![]() ![]() explicitly optimizing model parameters for fast learning (optimization-based).learning effective distance metrics (metrics-based).using (cyclic) networks with external or internal memory (model-based).Procedural bias imposes constraints on the ordering of the inductive hypotheses (e.g., preferring smaller hypotheses).Declarative bias specifies the representation of the space of hypotheses, and affects the size of the search space (e.g., represent hypotheses using linear functions only).Meta learning is concerned with two aspects of learning bias. Learning bias must be chosen dynamically.īias refers to the assumptions that influence the choice of explanatory hypotheses and not the notion of bias represented in the bias-variance dilemma.in a previous learning episode on a single dataset, or.Experience is gained by exploiting meta knowledge extracted.The system must include a learning subsystem.Definition Ī proposed definition for a meta learning system combines three requirements: In an open-ended hierarchical meta learning system using genetic programming, better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc. A good analogy to meta-learning, and the inspiration for Jürgen Schmidhuber's early work (1987) and Yoshua Bengio et al.'s work (1991), considers that genetic evolution learns the learning procedure encoded in genes and executed in each individual's brain. Critiques of meta learning approaches bear a strong resemblance to the critique of metaheuristic, a possibly related problem. This poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood.īy using different kinds of metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, select, alter or combine different learning algorithms to effectively solve a given learning problem. A learning algorithm may perform very well in one domain, but not on the next. This means that it will only learn well if the bias matches the learning problem. įlexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn. The well-trained models can be implemented in smartphone apps to improve glycemic control by enabling proactive actions through real-time glucose alerts.Is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. These results indicate that FCNN is a viable and effective approach for predicting BG levels in T1D. In particular, for a dataset including 12 subjects with T1D, FCNN achieved a root mean square error of 18.64☒.60 mg/dL and 31.07☓.62 mg/dL for 30 and 60-minute prediction horizons, respectively, which outperformed all the considered baseline methods with significant improvements. ![]() The proposed framework has been validated on three clinical datasets. The model-agnostic meta-learning is employed to enable fast adaptation for a new T1D subject with limited training data. In particular, an attention-based recurrent neural network is used to learn representations from CGM input and forward a weighted sum of hidden states to an evidential output layer, aiming to compute personalized BG predictions with theoretically supported model confidence. To this end, we propose a novel deep learning framework, Fast-adaptive and Confident Neural Network (FCNN), to meet these clinical challenges. However, there are several challenges that prevent the widespread implementation of deep learning algorithms in actual clinical settings, including unclear prediction confidence and limited training data for new T1D subjects. The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) prediction in type 1 diabetes (T1D) with superior performance. ![]()
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