Adversarial machine learningAdversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. To understand, note that most machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).
Integrative psychotherapyIntegrative psychotherapy is the integration of elements from different schools of psychotherapy in the treatment of a client. Integrative psychotherapy may also refer to the psychotherapeutic process of integrating the personality: uniting the "affective, cognitive, behavioral, and physiological systems within a person". Initially, Sigmund Freud developed a talking cure called psychoanalysis; then he wrote about his therapy and popularized psychoanalysis. After Freud, many different disciplines splintered off.
Hyperparameter optimizationIn machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns.
Clinical psychologyClinical psychology is an integration of human science, behavioral science, theory, and clinical knowledge for the purpose of understanding, preventing, and relieving psychologically-based distress or dysfunction and to promote subjective well-being and personal development. Central to its practice are psychological assessment, clinical formulation, and psychotherapy, although clinical psychologists also engage in research, teaching, consultation, forensic testimony, and program development and administration.
Hyperparameter (machine learning)In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training. Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Allied health professionsAllied health professions are health care professions that provide a range of diagnostic, technical, therapeutic, and support services in connection with health care. Their services are allied with and support the work of a number of other professions not considered allied health professions, such as medicine, nursing, dentistry, optometry, pharmacy, and others listed below as "excluded professions".
Neural architecture searchNeural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used: The search space defines the type(s) of ANN that can be designed and optimized. The search strategy defines the approach used to explore the search space.
Rural flightRural flight (also known as rural-to-urban migration or rural exodus) is the migratory pattern of people from rural areas into urban areas. It is urbanization seen from the rural perspective. In industrializing economies like Britain in the eighteenth century or East Asia in the twentieth century, it can occur following the industrialization of primary industries such as agriculture, mining, fishing, and forestry—when fewer people are needed to bring the same amount of output to market—and related secondary industries (refining and processing) are consolidated.
Evolutionary computationIn computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated.
SystemA system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and is expressed in its functioning. Systems are the subjects of study of systems theory and other systems sciences. Systems have several common properties and characteristics, including structure, function(s), behavior and interconnectivity.
Speeded up robust featuresIn computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, , classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT.
