![]() The competition is with code submission, fully blind-tested on the CodaLab challenge platform. By using mother datasets from these practical domains, we aim to maximize the humanitarian and societal impact. We carve such tasks from various “mother datasets” selected from diverse domains, such as healthcare, ecology, biology, manufacturing, and others. The goal is to meta-learn a good model that can quickly learn tasks from a variety of domains, with any number of classes also called “ways” (within the range 2-20) and any number of training examples per class also called “shots” (within the range 1-20). It focuses on “cross-domain meta-learning” for few-shot image classification using a novel “any-way” and “any-shot” setting. This challenge comes with an amazing on-line tut orial guiding you step-by-step and a white paper describing the challenge and baseline results. The winners of the first round have open-sourced their code. Having participated in the first round is NOT a prerequisite. In this new round, we propose an enlarged and more challenging meta-dataset. The first round of this competition was previously organized for WCCI 2022, please see the results on our website. Hence, meta-learners are expected to learn the exploration-exploitation trade-offs between exploiting an already tried good candidate algorithm and exploring new candidate algorithms. Meta-learners must “pay” a cost emulating computational time for revealing their next values. We offer pre-computed learning curves as a function of time, to facilitate benchmarking. Furthermore, we want to study the potential benefit of learned policies, as opposed to applying hand-crafted black-box optimization methods. We are interested in meta-learning strategies that leverage information on partially trained algorithms, hence reducing the cost of training them to convergence. Analysis of past ML challenges revealed that top-ranking methods often involve switching between algorithms during training. A learning curve evaluates an algorithm's incremental performance improvements, as a function of training time, number of iterations, and/or number of examples. The main goal of this competition is to push the state-of-the-art in meta-learning from learning curves, an important sub-problem in meta-learning. Meta-Album (Thursday 1st of December) : Ĭross-Domain MetaDL Competition (Tuesday 6th of December): Ĭontact us, if you want to join the organizing team. 2022, Grenoble, France, associated with ECML/PKDD 2022 Workshop (Meta-)Knowledge Transfer/Communication in Different Systems. Approaches include learning from algorithm evaluations, from task properties (or meta-features), and from prior models.įollowing the AutoDL 2019-2020 challenge series and past meta-learning challenges and benchmarks we have organized, including NeurIPS'21 (read our PAPER ) we are organizing three competitions in 2022:ġst Round of Meta-learning from learning curves ( ENDED will be presented at WCCI 2022).Ģnd Round of Meta-learning from learning curves ( ENDED, accepted to AutoML-Conf 2022 ).Ĭross-domain meta-learning ( ENDED, accepted to NeurIPS'22). Meta-learning promises to leverage the experience gained on previous tasks to train models faster, with fewer examples, and possibly better performance. We were able to determine the best algorithm or a combination of algorithms for specific datasets based on features extracted from them.Machine learning has solved with success many mono-task problems, but at the expense of long wasteful training times. With our new techniques, we were able to implement a tool for generating of many causal models and sampling many datasets from each model. Three standard structure learning algorithms were run on each of the generated datasets to discover the underlying causal networks and their performance was evaluated. Several Bayesian networks in literature were manipulated, sampled to generate thousands of datasets, and specific features were extracted from each for meta-learning. Meta-learning refers to learning about learning algorithms where different kinds of meta-data, such as properties of the learning problem, performance measures of different algorithms and patterns previously derived from the data are used to select the best or combine different learning algorithms to effectively solve a given learning problem. During this research, we proposed a novel meta-learning approach to this problem. Selection of the best causal discovery algorithm for any new dataset is a difficult and time consuming process as it requires a researcher to have prior knowledge about a number of existing standard structure learning algorithms.
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