Draw your Neural Networks
- URL: http://arxiv.org/abs/2012.09609v1
- Date: Sat, 12 Dec 2020 09:44:03 GMT
- Title: Draw your Neural Networks
- Authors: Jatin Sharma and Shobha Lata
- Abstract summary: We present Sketch framework, that uses this GUI-based approach to design and modify the neural networks.
The system provides popular layers and operations out-of-the-box and could import any supported pre-trained model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Deep Neural Networks are the basic building blocks of modern Artificial
Intelligence. They are increasingly replacing or augmenting existing software
systems due to their ability to learn directly from the data and superior
accuracy on variety of tasks. Existing Software Development Life Cycle (SDLC)
methodologies fall short on representing the unique capabilities and
requirements of AI Development and must be replaced with Artificial
Intelligence Development Life Cycle (AIDLC) methodologies. In this paper, we
discuss an alternative and more natural approach to develop neural networks
that involves intuitive GUI elements such as blocks and lines to draw them
instead of complex computer programming. We present Sketch framework, that uses
this GUI-based approach to design and modify the neural networks and provides
interoperability with traditional frameworks. The system provides popular
layers and operations out-of-the-box and could import any supported pre-trained
model making it a faster method to design and train complex neural networks and
ultimately democratizing the AI by removing the learning curve.
Related papers
- Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces.
While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks.
This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z) - Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences.
It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations.
Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
arXiv Detail & Related papers (2024-02-20T15:23:24Z) - GreenLightningAI: An Efficient AI System with Decoupled Structural and
Quantitative Knowledge [0.0]
Training powerful and popular deep neural networks comes at very high economic and environmental costs.
This work takes a radically different approach by proposing GreenLightningAI.
The new AI system stores the information required to select the system subset for a given sample.
We show experimentally that the structural information can be kept unmodified when re-training the AI system with new samples.
arXiv Detail & Related papers (2023-12-15T17:34:11Z) - COOL: A Constraint Object-Oriented Logic Programming Language and its
Neural-Symbolic Compilation System [0.0]
We introduce the COOL programming language, which seamlessly combines logical reasoning with neural network technologies.
COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data.
It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle.
arXiv Detail & Related papers (2023-11-07T06:29:59Z) - Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence [51.269276328087855]
Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence.
This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
arXiv Detail & Related papers (2023-07-06T05:16:55Z) - Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data.
We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data.
NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
arXiv Detail & Related papers (2022-05-25T12:41:59Z) - Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
We conduct a systematic review for both cloud and edge AI.
We are the first to set up the collaborative learning mechanism for cloud and edge modeling.
We discuss potentials and practical experiences of some on-going advanced edge AI topics.
arXiv Detail & Related papers (2021-11-11T05:58:23Z) - Brain-Inspired Learning on Neuromorphic Substrates [5.279475826661643]
This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates.
Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL) and biologically plausible learning rules for training Spiking Neural Networks (SNNs)
We motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm's computational complexity.
arXiv Detail & Related papers (2020-10-22T17:56:59Z) - Neuromorphic Processing and Sensing: Evolutionary Progression of AI to
Spiking [0.0]
Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements.
This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices.
A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs.
arXiv Detail & Related papers (2020-07-10T20:54:42Z) - AI from concrete to abstract: demystifying artificial intelligence to
the general public [0.0]
This article presents a new methodology, AI from concrete to abstract (AIcon2abs)
The main strategy adopted by is to promote a demystification of artificial intelligence.
The simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks.
arXiv Detail & Related papers (2020-06-07T01:14:06Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.