NINNs: Nudging Induced Neural Networks

• URL: http://arxiv.org/abs/2203.07947v1
• Date: Tue, 15 Mar 2022 14:29:26 GMT
• Title: NINNs: Nudging Induced Neural Networks
• Authors: Harbir Antil, Rainald L\"ohner, Randy Price
• Abstract summary: New algorithms called nudging induced neural networks (NINNs) to control and improve the accuracy of deep neural networks (DNNs) NINNs work by adding a feedback control term to the forward propagation of the network. Rigorous convergence analysis is established for NINNs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: New algorithms called nudging induced neural networks (NINNs), to control and improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs framework can be applied to almost all pre-existing DNNs, with forward propagation, with costs comparable to existing DNNs. NINNs work by adding a feedback control term to the forward propagation of the network. The feedback term nudges the neural network towards a desired quantity of interest. NINNs offer multiple advantages, for instance, they lead to higher accuracy when compared with existing data assimilation algorithms such as nudging. Rigorous convergence analysis is established for NINNs. The algorithmic and theoretical findings are illustrated on examples from data assimilation and chemically reacting flows.

Related papers

• Optimal ANN-SNN Conversion with Group Neurons [39.14228133571838]
  Spiking Neural Networks (SNNs) have emerged as a promising third generation of neural networks. The lack of effective learning algorithms remains a challenge for SNNs. We introduce a novel type of neuron called Group Neurons (GNs)
  arXiv  Detail & Related papers  (2024-02-29T11:41:12Z)
• Harnessing Neuron Stability to Improve DNN Verification [42.65507402735545]
  We present VeriStable, a novel extension of recently proposed DPLL-based constraint DNN verification approach. We evaluate the effectiveness of VeriStable across a range of challenging benchmarks including fully-connected feed networks (FNNs), convolutional neural networks (CNNs) and residual networks (ResNets) Preliminary results show that VeriStable is competitive and outperforms state-of-the-art verification tools, including $alpha$-$beta$-CROWN and MN-BaB, the first and second performers of the VNN-COMP, respectively.
  arXiv  Detail & Related papers  (2024-01-19T23:48:04Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Learning Ability of Interpolating Deep Convolutional Neural Networks [28.437011792990347]
  We study the learning ability of an important family of deep neural networks, deep convolutional neural networks (DCNNs) We show that by adding well-defined layers to a non-interpolating DCNN, we can obtain some interpolating DCNNs that maintain the good learning rates of the non-interpolating DCNN. Our work provides theoretical verification of how overfitted DCNNs generalize well.
  arXiv  Detail & Related papers  (2022-10-25T17:22:31Z)
• Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study [55.12108376616355]
  The study on NTK has been devoted to typical neural network architectures, but is incomplete for neural networks with Hadamard products (NNs-Hp) In this work, we derive the finite-width-K formulation for a special class of NNs-Hp, i.e., neural networks. We prove their equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK.
  arXiv  Detail & Related papers  (2022-09-16T06:36:06Z)
• Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks [7.840247953745616]
  Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. This paper establishes a precise mathematical mapping between the biological parameters of the Linear Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep Neural Networks (DNNs)
  arXiv  Detail & Related papers  (2022-05-31T17:02:26Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• A Mixed Integer Programming Approach for Verifying Properties of Binarized Neural Networks [44.44006029119672]
  We propose a mixed integer programming formulation for BNN verification. We demonstrate our approach by verifying properties of BNNs trained on the MNIST dataset and an aircraft collision avoidance controller.
  arXiv  Detail & Related papers  (2022-03-11T01:11:29Z)
• Topological Measurement of Deep Neural Networks Using Persistent Homology [0.7919213739992464]
  The inner representation of deep neural networks (DNNs) is indecipherable. Persistent homology (PH) was employed for investigating the complexities of trained DNNs.
  arXiv  Detail & Related papers  (2021-06-06T03:06:15Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.