Connectionism | EPFL Graph Search

Connectionism (coined by Edward Thorndike in the 1930s) is a name of an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks. Connectionism has had many 'waves' along the time since its beginnings. The first wave appeared in the 1950s with Warren Sturgis McCulloch and Walter Pitts both focusing on comprehending neural circuitry through a formal and mathematical approach, and Frank Rosenblatt who published the 1958 book “The Perceptron: A Probabilistic Model For Information Storage and Organization in the Brain” in Psychological Review, while working at the Cornell Aeronautical Laboratory. The first wave ended with the 1969 book about the limitations of the original perceptron idea, written by Marvin Minsky and Papert, which contributed to discouraging major funding agencies in the US from investing in connectionist research. With a few noteworthy deviations, the majority of connectionist research entered a period of inactivity until the mid-1980s. The second wave began in the late 1980s, following the 1987 book about Parallel Distributed Processing by James L. McClelland, David E. Rumelhart et al., which introduced a couple of improvements to the simple perceptron idea, such as intermediate processors (known as "hidden layers" now) alongside input and output units and used sigmoid activation function instead of the old 'all-or-nothing' function. Their work has, in turn, built upon that of John Hopfield, who was a key figure investigating the mathematical characteristics of sigmoid activation functions. From the late 1980s to the mid-1990s, connectionism took on an almost revolutionary tone when Schneider, Terence Horgan and Tienson posed the question of whether connectionism represented a fundamental shift in psychology and GOFAI. Some advantages of the second wave connectionist approach included its applicability to a broad array of functions, structural approximation to biological neurons, low requirements for innate structure, and capacity for graceful degradation.

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

Alexandre Massoud Alahi, Yuejiang Liu

Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In t ...

2023