Energy Decay Network (EDeN)
- URL: http://arxiv.org/abs/2103.15552v1
- Date: Wed, 10 Mar 2021 23:17:59 GMT
- Title: Energy Decay Network (EDeN)
- Authors: Jamie Nicholas Shelley, Optishell Consultancy
- Abstract summary: The Framework attempts to develop a genetic transfer of experience through potential structural expressions.
Successful routes are defined by stability of the spike distribution per epoch.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper and accompanying Python and C++ Framework is the product of the
authors perceived problems with narrow (Discrimination based) AI. (Artificial
Intelligence) The Framework attempts to develop a genetic transfer of
experience through potential structural expressions using a common
regulation/exchange value (energy) to create a model whereby neural
architecture and all unit processes are co-dependently developed by genetic and
real time signal processing influences; successful routes are defined by
stability of the spike distribution per epoch which is influenced by
genetically encoded morphological development biases.These principles are aimed
towards creating a diverse and robust network that is capable of adapting to
general tasks by training within a simulation designed for transfer learning to
other mediums at scale.
Related papers
- Self Expanding Convolutional Neural Networks [1.4330085996657045]
We present a novel method for dynamically expanding Convolutional Neural Networks (CNNs) during training.
We employ a strategy where a single model is dynamically expanded, facilitating the extraction of checkpoints at various complexity levels.
arXiv Detail & Related papers (2024-01-11T06:22:40Z) - Recurrent neural networks and transfer learning for elasto-plasticity in
woven composites [0.0]
This article presents Recurrent Neural Network (RNN) models as a surrogate for computationally intensive meso-scale simulation of woven composites.
A mean-field model generates a comprehensive data set representing elasto-plastic behavior.
In simulations, arbitrary six-dimensional strain histories are used to predict stresses under random walking as the source task and cyclic loading conditions as the target task.
arXiv Detail & Related papers (2023-11-22T14:47:54Z) - Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
We propose a domain transfer approach based on conditional invertible neural networks (cINNs)
Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood.
Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
arXiv Detail & Related papers (2023-03-17T18:00:27Z) - Multiobjective Evolutionary Pruning of Deep Neural Networks with
Transfer Learning for improving their Performance and Robustness [15.29595828816055]
This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm.
We use Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm.
Experiments show that our proposal achieves promising results in all the objectives, and direct relation are presented.
arXiv Detail & Related papers (2023-02-20T19:33:38Z) - Sequence Learning Using Equilibrium Propagation [2.3361887733755897]
Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation.
We leverage recent developments in modern hopfield networks to further understand energy based models and develop solutions for complex sequence classification tasks using EP.
arXiv Detail & Related papers (2022-09-14T20:01:22Z) - A developmental approach for training deep belief networks [0.46699574490885926]
Deep belief networks (DBNs) are neural networks that can extract rich internal representations of the environment from the sensory data.
We present iDBN, an iterative learning algorithm for DBNs that allows to jointly update the connection weights across all layers of the hierarchy.
Our work paves the way to the use of iDBN for modeling neurocognitive development.
arXiv Detail & Related papers (2022-07-12T11:37:58Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - Epigenetic evolution of deep convolutional models [81.21462458089142]
We build upon a previously proposed neuroevolution framework to evolve deep convolutional models.
We propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer.
The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator.
arXiv Detail & Related papers (2021-04-12T12:45:16Z) - Neural Cellular Automata Manifold [84.08170531451006]
We show that the neural network architecture of the Neural Cellular Automata can be encapsulated in a larger NN.
This allows us to propose a new model that encodes a manifold of NCA, each of them capable of generating a distinct image.
In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation.
arXiv Detail & Related papers (2020-06-22T11:41:57Z) - Delta Schema Network in Model-based Reinforcement Learning [125.99533416395765]
This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning.
We are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data.
We present algorithms for training a Delta Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward.
arXiv Detail & Related papers (2020-06-17T15:58:25Z) - Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
We propose a novel learning framework for inference and estimation problems of diffusion on networks.
Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities.
Our approach is versatile and robust to variations of the underlying diffusion network models.
arXiv Detail & Related papers (2020-06-16T18:45:20Z)
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.