In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive non-occurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a novel definition of confusion matrix and skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce a deep ensemble learning procedure for binary classification, in which the probabilistic outcomes of a neural network are clustered via optimization of these value-weighted skill scores. We finally show the performances of this approach in the case of three applications concerned with pollution, space weather and stock prize forecasting.

In medical imaging, chromosome straightening plays a significant role in the pathological study of chromosomes and in the development of cytogenetic maps. Whereas different approaches exist for the straightening task, they are mostly geometric algorithms whose outputs are characterized by jagged edges or fragments with discontinued banding patterns. To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details. In addition, to avoid the pitfall of deficient input chromosomes, we construct an augmented dataset using only one single curved chromosome image for training models. Based on this framework, we apply two popular image-to-image translation architectures, U-shape networks and conditional generative adversarial networks, to assess its efficacy. Experiments on a dataset comprising of 642 real-world chromosomes demonstrate the superiority of our framework as compared to the geometric method in straightening performance by rendering realistic and continued chromosome details. Furthermore, our straightened results improve the chromosome classification, achieving 0.98%-1.39% in mean accuracy.

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks. What is worse, the trained diagnosis system would be fixed once deployed but collecting training data that covers enough diseases is infeasible, which inspires us to develop a lifelong learning diagnosis system. In this work, we propose to adopt attention to combine medical entities and context, embedding episodic memory and consolidation to retain knowledge, such that the learned model is capable of adapting to sequential disease-diagnosis tasks. Moreover, we establish a new benchmark, named Jarvis-40, which contains clinical notes collected from various hospitals. Experiments show that the proposed method can achieve state-of-the-art performance on the proposed benchmark. Code is available at https://github.com/yifyang/

This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new--and much smaller--places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT). The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

This article is a part of a series that I'm writing, and where I will try to address the topic of using Deep Learning in NLP. First of all, I was writing an article for an example of text classification using a perceptron, but I was thinking that will be better to review some basics before, as activation and loss functions. Loss function also called the objective function, is one of the main bricks in supervised machine learning algorithm which is based on labeled data. A loss function guides the training algorithm to update parameters in the right way. In a much simple definition, a loss function takes a truth (y) and a prediction (ŷ) as input and gives a score of real value number. This value indicates how much the prediction is close to the truth.

In the following sections, I will introduce various articles and papers not only on the above contents but also on the following five topics. Zero-Shot Text-to-Image Generation They proposed DALL-E, which generates images from text with zero-shot. First, as in VQVAE, they compress the image to 32x32 using an encoder, re-select a representation from the codebook that is close to each grid representation, and learn discrete VAE to generate images from it. Next, using the paired data of image and text, they train an autoregressive model to generate "image tokens" using the text as input and the 8192 expressions in the codebook as vocabulary.

Motivated by prior work connecting the geometry of the loss landscape and generalization, we introduce a novel, effective procedure for instead simulta- neously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighbor- hoods having uniformly low loss; this formulation results in a min-max optimiza- tion problem on which gradient descent can be performed efficiently. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets[1] In Deep Learning we use optimization algorithms such as SGD/Adam to achieve convergence in our model, which leads to finding the global minima, i.e a point where the loss of the training dataset is low. But several kinds of research such as Zhang et al have shown, many networks can easily memorize the training data and have the capacity to readily overfit, To prevent this problem and add more generalization, Researchers at Google have published a new paper called Sharpness Awareness Minimization which provides State of the Art results on CIFAR10 and other datasets. In this article, we will look at why SAM can achieve better generalization and how we can implement SAM in Pytorch.

The reputation and bottom line of a company can be adversely affected if defective products are released. If a defect is not detected, and the flawed product is not removed early in the production process, the damage can be costly – and the higher the unit value, the higher those costs will be. And worst of all, dissatisfied customers can demand returns. To mitigate these costs, many manufacturers install cameras to monitor their products as they move along their production lines. However, the data obtained may not always be useful – or more appropriately said, the data is useful, but existing machine vision systems may not be able to accurately assess it at full production speeds.

Researchers at Google Brain have open-sourced the Switch Transformer, a natural-language processing (NLP) AI model. The model scales up to 1.6T parameters and improves training time up to 7x compared to the T5 NLP model, with comparable accuracy. The team described the model in a paper published on arXiv. The Switch Transformer uses a mixture-of-experts (MoE) paradigm to combine several Transformer attention blocks. Because only a subset of the model is used to process a given input, the number of model parameters can be increased while holding computational cost steady.

You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. How this course will help you?