What is the difference between deep and shallow modules?
Best insight from top research papers
Deep and shallow modules refer to different approaches in graph representation learning for complex systems. Shallow graph representation algorithms aim to embed graph nodes in a d-dimensional space, while deep representation learning methods involve designing and testing deep learning frameworks. Deep representation learning methods have been primarily applied to social networks, citation networks, and recommendation systems, and it is unclear if they are beneficial for module detection in molecular networks .
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
15 Citations | Copilot couldn't generate the response. Please try again after some time. |
| Copilot couldn't generate the response. Please try again after some time. | |
06 Dec 2022 | The paper does not provide information about the difference between deep and shallow modules. |
Open access•Posted Content | Copilot couldn't generate the response. Please try again after some time. |
06 Dec 2022 | Copilot couldn't generate the response. Please try again after some time. |
Related Questions
Why is shallowness a problem?5 answersShallowness is a problem because it restricts the ability to model turbulent flow accurately. In shallow-water flow, the small depth limits the large coherent structures to two-dimensional horizontal motions, while bottom friction gives rise to small-scale three-dimensional turbulence. This coexistence of different turbulent scales cannot be effectively modeled with conventional turbulence models that use a single length scale. Additionally, shallowness affects environmental flows by influencing bed friction and creating horizontal velocity gradients. The dominance of horizontal length scales over vertical length scales in shallow flows leads to horizontal momentum exchange, but three-dimensional effects driven by turbulence anisotropy also play a role. In the case of mixing layers, shallowness impedes the growth of the layer width and reduces the coherence of large turbulence structures, affecting the exchange of momentum. Therefore, shallowness poses challenges in accurately understanding and modeling various flow phenomena.
What is deep technology?5 answersDeep technology refers to technological advancements that are difficult to develop currently but have the potential to become widely adopted in the future. It involves the convergence of advanced science, engineering, and design to address complex challenges and problem complexity across various sectors. Deep tech innovations have been seen in fields such as parasite detection and diagnosis through smartphone applications, utilizing deep learning techniques for accurate predictions. These technological developments often require significant financial investment and are accompanied by bureaucratic and legal hurdles. Governments can play a crucial role in supporting the development and transfer of deep tech through targeted support measures, contributing to long-term competitiveness and independence of industrial regions. Unlocking deep tech in startups can lead to their long-term success, and support from industry giants, regulatory changes, and access to finance are key factors in fostering deep tech entrepreneurship.
How do module structure systems work?3 answersModule structure systems work by utilizing various components and mechanisms to create a stable and flexible structure. These systems often include modules, which are assembled units that provide framing for the structure. The modules may consist of panels that are implemented within them to create the building structure. The modules are connected to each other using fixed hinges and hinge connectors, which allow for easy attachment and assembly. Additionally, module structures may include support film layers with stress relief gaps, which help to prevent disengagement of the film layers when the structure is bent. Some module structures also incorporate rotating mechanisms, clamping air cylinders, and control mechanisms to facilitate rapid and efficient assembly. Overall, module structure systems offer advantages such as simplified construction, improved connection strength, and the ability to create diverse structural geometries.
What is the difference between deep structure and surface structure in syntax?3 answersDeep structure and surface structure are concepts in syntax that describe different levels of linguistic representation. Deep structure refers to the underlying meaning and organization of a sentence, while surface structure refers to the actual arrangement of words in a sentence. Deep structure is concerned with the relationships between words and the overall meaning conveyed, while surface structure focuses on the specific word order and grammatical structure. The transformational process maps deep structure to surface structure, allowing for the expression of meaning in a grammatically correct and coherent way. The deep-syntactic representation serves as an intermediate stage between deep structure and surface structure, containing grammatical function relation labels but not all function words. Understanding the distinction between deep structure and surface structure is crucial in the study of syntax and how words come together to form sentences.
What are the differences between deep learning and machine learning?5 answersDeep learning is a subset of machine learning that uses deep neural networks with multiple layers to learn and extract features from complex data. Machine learning, on the other hand, involves the development of algorithms that can learn patterns from data and make predictions or decisions without being explicitly programmed. Deep learning algorithms are able to learn complex representations and enhance pattern recognition from raw data, while traditional machine learning techniques rely on human experts to extract features from the data. Deep learning is modeled after computation in the brain and can provide insights that would be impossible to obtain through manual analysis. In summary, deep learning is a more advanced and complex form of machine learning that uses deep neural networks to automatically extract higher-level features from raw input data.
Does Salicornia bigelovii have a higher root biomass in shallow soil or deep soil?5 answersSalicornia bigelovii has been found to have increased shoot and root biomass when treated with certain rhizosphere-competent actinobacterial isolates, such as Streptomyces chartreusis, S. tritolerans, and S. rochei. These isolates significantly enhanced shoot and root dry biomass compared to non-inoculated plants. Additionally, the application of Actinoplanes deccanensis and Streptomyces euryhalinus also increased the length and dry weight of shoot and root tissues in S. bigelovii. However, there is no specific information available in the abstracts regarding the root biomass of Salicornia bigelovii in shallow soil or deep soil.