Masked Bayesian Neural Networks : Computation and Optimality

• URL: http://arxiv.org/abs/2206.00853v2
• Date: Tue, 23 May 2023 05:40:32 GMT
• Title: Masked Bayesian Neural Networks : Computation and Optimality
• Authors: Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Yongdai Kim
• Abstract summary: We propose a novel sparse Bayesian neural network (BNN) which searches a good deep neural network with an appropriate complexity. We employ the masking variables at each node which can turn off some nodes according to the posterior distribution to yield a nodewise sparse DNN. By analyzing several benchmark datasets, we illustrate that the proposed BNN performs well compared to other existing methods.
• Score: 1.3649494534428745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify such complex and huge DNNs. In this paper, we propose a novel sparse Bayesian neural network (BNN) which searches a good DNN with an appropriate complexity. We employ the masking variables at each node which can turn off some nodes according to the posterior distribution to yield a nodewise sparse DNN. We devise a prior distribution such that the posterior distribution has theoretical optimalities (i.e. minimax optimality and adaptiveness), and develop an efficient MCMC algorithm. By analyzing several benchmark datasets, we illustrate that the proposed BNN performs well compared to other existing methods in the sense that it discovers well condensed DNN architectures with similar prediction accuracy and uncertainty quantification compared to large DNNs.

Related papers

• NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
  We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
  arXiv  Detail & Related papers  (2024-08-28T02:17:58Z)
• On Generalization Bounds for Deep Compound Gaussian Neural Networks [1.4425878137951238]
  Unrolled deep neural networks (DNNs) provide better interpretability and superior empirical performance than standard DNNs. We develop novel generalization error bounds for a class of unrolled DNNs informed by a compound Gaussian prior. Under realistic conditions, we show that, at worst, the generalization error scales $mathcalO(nsqrt(n))$ in the signal dimension and $mathcalO(($Network Size$)3/2)$ in network size.
  arXiv  Detail & Related papers  (2024-02-20T16:01:39Z)
• Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference [1.2722697496405464]
  We propose a new node-sparse BNN model which has good theoretical properties and is computationally feasible. We prove that the posterior concentration rate to the true model is near minimax optimal and adaptive to the smoothness of the true model. In addition, we develop a novel MCMC algorithm which makes the Bayesian inference of the node-sparse BNN model feasible in practice.
  arXiv  Detail & Related papers  (2023-05-24T06:16:11Z)
• Searching Similarity Measure for Binarized Neural Networks [14.847148292246374]
  Binarized Neural Networks (BNNs) are a promising model to be deployed in resource-limited devices. BNNs suffer from non-trivial accuracy degradation, limiting its applicability in various domains. We propose an automatic searching method, based on genetic algorithm, for BNN-tailored similarity measure.
  arXiv  Detail & Related papers  (2022-06-05T06:53:53Z)
• 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)
• Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks [72.81092567651395]
  Sub-bit Neural Networks (SNNs) are a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs.
  arXiv  Detail & Related papers  (2021-10-18T11:30:29Z)
• Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks [2.649890751459017]
  This paper proposes a novel learning approach to obtain sparse fully connected layers in neural networks (NNs) automatically. We design a switcher neural network (SNN) to optimize the structure of the task neural network (TNN)
  arXiv  Detail & Related papers  (2021-03-26T11:29:40Z)
• Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification [10.727102755903616]
  We aim for efficient deep BNNs amenable to complex computer vision architectures. We achieve this by leveraging variational autoencoders (VAEs) to learn the interaction and the latent distribution of the parameters at each network layer. Our approach, Latent-Posterior BNN (LP-BNN), is compatible with the recent BatchEnsemble method, leading to highly efficient (in terms of computation and memory during both training and testing) ensembles.
  arXiv  Detail & Related papers  (2020-12-04T19:50:09Z)
• Exact posterior distributions of wide Bayesian neural networks [51.20413322972014]
  We show that the exact BNN posterior converges (weakly) to the one induced by the GP limit of the prior. For empirical validation, we show how to generate exact samples from a finite BNN on a small dataset via rejection sampling.
  arXiv  Detail & Related papers  (2020-06-18T13:57:04Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks [52.972605601174955]
  We show a ResNet-type CNN can attain the minimax optimal error rates in important function classes. We derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H"older classes.
  arXiv  Detail & Related papers  (2019-03-24T19:42:39Z)

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.