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Found 5 papers, 2 papers with code
Machine Learning-Powered Course Allocation
We study the course allocation problem, where universities assign course schedules to students.
Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?
Our first main result transcribes this "structured" approximation problem into a universality problem.
Universal Approximation Under Constraints is Possible with Transformers
Many practical problems need the output of a machine learning model to satisfy a set of constraints, $K$.
Learning Sub-Patterns in Piecewise Continuous Functions
Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable in their parameters; however, this implies that the neural network's activation function must exhibit a degree of continuity which limits the neural network model's uniform approximation capacity to continuous functions.
A Canonical Transform for Strengthening the Local $L^p$-Type Universal Approximation Property
The transformed model class, denoted by $\mathscr{F}\text{-tope}$, is shown to be dense in $L^p_{\mu,\text{strict}}(\mathbb{R}^d,\mathbb{R}^D)$ which is a topological space whose elements are locally $p$-integrable functions and whose topology is much finer than usual norm topology on $L^p_{\mu}(\mathbb{R}^d,\mathbb{R}^D)$; here $\mu$ is any suitable $\sigma$-finite Borel measure $\mu$ on $\mathbb{R}^d$.
