Perceptrons An Introduction To Computational Geometry

By M. Becker. 2016

232 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Environmental drivers of systematicity and generalization in a situated agent (2019, methodology): “This is in keeping with a long history of using simulation for the scientiﬁc analysis of learning machines (Minsky & Papert, 1969; Marcus, 1998).”
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (2015, neutral): “There is a rich history of the use of synthetic tasks in machine learning, from the XOR problem which helped motivate neural networks (Minsky & Papert, 1969; Rumelhart et al., 1985), to circle, spiral and ring datasets that helped motivate some of the most well-known clustering and semi-supervised…”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, background): “However, these deep learning models are difficult to interpret and require many samples to accurately train: for example, recent estimations on Monte Carlo simulated data for MLP estimate a requirement of 50 times the number of adjustable parameters in the network (e.g., number of analytes, number o”
Deep Learning and Its Applications in Biomedicine (2018, background): “However, ANN research stagnated after the 1960s, due to the low capability resulting from its shallow structures and the limited computational capacity of computers at that time [17].”
Deep learning in bioinformatics (2016, methodology): “Some papers have used ‘DNNs’ to encompass all deep learning architectures [7, 8]; however, in this review, we use ‘DNNs’ to refer specifically to multilayer perceptron (MLP) [26], stacked auto-encoder (SAE) [27, 28] and deep belief networks (DBNs) [29, 30], which use perceptrons [42], auto-encoders ”
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions (2018, background): “Historically, Multi-Layer Perceptrons (MLPs) are the first neural networks in the literature [51].”
Emergent Systematic Generalization in a Situated Agent (2019, methodology): “This is in keeping with a long history of using simulation for the scientiﬁc analysis of learning machines (Minsky & Papert, 1969; Marcus, 1998).”
Dense Associative Memory for Pattern Recognition (2016, background): “It can also be thought of as a linear perceptron, and the inability to solve this problem represents the well known statement [17] that linear perceptrons cannot compute XOR without hidden neurons.”