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The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML , it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held every December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. [1]
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification , knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Machine learning is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the choice of representation, localist logical rather than distributed, and the non-use of gradient-based learning algorithms).
Attention in Machine Learning is a technique that mimics cognitive attention. In the context of learning on graphs, the attention coefficient α u v {\displaystyle \alpha _{uv}} measures how important is node u ∈ V {\displaystyle u\in V} to node v ∈ V {\displaystyle v\in V} .