GradientBased Learning Applied to Document Recognition

Haykin, S.; Kosko, Bart

doi:10.1109/9780470544976.ch9

Cited by 198 publications

(9 citation statements)

References 59 publications

(98 reference statements)

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“…To determine if MultiMAP can effectively leverage features unique to certain datasets, we used the MNIST database 14 , where handwritten images were split horizontally with thin overlap (Figure 2c; see Methods for details). The two datasets can be considered multimodal because they have different feature spaces but describe the same set of digit images.…”

Section: Resultsmentioning

confidence: 99%

MultiMAP: Dimensionality Reduction and Integration of Multimodal Data

Jain

Polański

Conde

et al. 2021

Preprint

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Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, an approach for the dimensionality reduction and integration of multiple datasets. MultiMAP recovers a single manifold on which all of the data resides and then projects the data into a single low-dimensional space so as to preserve the structure of the manifold. MultiMAP is based on a framework of Riemannian geometry and algebraic topology, and generalizes the popular UMAP algorithm to the multimodal setting. MultiMAP can be used for visualization of multimodal data, and as an integration approach that enables joint analyses. MultiMAP has several advantages over existing integration strategies for single-cell data, including that MultiMAP can integrate any number of datasets, leverages features that are not present in all datasets (i.e. datasets can be of different dimensionalities), is not restricted to a linear mapping, can control the influence of each dataset on the embedding, and is extremely scalable to large datasets. We apply MultiMAP to the integration of a variety of single-cell transcriptomics, chromatin accessibility, methylation, and spatial data, and show that it outperforms current approaches in preservation of high-dimensional structure, alignment of datasets, visual separation of clusters, transfer learning, and runtime. On a newly generated single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) and single-cell RNA-seq (scRNA-seq) dataset of the human thymus, we use MultiMAP to integrate cells along a temporal trajectory. This enables the quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of transcription factor kinetics.

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Section: Resultsmentioning

confidence: 99%

MultiMAP: Dimensionality Reduction and Integration of Multimodal Data

Jain

Polański

Conde

et al. 2021

Preprint

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“…It is easier for people to recognize the difference between clean inputs and perturbed inputs. For example, the τ of the MNIST dataset [28] should be smaller than the CIFAR-10 dataset [29].…”

Section: Definition 3 (N-order Tensormentioning

confidence: 99%

Minimum Adversarial Examples

Liu

Yan

2022

Entropy

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Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and the successful attack as the constraint. These all involve two fundamental problems of AEs: the minimum boundary of constructing the AEs and whether that boundary is reachable. The reachability means whether the AEs of successful attack models exist equal to that boundary. Previous optimization models have no complete answer to the problems. Therefore, in this paper, for the first problem, we propose the definition of the minimum AEs and give the theoretical lower bound of the amplitude of the minimum AEs. For the second problem, we prove that solving the generation of the minimum AEs is an NPC problem, and then based on its computational inaccessibility, we establish a new third optimization model. This model is general and can adapt to any constraint. To verify the model, we devise two specific methods for generating controllable AEs under the widely used distance evaluation standard of adversarial perturbations, namely Lp constraint and SSIM constraint (structural similarity). This model limits the amplitude of the AEs, reduces the solution space’s search cost, and is further improved in efficiency. In theory, those AEs generated by the new model which are closer to the actual minimum adversarial boundary overcome the blindness of the adversarial amplitude setting of the existing methods and further improve the attack success rate. In addition, this model can generate accurate AEs with controllable amplitude under different constraints, which is suitable for different application scenarios. In addition, through extensive experiments, they demonstrate a better attack ability under the same constraints as other baseline attacks. For all the datasets we test in the experiment, compared with other baseline methods, the attack success rate of our method is improved by approximately 10%.

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“…A CNN with a convolutional layer and a pooling layer was proposed by Fukushima (1980) , which was subsequently improved to LeNet ( LeCun et al, 1998 ), GoogleNet ( Szegedy et al, 2015 ), ResNet ( He et al, 2016 ), AlexNet ( Krizhevsky et al, 2017 ), and so on. With the appearance of R-CNN ( Girshick et al, 2014 ), CNN-based object detection became a hot research topic on computer vision and digital image processing ( Zhao Z. et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

et al. 2022

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As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

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GradientBased Learning Applied to Document Recognition

Cited by 198 publications

References 59 publications

MultiMAP: Dimensionality Reduction and Integration of Multimodal Data

MultiMAP: Dimensionality Reduction and Integration of Multimodal Data

Minimum Adversarial Examples

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

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