Perceptrons: An Introduction to Computational Geometry

By Marvin Minsky, S. Papert. 1969

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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…”
Neural Approaches to Conversational AI (2018, neutral): “incapable of learning even simple functions such as the logical XOR, so subsequent work explored the use of “deep” architectures, which added hidden layers between input and output (Rosenblatt, 1962; Minsky and Papert, 1969), a form of neural network that is commonly called the multi-layer perceptro”
Advanced Structured Prediction (2014, background): “The Perceptron Cycling Theorem is a classic result due to (Minsky and Papert, 1969; Block and Levin, 1970), which states: Theorem 1.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “By the end of the 1960s, Minsky and Papertkey’s Perceptrons book [315] drew the limitations of perceptrons-based NN through mathematical analysis, marking a historical turn in AI and ML in particular, and significantly reducing the research interest for this area over the next several years [397].”
A comparison of natural and artificial intelligence (1975, background): “Simple perceptrons, perhaps the best known class of adaptive networks, have been shown to be of very limited capability (Minsky and Papert, 1969).”
A Theory of Program Size Formally Identical to Information Theory (1975, background): “See [17–32] for other applications of information/entropy concepts.”