286 citations · 823 across the 37 of their papers we have counts for
9 papers · 1 filter
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Yubei Chen, Zeyu Yun, Yi Ma +2
We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that ach…
Bispectral Neural Networks
Sophia Sanborn, Christian Shewmake, Bruno Olshausen +1
We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the spac…
Computing on Functions Using Randomized Vector Representations
E. Paxon Frady, Denis Kleyko, Christopher J. Kymn +2
Vector space models for symbolic processing that encode symbols by random vectors have been proposed in cognitive science and connectionist communities under the names Vector Symbo…
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing
Cameron Diao, Denis Kleyko, Jan M. Rabaey +1
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored f…
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
Hong-Ye Hu, Dian Wu, Yi-Zhuang You +2
Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse…
Tent: Fully Test-time Adaptation by Entropy Minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu +2
A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own paramet…
Superposition of many models into one
Brian Cheung, Alex Terekhov, Yubei Chen +2
We present a method for storing multiple models within a single set of parameters. Models can coexist in superposition and still be retrieved individually. In experiments with neur…
Discovering Hidden Factors of Variation in Deep Networks
Brian Cheung, Jesse A. Livezey, Arjun K. Bansal +1
Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification. But there has been less exploration in learning…
Efficient Methods for Unsupervised Learning of Probabilistic Models
Jascha Sohl-Dickstein
In this thesis I develop a variety of techniques to train, evaluate, and sample from intractable and high dimensional probabilistic models. Abstract exceeds arXiv space limitations…