High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the “curse of dimensionality” states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the “blessing of dimensionality”, has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effe...
To date, the world continues to generate quintillion bytes of data daily, leading to the pressing ne...
In the wake of the revolution brought by Deep Learning, we believe neural networks can be leveraged ...
Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional...
Despite the widely-spread consensus on the brain complexity, sprouts of the single neuron revolution...
In their review article (this issue) [1], Gorban, Makarov and Tyukin develop a successful effort to ...
Complexity is an indisputable, well-known, and broadly accepted feature of the brain. Despite the ap...
Machine learning methods are used to build models for classification and regression tasks, among oth...
Machine learning is used nowadays to build models for classification and regression tasks, among oth...
The concentrations of measure phenomena were discovered as the mathematical background to statistica...
Abstract—It is believed that if machine can learn human-level invariant semantic concepts from highl...
Observations from real-world problems are often high-dimensional vectors, i.e. made up of many varia...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
Dimensionality reduction aims at providing faithful low-dimensional representations of high-dimensio...
Machine learning methods are used to build models for classification and regression tasks, among oth...
If spikes are the medium, what is the message? Answering that question is driving the development of...
To date, the world continues to generate quintillion bytes of data daily, leading to the pressing ne...
In the wake of the revolution brought by Deep Learning, we believe neural networks can be leveraged ...
Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional...
Despite the widely-spread consensus on the brain complexity, sprouts of the single neuron revolution...
In their review article (this issue) [1], Gorban, Makarov and Tyukin develop a successful effort to ...
Complexity is an indisputable, well-known, and broadly accepted feature of the brain. Despite the ap...
Machine learning methods are used to build models for classification and regression tasks, among oth...
Machine learning is used nowadays to build models for classification and regression tasks, among oth...
The concentrations of measure phenomena were discovered as the mathematical background to statistica...
Abstract—It is believed that if machine can learn human-level invariant semantic concepts from highl...
Observations from real-world problems are often high-dimensional vectors, i.e. made up of many varia...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
Dimensionality reduction aims at providing faithful low-dimensional representations of high-dimensio...
Machine learning methods are used to build models for classification and regression tasks, among oth...
If spikes are the medium, what is the message? Answering that question is driving the development of...
To date, the world continues to generate quintillion bytes of data daily, leading to the pressing ne...
In the wake of the revolution brought by Deep Learning, we believe neural networks can be leveraged ...
Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional...