Fetching the paper…
Reading the bibliography…
Not all neural network architectures are created equal, some perform much better than others for certain tasks.
D. R. So, C. Liang, and Q. V. Le · 1901
Earlier work this paper cites.
Random search and reproducibility for neural architecture search
L. Li and A. Talwalkar · 1902
Earlier work this paper cites.
Evaluating the search phase of neural architecture search
C. Sciuto, K. Yu, M. Jaggi, C. Musat, and M. Salzmann · 1902
Earlier work this paper cites.
Deconstructing lottery tickets: Zeros, signs, and the supermask
H. Zhou, J. Lan, R. Liu, and J. Yosinski · 1905
Earlier work this paper cites.
Intelligent machinery
A. M. Turing · 1948
Earlier work this paper cites.
A formal theory of inductive inference. part i
R. J. Solomonoff · 1964
Earlier work this paper cites.
Three approaches to the quantitative definition of information
A. N. Kolmogorov · 1965
Earlier work this paper cites.
Modeling by shortest data description
J. Rissanen · 1978
Earlier work this paper cites.
The structure of the nervous system of the nematode caenorhabditis elegans
J. G. White, E. Southgate, J. N. Thomson, and S. Brenner · 1986
Earlier work this paper cites.
When learning guides evolution
J. M. Smith · 1987
Earlier work this paper cites.
Learning causes synaptogenesis, whereas motor activity causes angiogenesis, in cerebellar cortex of adult rats
J. E. Black, K. R. Isaacs, B. J. Anderson, A. A. Alcantara, and W. T. Greenough · 1990
Earlier work this paper cites.
Meiosis networks
S. J. Hanson · 1990
Earlier work this paper cites.
A stochastic version of the delta rule
S. J. Hanson · 1990
Earlier work this paper cites.
Designing application-specific neural networks using the genetic algorithm
S. A. Harp, T. Samad, and A. Guha · 1990
Earlier work this paper cites.
Morphometric study of human cerebral cortex development
P. R. Huttenlocher · 1990
Earlier work this paper cites.
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
Interactions between learning and evolution
D. Ackley and M. Littman · 1991
Earlier work this paper cites.
Curious model-building control systems
J. Schmidhuber · 1991
Earlier work this paper cites.
Designing application-specific neural networks using the structured genetic algorithm
D. Dasgupta and D. R. McGregor · 1992
Earlier work this paper cites.
Using marker-based genetic encoding of neural networks to evolve finite-state behaviour
B. Fullmer and R. Miikkulainen · 1992
Earlier work this paper cites.
Bayesian interpolation
D. J. MacKay · 1992
Earlier work this paper cites.
Simplifying neural networks by soft weight-sharing
S. J. Nowlan and G. E. Hinton · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Evolving feedforward neural networks
H. Braun and J. Weisbrod · 1993
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon
B. Hassibi and D. G. Stork · 1993
Earlier work this paper cites.
Keeping neural networks simple by minimizing the description length of the weights
G. Hinton and D. Van Camp · 1993
Earlier work this paper cites.
Representation and evolution of neural networks
M. Mandischer · 1993
Earlier work this paper cites.
Evolving optimal neural networks using genetic algorithms with occam’s razor
B.-T. Zhang and H. Muhlenbein · 1993
Earlier work this paper cites.
An evolutionary algorithm that constructs recurrent neural networks
P. J. Angeline, G. M. Saunders, and J. B. Pollack · 1994
Earlier work this paper cites.
Delta-gann: A new approach to training neural networks using genetic algorithms
R. Krishnan and V. B. Ciesielski · 1994
Earlier work this paper cites.
Genetic evolution of the topology and weight distribution of neural networks
V. Maniezzo · 1994
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Y. LeCun and Y. Bengio · 1995
Earlier work this paper cites.
Morphological correlates of locomotor performance in hatchling amblyrhynchus cristatus
D. B. Miles, L. A. Fitzgerald, and H. L. Snell · 1995
Earlier work this paper cites.
Genetic algorithms, tournament selection, and the effects of noise
B. L. Miller and D. E. Goldberg · 1995
Earlier work this paper cites.
A comparison between cellular encoding and direct encoding for genetic neural networks
F. Gruau, D. Whitley, and L. Pyeatt · 1996
Earlier work this paper cites.
How learning can guide evolution
G. E. Hinton and S. J. Nowlan · 1996
Earlier work this paper cites.
Evolutionary ordered neural network with a linked-list encoding scheme
C.-H. Lee and J.-H. Kim · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Connectionist theory refinement: Genetically searching the space of network topologies
D. W. Opitz and J. W. Shavlik · 1997
Earlier work this paper cites.
Discovering neural nets with low kolmogorov complexity and high generalization capability
J. Schmidhuber · 1997
Earlier work this paper cites.
Ensemble learning in bayesian neural networks
D. Barber and C. Bishop · 1998
Earlier work this paper cites.
Antipredator behaviour of hatchling snakes: effects of incubation temperature and simulated predators
J. Burger · 1998
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
Y. LeCun · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
Cited alongside, same era.
Evolving the topology and the weights of neural networks using a dual representation
J. C. F. Pujol and R. Poli · 1998
Cited alongside, same era.
Patterns of development: the altricial-precocial spectrum
J. M. Starck and R. E. Ricklefs · 1998
Cited alongside, same era.
Towards designing artificial neural networks by evolution
X. Yao and Y. Liu · 1998
Cited alongside, same era.
Neural connections: Some you use, some you lose
J. T. Bruer · 1999
Cited alongside, same era.
Using MPI: portable parallel programming with the message-passing interface
W. D. Gropp, W. Gropp, E. Lusk, and A. Skjellum · 1999
Cited alongside, same era.
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
K. Fukushima and S. Miyake · 2016
Later among the works it cites.
Uncertainty in deep learning
Y. Gal · 2016
Later among the works it cites.
Improving pilco with bayesian neural network dynamics models
Y. Gal, R. McAllister, and C. E. Rasmussen · 2016
Later among the works it cites.
Adaptive computation time for recurrent neural networks
A. Graves · 2016
Later among the works it cites.
Dynamic network surgery for efficient dnns
Y. Guo, A. Yao, and Y. Chen · 2016
Later among the works it cites.
A powerful generative model using random weights for the deep image representation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Does prey matter? geographic variation in antipredator responses of hatchlings of a japanese natricine snake (rhabdophis tigrinus)
A. Mori and G. M. Burghardt · 2000
Cited alongside, same era.
Innate predator-recognition in australian brush-turkey (alectura lathami, megapodiidae) hatchlings
A. Goth · 2001
Cited alongside, same era.
A fast and elitist multiobjective genetic algorithm: Nsga-ii
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan · 2002
Cited alongside, same era.
Motor learning-dependent synaptogenesis is localized to functionally reorganized motor cortex
J. A. Kleim, S. Barbay, N. R. Cooper, T. M. Hogg, C. N. Reidel, M. S. Remple, and R. J. Nudo · 2002
Cited alongside, same era.
Evolving neural networks through augmenting topologies
K. O. Stanley and R. Miikkulainen · 2002
Cited alongside, same era.
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H. Jaeger and H. Haas · 2004
Cited alongside, same era.
K. He, Y. Wang, and J. Hopcroft · 2016
Later among the works it cites.
Inductive bias of deep convolutional networks through pooling geometry
N. Cohen and A. Shashua · 2017
Later among the works it cites.
The complete connectome of a learning and memory centre in an insect brain
K. Eichler, F. Li, A. Litwin-Kumar, Y. Park, I. Andrade, C. M. Schneider-Mizell, T. Saumweber, A. Huser, C. Eschbach, and B. Gerber · 2017
Later among the works it cites.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Later among the works it cites.
Evolving stable strategies
D. Ha · 2017
Later among the works it cites.
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2017
Later among the works it cites.
D. Krueger, C.-W. Huang, R. Islam, R. Turner, A. Lacoste, and A. Courville · 2017
Later among the works it cites.
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Later among the works it cites.
Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Later among the works it cites.
Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
Later among the works it cites.
Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
Later among the works it cites.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin · 2017
Later among the works it cites.
Dynamic routing between capsules
S. Sabour, N. Frosst, and G. E. Hinton · 2017
Later among the works it cites.
A connectome of a learning and memory center in the adult drosophila brain
S.-y. Takemura, Y. Aso, T. Hige, A. Wong, Z. Lu, C. S. Xu, P. K. Rivlin, H. Hess, T. Zhao, and T. Parag · 2017
Later among the works it cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Later among the works it cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
Later among the works it cites.
The description length of deep learning models
L. Blier and Y. Ollivier · 2018
Later among the works it cites.
Smash: One-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2018
Later among the works it cites.
Compiling machine learning programs via high-level tracing, 2018
R. Frostig, M. J. Johnson, and C. Leary · 2018
Later among the works it cites.
Reinforcement learning for improving agent design
D. Ha · 2018
Later among the works it cites.
Recurrent world models facilitate policy evolution
D. Ha and J. Schmidhuber · 2018
Later among the works it cites.
Measuring the intrinsic dimension of objective landscapes
C. Li, H. Farkhoor, R. Liu, and J. Yosinski · 2018
Later among the works it cites.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
Later among the works it cites.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights
A. Mallya, D. Davis, and S. Lazebnik · 2018
Later among the works it cites.
Continual lifelong learning with neural networks: A review
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2018
Later among the works it cites.
Efficient neural architecture search via parameter sharing
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean · 2018
Later among the works it cites.
A. Trask, F. Hill, S. E. Reed, J. Rae, C. Dyer, and P. Blunsom · 2018
Later among the works it cites.
Deep image prior
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
Later among the works it cites.
Pytorch implementation of improving pilco with bayesian neural network dynamics models, 2018
X. Zuo · 2018
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2019
Closest in time.
Snip: Single-shot network pruning based on connection sensitivity
N. Lee, T. Ajanthan, and P. Torr · 2019
Closest in time.
Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
Closest in time.
Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2019
Closest in time.
R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, and N. Duffy · 2019
Closest in time.
Variance networks: When expectation does not meet your expectations
K. Neklyudov, D. Molchanov, A. Ashukha, and D. Vetrov · 2019
Closest in time.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
Closest in time.
A critique of pure learning and what artificial neural networks can learn from animal brains
A. M. Zador · 2019
Closest in time.