Convolutional
neural network
|
"In deep
learning, a convolutional neural network (CNN,
or ConvNet)
is a class of deep neural networks, most commonly applied to analyzing
visual imagery.[1] They are also known as shift invariant or space
invariant artificial neural networks (SIANN), based on their
shared-weights architecture and translation invariance
characteristics.[2][3] They have applications in image and video
recognition, recommender systems,[4] image classification, medical
image analysis, natural language processing,[5] and financial time
series.[6]" - convolutional
neural network (CNN, or ConvNet) |
|
Deep learning
|
"Deep learning
(also known as deep structured learning) is part of a broader family of
machine learning methods based on artificial neural networks with
representation learning. Learning can be supervised, semi-supervised or
unsupervised.[1][2][3]"- https://en.wikipedia.org/wiki/Deep_learning
|
|
Recurrent
neural
network
|
"A recurrent
neural network (RNN)
is a class of artificial neural networks where connections between
nodes form a directed graph along a temporal sequence. This allows it
to exhibit temporal dynamic behavior. Derived from feedforward neural
networks, RNNs can use their internal state (memory) to process
variable length sequences of inputs.[1] This makes them applicable to
tasks such as unsegmented, connected handwriting recognition[2] or
speech recognition.[3][4] "- Recurrent
neural network. |
|
Machine
learning
|
"Machine learning (ML) is the
study of computer algorithms that can improve automatically through
experience and by the use of data.[1] It is seen as a part of
artificial intelligence. Machine learning
algorithms build a model based on sample data, known as "training
data", in order to make predictions or decisions without being
explicitly programmed to do so.[2] Machine learning algorithms are used
in a wide variety of applications, such as in medicine, email
filtering, speech recognition, and computer vision, where it is
difficult or unfeasible to develop conventional algorithms to perform
the needed tasks.[3]" -Machine learning.
|
|
Pareto efficiency
|
"Pareto
efficiency or Pareto optimality is a situation where no individual or
preference criterion can be better off without making at least one
individual or preference criterion worse off or without any loss
thereof. The concept is named after Vilfredo Pareto (1848–1923),
Italian civil engineer and economist, who used the concept in his
studies of economic efficiency and income distribution. The following
three concepts are closely related:
●Given an initial situation, a Pareto improvement is a new situation
where some agents will gain, and no agents will lose.
●●A situation is called Pareto dominated if there exists a possible
Pareto improvement.
●●●A situation is called Pareto optimal or Pareto efficient if no
change could lead to improved satisfaction for some agent without some
other agent losing or if there is no scope for further Pareto
improvement.
The Pareto frontier is the set of all Pareto efficient allocations,
conventionally shown graphically. It also is variously known as the
Pareto front or Pareto set.[1]"-Pareto efficiency.
|
|
Federated learning
|
"Federated learning (also known as collaborative learning) is a
machine learning technique that trains an algorithm across multiple
decentralized edge devices or servers holding local data samples,
without exchanging them. This approach stands in contrast to
traditional centralized machine learning techniques where all the local
datasets are uploaded to one server, as well as to more classical
decentralized approaches which often assume that local data samples are
identically distributed. Federated learning enables multiple actors to
build a common, robust machine learning model without sharing data,
thus allowing to address critical issues such as data privacy, data
security, data access rights and access to heterogeneous data. Its
applications are spread over a number of industries including defense,
telecommunications, IoT, and pharmaceutics."
|
|
| Federated Learning of Cohorts |
"Federated Learning of Cohorts (FLoC) is a type of web tracking through federated learning. It groups people into "cohorts" based on their browsing history for the purpose of interest-based advertising.[1][2]
Google began testing the technology in the Chrome browser in March 2021
as a replacement for third-party cookies, which it plans to stop
supporting in Chrome by early 2023. FLoC is being developed as a part
of Google's Privacy Sandbox initiative,[3] which includes several other
advertising-related technologies with bird-themed names.[1][4]:48"- Federated Learning of Cohorts.
|
|
Society of Mind
|
"The Society of Mind is both the title of a 1986 book and the name of a theory of natural intelligence as written and developed by Marvin Minsky.[1] In his book of the same name, Minsky constructs a
model of human intelligence step by step, built up from the
interactions of simple parts called agents, which are themselves
mindless. He describes the postulated interactions as constituting a "society of mind", hence the title.[2]" -Society of Mind.
|
|
