New Millennium AI and the Convergence of History: Update of 2012

Schmidhuber, Jürgen

doi:10.1007/978-3-642-32560-1_4

Cited by 9 publications

(9 citation statements)

References 80 publications

(116 reference statements)

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“…Indeed, the main reason that the method is written as a generative probabilistic model is precisely so that it can account for the changing noise model (changing noise variances) from object to object and pixel to pixel. Standard supervised methods from the machine-learning literature (such as Random Forest, Breiman 2001, Deep Learning (e.g., LeCun et al 1989Bengio 2009;Schmidhuber 2015), and Kernel Support Vector Machines (Smola & Schlkopf 2004), do not have the property that they can account for variable noise models. These traditional machine-learning methods perform very badly as the training data become different from the test data (as they do in our S/N experiments in Section 5.6).…”

Section: Discussionmentioning

confidence: 99%

THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION

Ness

Hogg

Rix

et al. 2015

ApJ

396

537

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New spectroscopic surveys offer the promise of stellar parameters and abundances ("stellar labels") for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the "known" labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with T eff , g log , and [Fe H] as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.

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Section: Discussionmentioning

confidence: 99%

THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION

Ness

Hogg

Rix

et al. 2015

ApJ

396

537

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“…In Figure 8, the CIFAR10 data set, which includes 32 × 32 × 3 pixel colour mappings. 10,000 training set, and 10,000 testing data, was generated using this data set [28]. Figures 9 and 10 are given below.…”

Section: Problem Formulationmentioning

confidence: 99%

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Roohullah

Wahid

Ali

et al. 2022

Security and Communication Networks

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Accident detection in autonomous vehicles could save lives by reducing the time it takes for information to reach emergency responder. One of the most common reason for the death of humans is accident. Indeed, it was determined throughout the survey that road accidents are indeed the second greatest cause of death in the United States for people aged 30 to 44 years, representing for 1/3 among all deaths. The transportation industry is increasingly relying on mathematical methods and new data assets to detect injuries. Many machine learning and deep learning models have already been proposed for accident detection but still there is much space for further improvement to be done to save human lives in case of accident detection, if accidents are not identified well. In our present study, we proposed modified restricted Boltzmann machine for accident detection. Our proposed methodology consists of the following steps. In the first step, we took different accidental and nonaccidental images as an input. In the second step, we applied our proposed deep learning technique modified restricted Boltzmann machine. In the third step, when weight acceleration and coefficient adjustments are run as a generalization mechanism, then we check our model performance after applying through multiple procedures. As a result, multiple images are classified as accidental and nonaccidental images of vehicles. Proposed methodology has been applied for data set, and data have been divided into different training and testing ratios. The proposed MRBM model has an accuracy of 98% in classification of both accidental and nonaccidental images of vehicles. The proposed model outperforms the competition significantly than other in which they are compared like artificial neural network, support vector machine, and restricted Boltzmann machine techniques.

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“…Figure 12: e CIFAR10 dataset, which contains color maps with 32 × 32 × 3 pixels. We used 10,000 training samples and 10,000 test samples of this dataset [28]. Figure 13: e CIFAR100 dataset, which contains color maps with 32 × 32 × 3 pixels.…”

Section: Design Of the Comparative Experimentmentioning

confidence: 99%

“…Figure 13: e CIFAR100 dataset, which contains color maps with 32 × 32 × 3 pixels. e CIFAR100 dataset used in this paper consists of 10,000 training sets and 10,000 test sets [28]. Figure 14: e USPS handwritten dataset derived from the US Postal Service handwritten numeral recognition library.…”

Section: Design Of the Comparative Experimentmentioning

confidence: 99%

A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy

Wang

Gao

Wan

et al. 2020

Mathematical Problems in Engineering

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The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient. An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain. The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients. To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm. The experiments involved learning the RBM based on standard data. The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database. The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification.

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New Millennium AI and the Convergence of History: Update of 2012

Cited by 9 publications

References 80 publications

THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION

THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy

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