Fetching the paper…
Reading the bibliography…
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Use of genetic programming for the search of a new learning rule for neural networks
Samy Bengio, Yoshua Bengio, and Jocelyn Cloutier · 1994
Earlier work this paper cites.
Genetic programming as a means for programming computers by natural selection
John R Koza · 1994
Earlier work this paper cites.
Evolution and design of distributed learning rules
Thomas Philip Runarsson and Magnus Thor Jonsson · 2000
Earlier work this paper cites.
Theoretical interpretation of learned step size in deep-unfolded gradient descent
Satoshi Takabe and Tadashi Wadayama · 2001
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
Earlier work this paper cites.
On improving genetic programming for symbolic regression
Steven Gustafson, Edmund K Burke, and Natalio Krasnogor · 2005
Earlier work this paper cites.
Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
Earlier work this paper cites.
Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Earlier work this paper cites.
The evolution of a generalized neural learning rule
Jeff Orchard and Lin Wang · 2016
Earlier work this paper cites.
Large data throughput optimization model with full c order model parallel flow number prediction optical domain
Hao Yang, Jianan Zhao, Wenqing Zheng, and Jianguo Yu · 2016
Earlier work this paper cites.
Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le · 2017
Earlier work this paper cites.
Amp-inspired deep networks for sparse linear inverse problems
Mark Borgerding, Philip Schniter, and Sundeep Rangan · 2017
Earlier work this paper cites.
Learning to learn without gradient descent by gradient descent
Yutian Chen, Matthew W Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P Lillicrap, Matt Botvinick, and Nando Freitas · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Earlier work this paper cites.
Learning gradient descent: Better generalization and longer horizons
Kaifeng Lv, Shunhua Jiang, and Jian Li · 2017
Earlier work this paper cites.
Understanding the learned iterative soft thresholding algorithm with matrix factorization
Thomas Moreau and Joan Bruna · 2017
Cited alongside, same era.
Unbiasing truncated backpropagation through time
Corentin Tallec and Yann Ollivier · 2017
Cited alongside, same era.
Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Theoretical linear convergence of unfolded ista and its practical weights and thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang, and Wotao Yin · 2018
Cited alongside, same era.
Benchmarking large-scale graph training over effectiveness and efficiency
Keyu Duan, Zirui Liu, Wenqing Zheng, Peihao Wang, Kaixiong Zhou, Tianlong Chen, Zhangyang Wang, and Xia Hu · 2018
Cited alongside, same era.
Blind image blur assessment based on markov-constrained fcm and blur entropy
Yaqian Xu, Wenqing Zheng, Jingchen Qi, and Qi Li · 2019
Later among the works it cites.
Joint position, orientation and channel estimation in hybrid mmwave mimo systems
Wenqing Zheng and Nuria González-Prelcic · 2019
Later among the works it cites.
Pysr: Fast & parallelized symbolic regression in python/julia, September 2020
Miles Cranmer · 2020
Later among the works it cites.
Discovering symbolic models from deep learning with inductive biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho · 2020
Later among the works it cites.
Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Dong Liang, Jing Cheng, Ziwen Ke, and Leslie Ying · 2020
Later among the works it cites.
Reverse engineering learned optimizers reveals known and novel mechanisms
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tradeoffs between convergence speed and reconstruction accuracy in inverse problems
Raja Giryes, Yonina C. Eldar, Alex M. Bronstein, and Guillermo Sapiro · 2018
Cited alongside, same era.
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C Daniel Freeman, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Understanding short-horizon bias in stochastic meta-optimization
Yuhuai Wu, Mengye Ren, Renjie Liao, and Roger Grosse · 2018
Cited alongside, same era.
Ista-net: Interpretable optimization-inspired deep network for image compressive sensing
Jian Zhang and Bernard Ghanem · 2018
Cited alongside, same era.
Deep unfolding for communications systems: A survey and some new directions
Alexios Balatsoukas-Stimming and Christoph Studer · 2019
Cited alongside, same era.
Learning to optimize in swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2019
Cited alongside, same era.
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, and Jascha Sohl-Dickstein · 2020
Later among the works it cites.
Automl-zero: Evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David So, and Quoc Le · 2020
Later among the works it cites.
Glad: Learning sparse graph recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, and Le Song · 2020
Later among the works it cites.
L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2020
Later among the works it cites.
5g v2x communication at millimeter wave: Rate maps and use cases
W Zheng, Anum Ali, N González-Prelcic, RW Heath, Aldebaro Klautau, and E Moradi Pari · 2020
Later among the works it cites.
A generalizable approach to learning optimizers
Diogo Almeida, Clemens Winter, Jie Tang, and Wojciech Zaremba · 2021
Later among the works it cites.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Later among the works it cites.
Meta-learning in neural networks: A survey
Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J Storkey · 2021
Later among the works it cites.
Scalable perception-action-communication loops with convolutional and graph neural networks
Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Atlas Wang, Alejandro R Ribeiro, and Brian M Sadler · 2021
Later among the works it cites.
Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond
Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou · 2021
Later among the works it cites.
Learning a minimax optimizer: A pilot study
Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, and Zhangyang Wang · 2021
Later among the works it cites.
End-to-end sequential sampling and reconstruction for mr imaging
Tianwei Yin, Zihui Wu, He Sun, Adrian V Dalca, Yisong Yue, and Katherine L Bouman · 2021
Later among the works it cites.
Practical tradeoffs between memory, compute, and performance in learned optimizers
Luke Metz, C Daniel Freeman, James Harrison, Niru Maheswaranathan, and Jascha Sohl-Dickstein · 2022
Closest in time.
Peihao Wang, Wenqing Zheng, Tianlong Chen, and Zhangyang Wang · 2022
Closest in time.