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Found 7 papers, 2 papers with code
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.
Learning the Positions in CountSketch
Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned.
Overview of FPGA deep learning acceleration based on convolutional neural network
In recent years, deep learning has become more and more mature, and as a commonly used algorithm in deep learning, convolutional neural networks have been widely used in various visual tasks.
A framework for learned sparse sketches
In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
Safe Control Under Input Limits with Neural Control Barrier Functions
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations.
Learning the Positions in CountSketch
We fix this issue and propose approaches for learning a sketching matrix for both low-rank approximation and Hessian approximation for second order optimization.
Synthesis and verification of robust-adaptive safe controllers
Our raCBFs are currently the most effective way to guarantee safety for uncertain systems, achieving 100% safety and up to 55% performance improvement over a robust baseline.
