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Found 9 papers, 2 papers with code
Symmetric Single Index Learning
Learning this model with SGD is relatively well-understood, whereby the so-called information exponent of the link function governs a polynomial sample complexity rate.
On Single Index Models beyond Gaussian Data
Sparse high-dimensional functions have arisen as a rich framework to study the behavior of gradient-descent methods using shallow neural networks, showcasing their ability to perform feature learning beyond linear models.
Towards Antisymmetric Neural Ansatz Separation
We study separations between two fundamental models (or \emph{Ans\"atze}) of antisymmetric functions, that is, functions $f$ of the form $f(x_{\sigma(1)}, \ldots, x_{\sigma(N)}) = \text{sign}(\sigma)f(x_1, \ldots, x_N)$, where $\sigma$ is any permutation.
Exponential Separations in Symmetric Neural Networks
In this work we demonstrate a novel separation between symmetric neural network architectures.
Neural Algorithms for Graph Navigation
The application of deep reinforcement learning (RL) to graph learning and meta-learning admits challenges from both topics.
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Symmetric functions, which take as input an unordered, fixed-size set, are known to be universally representable by neural networks that enforce permutation invariance.
Provably Efficient Third-Person Imitation from Offline Observation
Domain adaptation in imitation learning represents an essential step towards improving generalizability.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipelines.
Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data.
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