deep representations

USAGE SUMMARY

The phrase 'deep representations' is correct and can be used in written English.
It is typically used to describe a process where various components of a given set of data are extracted to create an abstract or condensed version of the original data. For example, one may use deep representations to analyze the sentiment of a text by extracting components like word polarity and sentence structure.

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

17 human-written examples

All these issues are resolved in learning deep representations.

Procedia Computer Science

By utilizing generic and attribute-specific deep representations for maritime vessels, we obtained promising results for the aforementioned applications.

IPSJ Transactions on Computer Vision and Applications

Moreover, we provide generic deep representations for maritime vessels and prove their success in aforementioned tasks by performing extensive experiments.

IPSJ Transactions on Computer Vision and Applications

This choice makes our vessel verification performance better than the case with the deep representations after ReLU case.

IPSJ Transactions on Computer Vision and Applications

introduce four operations which can be inserted into convolutional neural network to learn deep representations equivariant to rotation.

Neurocomputing

On the other hand, we also compare these learned deep representations (employing the content information) with another effective representation, designed for object classification.

IPSJ Transactions on Computer Vision and Applications

Human-verified similar examples from authoritative sources

Similar Expressions

43 human-written examples

Deepgram then processes the speech, which is stored in what's called a "deep representation index".

TechCrunch

Indeed, the rise of executive women has led to a deep representation on our list, with 47 of the 100 Power Women coming from the corporate world.

Forbes

1. Kosslyn does not claim that the specific, LISP-like format of deep representation shown in figure 4.4.2_1 is psychologically real.

SEP

However, failing to seek deep representation of raw data completely brought by shallow architecture has made a plenty of research work stagnant, when ELM was chosen as the basic model.

Knowledge-Based Systems

The array representation is constructed from a "deep representation" description, stored in the computer's equivalent of long term memory (as in figure 1 – the program had just two "images" hand-coded in: a car, as shown in figure 2, and a chair, in similar vein), and can thus be readily manipulated in various ways.

SEP

Expert writing Tips

Best practice

When discussing machine learning or AI, use "deep representations" to denote the abstract, high-level features learned by deep neural networks.

Common error

Don't use "deep representations" as a synonym for any type of data representation; reserve it for contexts involving deep learning or similarly complex models.

Linguistic Context

The primary grammatical function of "deep representations" is as a noun phrase. It refers to the abstract features or patterns learned by deep learning models, as demonstrated in Ludwig examples describing the extraction of these features.

Expression frequency: Uncommon

Frequent in

Science

75%

News & Media

20%

Formal & Business

5%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "deep representations" is a noun phrase primarily used in scientific and technical contexts to describe the abstract features learned by deep learning models. According to Ludwig AI, the phrase is grammatically correct, although uncommon. When writing, it's best to use it when discussing machine learning or AI. Be careful not to overgeneralize its use. Alternatives include "abstracted representations", "hierarchical representations", and "latent representations". Overall, understanding and using "deep representations" correctly will enhance clarity and precision in technical writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

abstracted representations

Focuses on the level of abstraction achieved in the representation.

hierarchical representations

Highlights the hierarchical organization of the representation.

latent representations

Emphasizes the hidden or underlying nature of the representation.

learned representations

Highlights the process by which the representation was acquired.

complex models

Focuses on the intricacy and sophistication of the models.

multi-layered features

Describes the structure of the representation with multiple layers.

feature embeddings

Highlights the mapping of features into a lower-dimensional space.

internalized models

Focuses on the internal nature of the representation.

sophisticated abstractions

Highlights the complexity of the process.

in-depth portrayals

Emphasizes the level of detail used in the portrayal.

FAQs

How are "deep representations" used in machine learning?

"Deep representations" are learned by deep neural networks to automatically extract relevant features from raw data, creating hierarchical abstractions that improve performance in tasks such as image recognition and natural language processing.

What is an example of "deep representations" in image recognition?

In image recognition, a deep neural network might learn "deep representations" that capture edges, textures, and object parts, combining them to recognize whole objects. These "learned representations" are more robust than hand-engineered features.

How do "deep representations" differ from traditional feature engineering?

Traditional feature engineering requires manual selection and design of features, while "deep representations" are automatically learned from data, reducing the need for domain expertise and allowing the model to discover more complex and relevant features.

What are some alternatives to using "deep representations"?

Depending on the context, you could use terms like "abstracted representations", "hierarchical representations", or "latent representations" to convey similar ideas.