You can refer to the documentation if you want to learn more. Through this article you've learn about: I hope you can gain basic understanding about confusion matrix and the important metrics for classification task. Remember, never stop to learn & stay awesome!

Hi Folks. In all my previous blogs, I’ve talked about technical stuff you can do on the different operating systems, IDEs, etc. Today I’m going to explain yet another technical topic for which you…

Continual learning denotes machine learning methods which can adapt to new environments while retaining and reusing knowledge gained from past experiences. Such methods address two issues encountered by models in non-stationary environments: ungeneralisability to new data, and the catastrophic forgetting of previous knowledge when retrained. This is a pervasive problem in clinical settings where patient data exhibits covariate shift not only between populations, but also continuously over time. However, while continual learning methods have seen nascent success in the imaging domain, they have been little applied to the multi-variate sequential data characteristic of critical care patient recordings. Here we evaluate a variety of continual learning methods on longitudinal ICU data in a series of representative healthcare scenarios. We find that while several methods mitigate short-term forgetting, domain shift remains a challenging problem over large series of tasks, with only replay based methods achieving stable long-term performance.

Much of the success of modern machine learning has been measured by improvements in accuracy for various classification tasks. Across domains as diverse as image classification and text translation, machine learning models are achieving incredible levels of accuracy; in some cases, they have outperformed humans in visual recognition tasks (Ewerth et al., 2017). However, accuracy is an aggregate statistic that often obscures the underlying structure of mistaken predictions. Oakden-Rayner et al. (2020) recently raised this concern in the context of medical image analysis. Consider the problem of diagnosing a image as being indicative of lung cancer or not.

In this special issue we are reprinting our top stories of the past year. This article first appeared online in our "Mind" issue in March, 2021. One autumn afternoon in the bowels of UC Berkeley's Li Ka Shing Center, I was looking at my brain. I had just spent 10 minutes inside the 3 Tesla MRI scanner, the technical name for a very expensive, very high maintenance, very magnetic brain camera. Lying on my back inside the narrow tube, I had swallowed my claustrophobia and let myself be enveloped in darkness and a cacophony of foghorn-like bleats. At the time I was a research intern at UC Berkeley's Neuroeconomics Lab. That was the first time I saw my own brain from an MRI scan. It was a grayscale, 3-D reconstruction floating on the black background of a computer screen. As an undergraduate who studied neuroscience, I was enraptured. There is nothing quite like a young scientist's first encounter with an imaging technology that renders the hitherto invisible visible--magnetic resonance imaging took my breath away.

As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.

In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on. Various representations of code in terms of tokens, syntax trees, dependency graphs, code navigation paths, or a combination of their variants have been proposed, however, existing vanilla learning techniques have a major limitation in robustness, i.e., it is easy for the models to make incorrect predictions when the inputs are altered in a subtle way. To enhance the robustness, existing approaches focus on recognizing adversarial samples rather than on the valid samples that fall outside a given distribution, which we refer to as out-of-distribution (OOD) samples. Recognizing such OOD samples is the novel problem investigated in this paper. To this end, we propose to first augment the in=distribution datasets with out-of-distribution samples such that, when trained together, they will enhance the model's robustness. We propose the use of an energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD detection and adversarial samples detection, our evaluation results demonstrate a greater robustness for existing source code models to become more accurate at recognizing OOD data while being more resistant to adversarial attacks at the same time. Furthermore, the proposed energy-bounded score outperforms all existing OOD detection scores by a large margin, including the softmax confidence score, the Mahalanobis score, and ODIN.

This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics.

The Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. (WHO, 2020). While most persons with COVID-19 recover and return to normal health, some patients can have symptoms that can last for weeks or even months after recovery from acute illness.

Researchers at the University of Exeter, led by Professor David Lewellin, who published the study in the American medical journal JAMA Network Open, used data from 15.307 people with a mean age of 72 years and memory problems (of whom 1.568 diagnosed with Alzheimer's or another form of dementia within the next two years) tothe new machine learning algorithm, in order to recognize the precursor symptoms of dementia. The "smart" system has learned to detect hidden clues in the data, which the human eye, even a neurologist or other specialist, can not recognize. In addition, 130 diagnoses (8% of the total) turned out to be incorrect, as they were later overturned. Of these false positive cases of dementia, the algorithm was able to correctly diagnose that 84% actually had nothing to do with dementia. Therefore the system can not only distinguish who may develop neurodegeneration of the brain in the future, but also improve the accuracy of the diagnosis, so that someone who is not is not diagnosed as a patient.