Learning of Generative Networks
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy.
Compressive Classification (Machine Learning without learning)
Compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it.
Quantized Compressive K-Means
The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive datasets.
