What This Covers: Authors: Shuichi Hirahara (National Institute of Informatics); Mikito Nanashima (Tokyo Institute of Technology) ITCS - Innovations ... Explicit SoS lower bounds from high-dimensional expanders Irit Dinur (Weizmann Institute of Science), Yuval Filmus (Technion), ...

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An important theme in theoretical computer science over the last decade has been the usefulness of translating a combinatorial ... Authors: Shuichi Hirahara (National Institute of Informatics); Mikito Nanashima (Tokyo Institute of Technology) ITCS - Innovations ... Explicit SoS lower bounds from high-dimensional expanders Irit Dinur (Weizmann Institute of Science), Yuval Filmus (Technion), ...

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Explicit SoS lower bounds from high-dimensional expanders Irit Dinur (Weizmann Institute of Science), Yuval Filmus (Technion), ... Abstract: A degree-d threshold function is a boolean function of the form f(x) = sign(p(x)), where p(x) is a degree-d polynomial over ...

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  • Russell Impagliazzo (UC San Diego) Simons Institute 10th Anniversary Symposium.
  • Abstract: A degree-d threshold function is a boolean function of the form f(x) = sign(p(x)), where p(x) is a degree-d polynomial over ...
  • An important theme in theoretical computer science over the last decade has been the usefulness of translating a combinatorial ...
  • Explicit SoS lower bounds from high-dimensional expanders Irit Dinur (Weizmann Institute of Science), Yuval Filmus (Technion), ...
  • Authors: Shuichi Hirahara (National Institute of Informatics); Mikito Nanashima (Tokyo Institute of Technology) ITCS - Innovations ...

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Connections Between Pseudorandomness and Machine Learning
Learning versus Pseudorandom Generators in Constant Parallel Time
Pseudorandomness
Pseudorandomness in Data Structures
Learning models: connections between boosting...and regularity I - Russell Impagliazzo
What Are Skip Connections ResNets and Why Do They Work
Threshold Functions: Approximation, Pseudorandomness and Learning
The Power of Distinguishing Simple From Random (Part I)
Learning Versus Pseudorandom Generators in Constant Parallel Time
Limit Theorems in Pseudorandomness and Learning Theory
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Connections Between Pseudorandomness and Machine Learning

Connections Between Pseudorandomness and Machine Learning

Russell Impagliazzo (UC San Diego) Simons Institute 10th Anniversary Symposium.

Learning versus Pseudorandom Generators in Constant Parallel Time

Learning versus Pseudorandom Generators in Constant Parallel Time

Authors: Shuichi Hirahara (National Institute of Informatics); Mikito Nanashima (Tokyo Institute of Technology) ITCS - Innovations ...

Pseudorandomness

Pseudorandomness

Explicit SoS lower bounds from high-dimensional expanders Irit Dinur (Weizmann Institute of Science), Yuval Filmus (Technion), ...

Pseudorandomness in Data Structures

Pseudorandomness in Data Structures

Read more details and related context about Pseudorandomness in Data Structures.

Learning models: connections between boosting...and regularity I - Russell Impagliazzo

Learning models: connections between boosting...and regularity I - Russell Impagliazzo

Read more details and related context about Learning models: connections between boosting...and regularity I - Russell Impagliazzo.

What Are Skip Connections ResNets and Why Do They Work

What Are Skip Connections ResNets and Why Do They Work

Read more details and related context about What Are Skip Connections ResNets and Why Do They Work.

Threshold Functions: Approximation, Pseudorandomness and Learning

Threshold Functions: Approximation, Pseudorandomness and Learning

Abstract: A degree-d threshold function is a boolean function of the form f(x) = sign(p(x)), where p(x) is a degree-d polynomial over ...

The Power of Distinguishing Simple From Random (Part I)

The Power of Distinguishing Simple From Random (Part I)

Read more details and related context about The Power of Distinguishing Simple From Random (Part I).

Learning Versus Pseudorandom Generators in Constant Parallel Time

Learning Versus Pseudorandom Generators in Constant Parallel Time

Read more details and related context about Learning Versus Pseudorandom Generators in Constant Parallel Time.

Limit Theorems in Pseudorandomness and Learning Theory

Limit Theorems in Pseudorandomness and Learning Theory

An important theme in theoretical computer science over the last decade has been the usefulness of translating a combinatorial ...