deep learning technology 2020
Deep Learning refers to the circumstance that we derive the output for our input by passing it through multiple layers / a hierarchy of transformations (i.e., we go through some depth of transforms), instead of having a single, linear formula that tells us the output value directly.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
Malicious URL Detection using Deep Learning
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Malicious Uniform Resource Locator (URL), aka malicious website is a primary mechanism to host unsolicited content, such as spam, malicious advertisements, phishing, drive-by exploits, to name few. There is imperative to detect the malicious URLs in a timely manner
Deep learning models for electrocardiograms are susceptible to adversarial attack
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Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpretation strategies. Recently, deep neural networks have been used to automatically analyze ECG tracings and
Deep learning applied to glacier evolution modelling.
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We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; ie deep learning ). This method has been included as the SMB component of an open-source regional glacier
Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing
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Abstract We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate
EE-559 Deep learning 1.1. From neural networks to deep learning
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1949 Donald Hebb proposes the Hebbian Learning principle. 1951 Marvin Minsky creates the first ANN . 1958 Frank Rosenblatt creates a perceptron to classify 20 20 images. 1959 David H. Hubel and Torsten Wiesel demonstrate
Is Deep Learning a Game Changer for Marketing Analytics
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Different types of organizations will try to harness the powers of deep learning in their own ways. An automaker might use them to target new customers, revamp the buying process, or fine-tune product features a specific set of buyers will want. It could draw on a sea of relevant From early adoption in rehabilitation, the brain machine interfaces (BMIs) have dovetailed into applications empowering humans in controlling external devices such as prosthesis and wheelchairs with a high level of autonomy. The success of such brain machine interfaces With the exponential growth in the use of social media networks such as Twitter, Facebook, Flickr, and many others, an astronomical amount of big data has been generated. This data is present in heterogeneous forms such as text, images, videos, audio, and graphics. A
Understanding Aesthetic Evaluation using Deep Learning
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A bottleneck in any evolutionary art system is aesthetic evaluation. Many different methods have been proposed to automate the evaluation of aesthetics, including measures of symmetry, coherence, complexity, contrast and grouping. The interactive genetic algorithm
EE-559 Deep learning 9.2. Autoencoders
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Many applications such as image synthesis, denoising, super-resolution, speech synthesis, compression, etc. require to go beyond classification and regression, and model explicitly a high dimension signal Many applications such as image synthesis, denoising
EE-559 Deep learning 6.4. Batch normalization
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Page 1. EE-559 Deep learning 6.4. Batch normalization François Fleuret https://fleuret.org/ee559/ 2 2020 Page 2. We saw that maintaining proper statistics of the activations and derivatives was a critical issue to allow the training of deep architectures. François Fleuret EE-559 Deep
EE-559 Deep learning 3.1. The perceptron
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This crude algorithm works often surprisingly well. With MNISTs 0 s as negative class, and 1 s as positive one. epoch 0 nb_changes 64 train_error 0.23% test_error 0.19% epoch 1 nb_changes 24 train_error 0.07% test_error 0.00% epoch 2 nb_changes 10 train_error
Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations
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Riverine bathymetry, ie, riverbed topography estimation, and river flow velocity estimation are of crucial importance for shipping operations, naval operations, and flood management. However, most commonly used techniques for flow velocity/bathymetry estimation rely on
Parameter initialization
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In a standard neural network, the derivative received by each parameter tells it how it should change so the final loss function is reduced, given what all other units are doing. Therefore, units change in a way that they fix up the mistakes of the other units. This Home health care comes as a potential solution to increasing stress on health-care systems, as well as concerns for medical patients comfort. However, additional distance from the care workers to the patients lead to more challenges, some of which can be addressed with
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
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The fragility of deep neural networks to adversarially-chosen inputs has motivated the need to revisit deep learning algorithms. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. This mechanism can be formulated
Communication-efficient distributed deep learning with merged gradient sparsification on gpus
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Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in large-scale deep learning applications, while it is known that the communication bottleneck limits the scalability of the distributed system. Gradient sparsification is a promising
EE-559 Deep learning 6.1. Benefits of depth
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Page 1. EE-559 Deep learning 6.1. Benefits of depth François Fleuret https://fleuret.org/ee559/ 2 2020 Page 2. Using deeper architectures has been key in improving performance in many applications. For instance image classification: model top-1 err. top-5 err. VGG-16
Deep Learning in Ultrasound Imaging
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In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging
EE-559 Deep learning 13.2. Transformer Networks
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GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes
EE-559 Deep learning 13.1. Attention Mechanisms
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Page 1. EE-559 Deep learning 13.1. Attention Mechanisms François Fleuret https://fleuret.org/ ee559/ 2020 Attention mechanisms aggregate features with an importance score that depends on the feature themselves, not only on their position in the tensor, relax locality
Deep Learning and System Identification⋆
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Deep learning is a topic of considerable interest today. Since it deals with estimating or learning models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this
Comparative Study on Time Series Forecasting using Deep learning Models.
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In univariate and multivariate time series forecasting, there are many proposed traditional models. The problem with traditional models is that they are unable to predict high number of observations. Deep learning models avoid these problems. In this paper, a comparative
EE-559 Deep learning 7.1. Computer vision tasks
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other (tracking in videos, camera pose estimation, body pose estimation, 3d reconstruction, denoising, super-resolution, auto-captioning, synthesis, etc.) François Fleuret EE-559 Deep learning / 8.1. Computer vision tasks MNIST and Fashion-MNIST: 10 classes (digits or pieces
EE-559 Deep learning 7.3. Networks for object detection
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Figure 7: Examples of bounding boxes produced by the regression network, before being combined into final predictions. The examples shown here are at a single scale. Predictions be more optimal at other scales depending on the objects. Here, most of the bounding
EE-559 Deep learning 8.3. Visualizing the processing in the input
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Page 1. EE-559 Deep learning 9.3. Visualizing the processing in the input François Fleuret Visualizing the processing in the input 1 / 22 Page 3. Another Human-centric visual analysis is regarded as one of the most fundamental problems in computer vision, which augments human images in a variety of application fields. Developing solutions for comprehensive human-centric visual applications could have
EE-559 Deep learning 6.6. Using GPUs
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Deep learning ee559/ 2 2020 Page 2. The size of current state-of-the-art networks makes computation a critical issue, in particular for training and optimizing meta-parameters
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
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Background: To control the spread of Corona Virus Disease (COVID-19), screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with
Data Curation with Deep Learning
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Data curation the process of discovering, integrating, and cleaning data is one of the oldest, hardest, yet inevitable data management problems. Despite decades of efforts from both researchers and practitioners, it is still one of the most time consuming and leastThe low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early
EE-559 Deep learning 11.2. Wasserstein GAN
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Page 1. EE-559 Deep learning 11.2. Wasserstein GAN François Fleuret https://fleuret.org/ee559/ 2020 Page 2. Arjovsky et al. (2017) point out that DJS does not account [much] for the metric structure of the space. François Fleuret EE-559 Deep learning / 11.2. Wasserstein GAN
Multimodal Deep Learning Framework for Mental Disorder Recognition
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Current methods for mental disorder recognition mostly depend on clinical interviews and self-reported scores that can be highly subjective. Building an automatic recognition system can help in early detection of symptoms and providing insights into the biological markers for