Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"BNN"”.
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Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies
… As a key example, Bayesian Neural Networks (BNNs) are one of the most successful Bayesian models being increasingly employed in a wide range of real-world AI applications which demand reliable and robust decisions. However, the nature of BNN stochastic inference and training procedures incurs …
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Priors in finite and infinite Bayesian convolutional neural networks
Bayesian neural networks (BNNs) have undergone many changes since the seminal work of Neal [Nea96]. Advances in approximate inference and the use of GPUs have scaled BNNs to larger data sets, and much higher layer and parameter counts. Yet, the priors used for BNN parameters have remained …
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Towards Reliable AI via Efficient Verification of Binarized Neural Networks
… In this context, Binarized Neural Networks (BNNs) are attractive because they work with quantized inputs and binarized internal activation and weight values and thus support verification free of floating point error. The binarized computation of BNNs directly corresponds to logical reasoning. …
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The Photochemistry of Beta-Naphthoyl Azide and 4-Acetylphenoxycarbonyl Azide: The Chemical and Physical Properties of Acylnitrenes
… (APN) and $\beta$-naphthoylnitrene (BNN) generated by the photolysis of 4-acetylphenoxycarbonyl azide and $\beta$-naphthoyl azide (BNA) respectively. These nitrenes were investigated using low-temperature optical, electron spin resonance, and transient absorption spectroscopy as well …
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Pengaruh etos kerja Islam terhadap kualitas kerja karyawan melalui kinerja: Studi pada Kantor Badan Narkotika Nasional Kota Malang
… dan organisasi. Penelitian dilakukan di kantor BNN Kota Malang. Tujuan penelitian ini adalah untuk menguji dan menganalisis pengaruh etos kerja islam terhadap kualitas kerja karyawan secara langsung, menguji dan menganalisis pengaruh etos kerja islam terhadap kualitas kerja karyawan melalui …
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Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
Bayesian Neural Networks (BNNs) integrate the representational power of standard neural networks with the uncertainty estimation capabilities of Bayesian Inference, offering a robust framework to address challenges such as overconfidence and overfitting. However, the inherent complexity of BNNs …
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Efficient Ensemble-based Bayesian Neural Networks for Depth Regression
… in the training data. Bayesian Neural Networks (BNNs) extend the capabilities of traditional NNs by modeling uncertainty in their predictions. Since analytical Bayesian inference is often intractable for high-dimensional NNs, approximate approaches are used to realize BNNs. Monte Carlo Dropout …
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Bayesian Learning for Data-Efficient Control
… control using Bayesian neural networks (BNN). Experimentally we show although filtering mitigates adverse effects of observation noise, much greater performance is achieved when optimising controllers with evaluations faithful to reality: by simulating closed-loop filtered control if …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
… approaches such as Bayesian neural networks (BNN) do not scale well in terms of memory and runtime and often underperform simple deterministic baselines in terms of accuracy. Furthermore, BNNs underperform deep ensembles as they fail to explore multiple modes, in the loss space, while being …
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Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data
… Vector Machines (SVM), Basic Neural Networks (BNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN) have been highly used in wildfire prediction. The goal of this research is to discover the best combination of data and prediction …
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Connectome-Constrained Artificial Neural Networks
In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural …
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Bayesian Neural Networks for Actuarial Mortality Modelling
The use of Bayesian neural networks (BNNs) for mortality modelling is an understudied, yet potentially promising area of research. They inherently offer robust uncertainty quantification, and are known for their application to sparse or small datasets. This research investigates the efficacy of BNN …
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The Future of Computing: An Energy-Efficient In-Memory Computing Architectures with Emerging VGSOT MRAM Technology
… using the MNIST and FMNIST datasets with a BNN model structured as 512-512-10 (input layer - hidden layer - output layer), the proposed VGSOT MRAM demonstrates exceptional inference accuracy. Specifically, it achieves a high accuracy rate of 97.40% for the MNIST dataset and 84.15% for the …
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Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals
… in this thesis relate to Bayesian NNs (BNNs). Specifying appropriate priors is an important step in any Bayesian model, yet it is not clear how to do this in BNNs. The first contribution shows that the connection between BNNs and Gaussian Processes (GPs) provides an effective lens to …
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Machine learning methods modeling waveform, multi-parameter full waveform inversion, and uncertainty quantification
… a method that uses the Bayesian neural network (BNN) to provide prior uncertainty for the elastic models and an algorithm that efficiently approximates inverse Hessian. During the inversion process, simplifications in wave propagation physics are often made to enhance computational efficiency. …
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Improved Sampling and Variational Inference Methods for Neural Networks
Bayesian Neural Networks (BNNs), an application of Bayesian inference to neural networks, offer an alternative way of training. They combine multiple weight settings, each compatible with the training data, and quantify the uncertainty about the network's weights. While Bayesian inference applied …
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Autonomous Experimentation to Accelerate Boiling Heat Transfer Research
… framework utilizing Binary Neural Networks (BNN) supported by direct memory access (DMA) of binary data recorded by an IR camera was established to implement ML prediction models seamlessly in real-time during experimental procedures. A machine learning-based model was developed to predict …
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Audio computing in the wild: frameworks for big data and small computers
… deployment system, Bitwise Neural Networks (BNN) will be also discussed. In the proposed BNN, all the input, hidden, and output nodes are binaries (+1 and -1), and so are all the weights and bias. Consequently, the operations on them during the test time are defined with Boolean algebra, too. …