Instantaneously trained neural networks

Key takeaways

Instantaneously trained neural networks belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Instantaneously trained neural networks to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Instantaneously trained neural networks from memory before moving on to harder problems.

Reference excerpt

Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization. This separation is done using the nearest hyperplane that can be written down instantaneously. In the two most important implementations the neighborhood of generalization either varies with the training sample (CC1 network) or remains constant (CC4 network). These networks use unary coding for an effective representation of the data sets. This type of network was first proposed in a 1993 paper of Subhash Kak. Since then, instantaneously trained neural networks have been proposed as models of short term learning and used in web search, and financial time series prediction applications. They have also been used in instant classification of documents and for deep learning and data mining. As in other neural networks, their normal use is as software, but they have also been implemented in hardware using FPGAs and by optical implementation.

CC4 network In the CC4 network, which is a three-stage network, the number of input nodes is one more than the size of the training vector, with the extra node serving as the biasing node whose input is always 1. For binary input vectors, the weights from the input nodes to the hidden neuron (say of index j) corresponding to the trained vector is given by the following formula:

w i j = { − 1 , for x i = 0 + 1 , for x i = 1 r − s + 1 , for i = n + 1 {\displaystyle w_{ij}={\begin{cases}-1,&{\mbox{for }}x_{i}=0\\+1,&{\mbox{for }}x_{i}=1\\r-s+1,&{\mbox{for }}i=n+1\end{cases}}}

where r {\displaystyle r} is the radius of generalization and s {\displaystyle s} is the Hamming weight (the number of 1s) of the binary sequence. From the hidden layer to the output layer the weights are 1 or -1 depending on whether the vector belongs to a given output class or not. The neurons in the hidden and output layers output 1 if the weighted sum to the input is 0 or positive and 0, if the weighted sum to the input is negative:

y = { 1 if ∑ x i ≥ 0 0 if ∑ x i < 0 {\displaystyle y=\left\{{\begin{matrix}1&{\mbox{if }}\sum x_{i}\geq 0\\0&{\mbox{if }}\sum x_{i}<0\end{matrix}}\right.}

Other networks The CC4 network has also been modified to include non-binary input with varying radii of generalization so that it effectively provides a CC1 implementation. In feedback networks the Willshaw network as well as the Hopfield network are able to learn instantaneously.

References

Worked examples

Example 1 — a first encounter with Instantaneously trained neural networks

Start with the simplest possible case. Write down what Instantaneously trained neural networks claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Instantaneously trained neural networks before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Instantaneously trained neural networks ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Instantaneously trained neural networks

In research: Instantaneously trained neural networks appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Instantaneously trained neural networks in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Instantaneously trained neural networks is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial neural networks, Learning, so understanding it makes those chapters shorter.
In everyday life: Look for Instantaneously trained neural networks outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Instantaneously trained neural networks in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Instantaneously trained neural networks means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Instantaneously trained neural networks out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Instantaneously trained neural networks in simple terms?

Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization.

Why does Instantaneously trained neural networks matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Instantaneously trained neural networks?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Instantaneously trained neural networks.