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Found 6 papers, 3 papers with code
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari.
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
Recently, an OGD algorithm emerged, employing gradient computation involving prior and most recent examples, a step that effectively reduces algorithmic complexity to $O(T)$, with $T$ being the number of received examples.
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
However, most of those iterative methods are based on the $\ell_1$ norm which requires restrictive applicability conditions and could fail in many cases.
Energy Efficient Training of SNN using Local Zeroth Order Method
To circumvent the problem surrogate method uses a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling.
metric-learn: Metric Learning Algorithms in Python
metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms.
