Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization

• URL: http://arxiv.org/abs/2403.05610v1
• Date: Fri, 8 Mar 2024 13:23:42 GMT
• Title: Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization
• Authors: Thien An L. Nguyen
• Abstract summary: Understanding the process of neural networks is one of the most complex and crucial issues in the field of machine learning. This paper focuses on the theoretical convergence of artificial neural networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the convergence process of neural networks is one of the most complex and crucial issues in the field of machine learning. Despite the close association of notable successes in this domain with the convergence of artificial neural networks, this concept remains predominantly theoretical. In reality, due to the non-convex nature of the optimization problems that artificial neural networks tackle, very few trained networks actually achieve convergence. To expand recent research efforts on artificial-neural-network convergence, this paper will discuss a different approach based on observations of cohesive-convergence groups emerging during the optimization process of an artificial neural network.

Related papers

• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry [43.584567991256925]
  We propose a new normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. Our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths.
  arXiv  Detail & Related papers  (2023-06-07T22:14:33Z)
• Spiking Generative Adversarial Network with Attention Scoring Decoding [4.5727987473456055]
  Spiking neural networks offer a closer approximation to brain-like processing. We build a spiking generative adversarial network capable of handling complex images.
  arXiv  Detail & Related papers  (2023-05-17T14:35:45Z)
• 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)
• Rank Diminishing in Deep Neural Networks [71.03777954670323]
  Rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear.
  arXiv  Detail & Related papers  (2022-06-13T12:03:32Z)
• Developing Constrained Neural Units Over Time [81.19349325749037]
  This paper focuses on an alternative way of defining Neural Networks, that is different from the majority of existing approaches. The structure of the neural architecture is defined by means of a special class of constraints that are extended also to the interaction with data. The proposed theory is cast into the time domain, in which data are presented to the network in an ordered manner.
  arXiv  Detail & Related papers  (2020-09-01T09:07:25Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics [50.83356836818667]
  We introduce a new theoretical framework to analyze deep learning optimization with connection to its generalization error. Existing frameworks such as mean field theory and neural tangent kernel theory for neural network optimization analysis typically require taking limit of infinite width of the network to show its global convergence.
  arXiv  Detail & Related papers  (2020-07-11T18:19:50Z)
• Topological Insights into Sparse Neural Networks [16.515620374178535]
  We introduce an approach to understand and compare sparse neural network topologies from the perspective of graph theory. We first propose Neural Network Sparse Topology Distance (NNSTD) to measure the distance between different sparse neural networks. We show that adaptive sparse connectivity can always unveil a plenitude of sparse sub-networks with very different topologies which outperform the dense model.
  arXiv  Detail & Related papers  (2020-06-24T22:27:21Z)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)
• Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks [3.7277730514654555]
  We use decision trees to capture relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches.
  arXiv  Detail & Related papers  (2020-02-11T11:22:20Z)

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.