Unsupervised learning of morphological forests od Luo, Jiaming, (Scientist in Electrical Engineering and Computer Science) Massachusetts Institute of Technology

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Unsupervised learning by program synthesis od Ellis, Kevin M., Solar Lezama, Armando, Tenenbaum, Joshua B

“…We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis.We apply our techniques to both a visual learning domain and a language learning problem,showing that our algorithm can learn many visual concepts from only a few examplesand that it can recover some English inflectional morphology.Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures,and a technique for applying program synthesis tools to noisy data.…”
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Unsupervised learning of invariant representations od Anselmi, Fabio, Leibo, Joel Z, Rosasco, Lorenzo, Mutch, James Vincent, Tacchetti, Andrea, Poggio, Tomaso A

“…The theory offers novel unsupervised learning algorithms for “deep” architectures for image and speech recognition. …”
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Unsupervised Learning of Morphological Forests od Luo, Jiaming, Narasimhan, Karthik, Barzilay, Regina

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Unsupervised learning with diffusion models od Wang, Jiankun

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Deep unsupervised learning from speech od Drexler, Jennifer Fox

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Unsupervised Learning and Recognition of Physical Activity Plans od Dong, Shuonan

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Unsupervised learning of lexical subclasses from phonotactics od Morita, Takashi, Ph. D. Massachusetts Institute of Technology

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Crowd-Centric Counting via Unsupervised Learning od Morselli, Flavio, Bartoletti, Stefania, Mazuelas, Santiago, Win, Moe Z., Conti, Andrea

“…This paper proposes a new technique for crowd-centric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. …”
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Unsupervised learning of spoken language with visual context od Harwath, David, Torralba, Antonio, Glass, James R.

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Toward an artificial intelligence physicist for unsupervised learning od Wu, Tailin, Tegmark, Max

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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration od Dalca, Adrian V., Balakrishnan, Guha, Guttag, John, Sabuncu, Mert R.

“…In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). …”
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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration od Dalca, Adrian Vasile, Balakrishnan, Guha, Guttag, John V, Sabunca, Mert R.

“…In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). …”
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Toward Faster Methods in Bayesian Unsupervised Learning od Nguyen, Tin D.

“…The application of Bayesian unsupervised learning faces three computational challenges. …”
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Unsupervised Learning for Generative Scene Editing and Motion od Fang, David S.

“…Unsupervised learning for images and videos is important for many applications in computer vision. …”
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Unsupervised learning and recognition of physical activity plans od Dong, Shuonan

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Nonparametric Bayesian methods for supervised and unsupervised learning od Mansinghka, Vikash Kumar

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Optimal Unsupervised Learning in Feedforward Neural Networks od Sanger, Terence D.

“…The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. …”
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Structured sparse representations for supervised and unsupervised learning od Zeng, Yijie

“…The goal of this thesis is to explore inherent sparse structures in machine learning problems, and design effective algorithms that utilize the derived sparse representations for solving supervised and unsupervised learning tasks. The first part of the thesis investigates the topic of dictionary learning. …”
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Unsupervised learning of object keypoints for perception and control od Kulkarni, T, Gupta, A, Ionescu, C, Borgeaud, S, Reynolds, M, Zisserman, A, Mnih, V