Unsupervised Learning

By Geoffrey E. Hinton, Terrence J. Sejnowski. 2018

2,067 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Representation & Self-Supervised Learning

Citations in the corpus

Experience Grounds Language (2020, background): “…tension of modeling symbols and distributed representations was nicely articulated by Smolensky (1990), and alternative representations (Kohonen, 1984; Hinton et al., 1986; Barlow, 1989) and approaches to structure and composition (Erk and Padó, 2008; Socher et al., 2012) span decades of research.”
Data-Efficient Image Recognition with Contrastive Predictive Coding (2019, background): “structure of the world in an unsupervised manner (Barlow, 1989; Hinton et al., 1999; LeCun et al., 2015).”
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2016, background): “This work falls in the area of representation/feature learning, which is an unsupervised learning problem [3].”
Local Aggregation for Unsupervised Learning of Visual Embeddings (2019, background): “Unsupervised learning methods span a very broad spectrum of approaches going back to the roots of artificial neural networks [57, 43, 3, 62, 30, 45, 31, 33, 27], and are too numerous to fully review here.”
Learning to See by Moving (2015, background): “This hypothesis inspired research in fully unsupervised learning from visual imagery and led to popular algorithms such as ICA [5, 20], autoen-coders [24, 9], sparse coding [32], restricted boltzmann machines [27, 37] and others [6].”
Deep learning in neural networks: An overview (2014, background): “Ideally, given an ensemble of input patterns, redundancy reduction through a deep NN will create a factorial code (a code with statistically independent components) of the ensemble (Barlow et al., 1989; Barlow, 1989), to disentangle the unknown factors of variation (e.”
Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019, background): “Learning good representations of high-dimensional sensory data is of fundamental importance to Artificial Intelligence [4, 3, 6, 49, 7, 69, 67, 50, 59, 73].”
DeepTox: Toxicity Prediction using Deep Learning (2016, neutral): “Images adopted from Lee et al. (2011) with permission from the authors.”
One Big Net For Everything (2018, background): “…of q special output units and train them through unsupervised learning [62] to produce for t ≤ t T a vector code ( t ) ∈ R q that represents an ideal factorial code [2] of the observed history so far, or that encodes the data in related ways generally considered useful, e.g., [23, 28, 81, 68, 21].”
A Survey of Inductive Biases for Factorial Representation-Learning (2016, background): “Horace Barlow argued for the biological and computational utility of factorial representations [6, 7].”
Neural Predictive Belief Representations (2018, methodology): “Modern supervised learning (Hastie et al., 2009a) and reinforcement learning (RL Sutton & Barto, 1998) methods have been applied successfully to many challenging applications (He et al., 2016; Sutskever et al., 2014; Mnih et al., 2015; Silver et al., 2016).”
Machine Learning in Aerodynamic Shape Optimization (2022, background): “Unsupervised learning algorithms analyze the data pattern and automatically set up learning rules [171, 172].”
Hierarchical Models in the Brain (2008, neutral): “See [33] for a comprehensive review of unsupervised learning for many of the models in this section.”
Discovering hierarchical speech features using convolutional non-negative matrix factorization (2003, background): “Unsupervised learning techniques [1] try to discover the structure underlying a dataset.”
Unsupervised Object Keypoint Learning using Local Spatial Predictability (2020, background): “For example, a representation that captures only information about relevant aspects of the world makes it easier to learn downstream tasks efficiently (Barlow, 1989; Bengio et al., 2013).”
Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning (2001, background): “Another way of thinking about the beneﬁts of inhibitory competition comes from the idea that, given the general structure of the natural environment, sparse distributed representations (i.e., with relatively few units active at a time) are particularly useful (Barlow, 1989; Field, 1994).”
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, methodology): “Unsupervised learning techniques include K-means clustering, spectral clustering, principal component analysis (PCA), singular value decomposition (SVD), and so on [38].”