Model Architecture Adaption for Bayesian Neural Networks

• URL: http://arxiv.org/abs/2202.04392v1
• Date: Wed, 9 Feb 2022 10:58:50 GMT
• Title: Model Architecture Adaption for Bayesian Neural Networks
• Authors: Duo Wang, Yiren Zhao, Ilia Shumailov, Robert Mullins
• Abstract summary: We show a novel network architecture search (NAS) that optimize BNNs for both accuracy and uncertainty. In our experiments, the searched models show comparable uncertainty ability and accuracy compared to the state-of-the-art (deep ensemble)
• Score: 9.978961706999833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to quantify the uncertainty of model predictions but come with a prohibitive computation cost for both training and inference. In this work, we show a novel network architecture search (NAS) that optimizes BNNs for both accuracy and uncertainty while having a reduced inference latency. Different from canonical NAS that optimizes solely for in-distribution likelihood, the proposed scheme searches for the uncertainty performance using both in- and out-of-distribution data. Our method is able to search for the correct placement of Bayesian layer(s) in a network. In our experiments, the searched models show comparable uncertainty quantification ability and accuracy compared to the state-of-the-art (deep ensemble). In addition, the searched models use only a fraction of the runtime compared to many popular BNN baselines, reducing the inference runtime cost by $2.98 \times$ and $2.92 \times$ respectively on the CIFAR10 dataset when compared to MCDropout and deep ensemble.

Related papers

• Bayesian Inference Accelerator for Spiking Neural Networks [3.145754107337963]
  spiking neural networks (SNNs) have the potential to reduce computational area and power. In this work, we demonstrate an optimization framework for developing and implementing efficient Bayesian SNNs in hardware. We demonstrate accuracies comparable to Bayesian binary networks with full-precision Bernoulli parameters, while requiring up to $25times$ less spikes.
  arXiv  Detail & Related papers  (2024-01-27T16:27:19Z)
• Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale [0.7025445595542577]
  Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. BayNNs with Dropout as an approximation offer a systematic approach to uncertainty, but they inherently suffer from high hardware overhead in terms of power, memory, and quantifying. We introduce a novel Spintronic memory-based CIM architecture for the proposed BayNN that achieves more than $100times$ energy savings compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2023-11-27T13:41:20Z)
• Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks [0.0]
  Multiplicative normalizing flows (MNFs) are a family of approximate posteriors for the parameters of BNNs. We have compared MNFs with respect to the standard BNNs, and the flipout estimator. MNFs provide more realistic predictive distribution closer to the true posterior mitigating the bias introduced by the variational approximation.
  arXiv  Detail & Related papers  (2023-01-09T16:07:48Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration [74.5509794733707]
  We present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.515% on BNNs. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.
  arXiv  Detail & Related papers  (2021-02-17T18:59:28Z)
• 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)
• FATNN: Fast and Accurate Ternary Neural Networks [89.07796377047619]
  Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. In this work, we show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by a factor of 2. We elaborately design an implementation-dependent ternary quantization algorithm to mitigate the performance gap.
  arXiv  Detail & Related papers  (2020-08-12T04:26:18Z)
• Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization [51.26579110596767]
  We propose a novel Barrier Penalty based NAS (BP-NAS) for mixed precision quantization. BP-NAS sets new state of the arts on both classification (Cifar-10, ImageNet) and detection (COCO)
  arXiv  Detail & Related papers  (2020-07-20T12:00:48Z)
• Accuracy Prediction with Non-neural Model for Neural Architecture Search [185.0651567642238]
  We study an alternative approach which uses non-neural model for accuracy prediction. We leverage gradient boosting decision tree (GBDT) as the predictor for Neural architecture search (NAS) Experiments on NASBench-101 and ImageNet demonstrate the effectiveness of using GBDT as predictor for NAS.
  arXiv  Detail & Related papers  (2020-07-09T13:28:49Z)
• Compromise-free Bayesian neural networks [0.0]
  We numerically sample the full (non-Gaussian and multimodal) network posterior and obtain numerical estimates of the Bayesian evidence. Networks with $ReLU$ activation functions have consistently higher evidences than those with $tanh$ functions.
  arXiv  Detail & Related papers  (2020-04-25T19:12:56Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)

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.