Persistent Neurons

• URL: http://arxiv.org/abs/2007.01419v2
• Date: Thu, 18 Mar 2021 09:16:24 GMT
• Title: Persistent Neurons
• Authors: Yimeng Min
• Abstract summary: We propose a trajectory-based strategy that optimize the learning task using information from previous solutions. Persistent neurons can be regarded as a method with gradient informed bias where individual updates are corrupted by deterministic error terms. We evaluate the full and partial persistent model and show it can be used to boost the performance on a range of NN structures.
• Score: 4.061135251278187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks (NN)-based learning algorithms are strongly affected by the choices of initialization and data distribution. Different optimization strategies have been proposed for improving the learning trajectory and finding a better optima. However, designing improved optimization strategies is a difficult task under the conventional landscape view. Here, we propose persistent neurons, a trajectory-based strategy that optimizes the learning task using information from previous converged solutions. More precisely, we utilize the end of trajectories and let the parameters explore new landscapes by penalizing the model from converging to the previous solutions under the same initialization. Persistent neurons can be regarded as a stochastic gradient method with informed bias where individual updates are corrupted by deterministic error terms. Specifically, we show that persistent neurons, under certain data distribution, is able to converge to more optimal solutions while initializations under popular framework find bad local minima. We further demonstrate that persistent neurons helps improve the model's performance under both good and poor initializations. We evaluate the full and partial persistent model and show it can be used to boost the performance on a range of NN structures, such as AlexNet and residual neural network (ResNet).

Related papers

• Improving Generalization of Deep Neural Networks by Optimum Shifting [33.092571599896814]
  We propose a novel method called emphoptimum shifting, which changes the parameters of a neural network from a sharp minimum to a flatter one. Our method is based on the observation that when the input and output of a neural network are fixed, the matrix multiplications within the network can be treated as systems of under-determined linear equations.
  arXiv  Detail & Related papers  (2024-05-23T02:31:55Z)
• Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks [2.0072624123275533]
  The generalized Gauss-Newton (GGN) optimization method incorporates curvature estimates into its solution steps. This work studies a GGN method for optimizing a two-layer neural network with explicit regularization.
  arXiv  Detail & Related papers  (2024-04-23T10:02:22Z)
• Model-Based Control with Sparse Neural Dynamics [23.961218902837807]
  We propose a new framework for integrated model learning and predictive control. We show that our framework can deliver better closed-loop performance than existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-20T06:25:02Z)
• Learning Large-scale Neural Fields via Context Pruned Meta-Learning [60.93679437452872]
  We introduce an efficient optimization-based meta-learning technique for large-scale neural field training. We show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals.
  arXiv  Detail & Related papers  (2023-02-01T17:32:16Z)
• Towards Theoretically Inspired Neural Initialization Optimization [66.04735385415427]
  We propose a differentiable quantity, named GradCosine, with theoretical insights to evaluate the initial state of a neural network. We show that both the training and test performance of a network can be improved by maximizing GradCosine under norm constraint. Generalized from the sample-wise analysis into the real batch setting, NIO is able to automatically look for a better initialization with negligible cost.
  arXiv  Detail & Related papers  (2022-10-12T06:49:16Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Acceleration techniques for optimization over trained neural network ensembles [1.0323063834827415]
  We study optimization problems where the objective function is modeled through feedforward neural networks with rectified linear unit activation. We present a mixed-integer linear program based on existing popular big-$M$ formulations for optimizing over a single neural network.
  arXiv  Detail & Related papers  (2021-12-13T20:50:54Z)
• Non-Gradient Manifold Neural Network [79.44066256794187]
  Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent. We propose a novel manifold neural network based on non-gradient optimization.
  arXiv  Detail & Related papers  (2021-06-15T06:39:13Z)
• A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks [23.038631072178735]
  We consider a broad class of optimization algorithms that are commonly used in practice. As a consequence, we can leverage the convergence behavior of neural networks. We believe our approach can also be extended to other optimization algorithms and network theory.
  arXiv  Detail & Related papers  (2020-10-25T17:10:22Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
  Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. The use of gradient combined nonvolutionity renders learning susceptible to novel problems. We propose fusing neighboring layers of deeper networks that are trained with random variables.
  arXiv  Detail & Related papers  (2020-01-28T18:25:15Z)

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.