Web: http://arxiv.org/abs/2201.08477

Jan. 24, 2022, 2:10 a.m. | Qiyu Hu, Shuhan Shi, Yunlong Cai, Guanding Yu

cs.LG updates on arXiv.org arxiv.org

Deep-unfolding neural networks (NNs) have received great attention since they
achieve satisfactory performance with relatively low complexity. Typically,
these deep-unfolding NNs are restricted to a fixed-depth for all inputs.
However, the optimal number of layers required for convergence changes with
different inputs. In this paper, we first develop a framework of deep
deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth
for different inputs, where the trainable parameters of deep-unfolding NN are
learned by DDPG, rather than updated by the stochastic …

arxiv bayesian deep learning

More from arxiv.org / cs.LG updates on arXiv.org

Director, Data Science (Advocacy & Nonprofit)

@ Civis Analytics | Remote

Data Engineer

@ Rappi | [CO] Bogotá

Data Scientist V, Marketplaces Personalization (Remote)

@ ID.me | United States (U.S.)

Product OPs Data Analyst (Flex/Remote)

@ Scaleway | Paris

Big Data Engineer

@ Risk Focus | Riga, Riga, Latvia

Internship Program: Machine Learning Backend

@ Nextail | Remote job