Semi-adaptive Synergetic Two-way Pseudoinverse Learning System

• URL: http://arxiv.org/abs/2406.18931v2
• Date: Sun, 7 Jul 2024 02:02:44 GMT
• Title: Semi-adaptive Synergetic Two-way Pseudoinverse Learning System
• Authors: Binghong Liu, Ziqi Zhao, Shupan Li, Ke Wang,
• Abstract summary: We propose a semi-adaptive synergetic two-way pseudoinverse learning system. Each subsystem encompasses forward learning, backward learning, and feature concatenation modules. The whole system is trained using a non-gradient descent learning algorithm.
• Score: 8.16000189123978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has become a crucial technology for making breakthroughs in many fields. Nevertheless, it still faces two important challenges in theoretical and applied aspects. The first lies in the shortcomings of gradient descent based learning schemes which are time-consuming and difficult to determine the learning control hyperparameters. Next, the architectural design of the model is usually tricky. In this paper, we propose a semi-adaptive synergetic two-way pseudoinverse learning system, wherein each subsystem encompasses forward learning, backward learning, and feature concatenation modules. The whole system is trained using a non-gradient descent learning algorithm. It simplifies the hyperparameter tuning while improving the training efficiency. The architecture of the subsystems is designed using a data-driven approach that enables automated determination of the depth of the subsystems. We compare our method with the baselines of mainstream non-gradient descent based methods and the results demonstrate the effectiveness of our proposed method. The source code for this paper is available at http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System}{http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System.

Related papers

• Deep Learning Method for Computing Committor Functions with Adaptive Sampling [4.599618895656792]
  We propose a deep learning method with two novel adaptive sampling schemes (I and II) In the two schemes, the data are generated actively with a modified potential where the bias potential is constructed from the learned committor function. We theoretically demonstrate the advantages of the sampling schemes and show that the data in sampling scheme II are uniformly distributed along the transition tube.
  arXiv  Detail & Related papers  (2024-04-09T10:53:29Z)
• Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy [17.11946381948498]
  Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. It is challenging to implement deep learning in hardware systems that use noisy analog memristors as artificial synapses. We propose a binary learning algorithm that modifies all elementary neural network operations.
  arXiv  Detail & Related papers  (2023-04-25T14:38:36Z)
• Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling [72.84259710412293]
  Some ODE solvers called adaptive can adapt their evaluation strategy depending on the complexity of the problem at hand. This paper describes a simple set of experiments to show why adaptive solvers cannot be seamlessly leveraged as a black-box for dynamical systems modelling.
  arXiv  Detail & Related papers  (2022-11-13T17:48:04Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• Efficient time stepping for numerical integration using reinforcement learning [0.15393457051344295]
  We propose a data-driven time stepping scheme based on machine learning and meta-learning. First, one or several (in the case of non-smooth or hybrid systems) base learners are trained using RL. Then, a meta-learner is trained which (depending on the system state) selects the base learner that appears to be optimal for the current situation.
  arXiv  Detail & Related papers  (2021-04-08T07:24:54Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Physics-Based Deep Learning for Fiber-Optic Communication Systems [10.630021520220653]
  We propose a new machine-learning approach for fiber-optic communication systems governed by the nonlinear Schr"odinger equation (NLSE) Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network. We exploit this connection by parameterizing the SSM and viewing the linear steps as general linear functions, similar to the weight matrices in a neural network.
  arXiv  Detail & Related papers  (2020-10-27T12:55:23Z)
• Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
  We introduce a fast optimization-based meta-learning method for few-shot classification. Our strategy enables important aspects of the base learner objective to be learned during meta-training. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-10-01T15:59:31Z)
• Gradient-based Competitive Learning: Theory [1.6752712949948443]
  This paper introduces a novel perspective in this area by combining gradient-based and competitive learning. The theory is based on the intuition that neural networks are able to learn topological structures by working directly on the transpose of the input matrix. The proposed approach has a great potential as it can be generalized to a vast selection of topological learning tasks.
  arXiv  Detail & Related papers  (2020-09-06T19:00:51Z)
• Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems [91.43582419264763]
  We study the problem of system identification and adaptive control in partially observable linear dynamical systems. We present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. We show that AdaptOn is the first algorithm that achieves $textpolylogleft(Tright)$ regret in adaptive control of unknown partially observable linear dynamical systems.
  arXiv  Detail & Related papers  (2020-03-25T06:00:33Z)

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.