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Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models.
Crafting papers on machine learning
Langley, P · 2000
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., and Fergus, R · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
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Large scale evolution of convolutional neural networks using volunteer computing
Desell, T · 2017
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Simple and efficient architecture search for convolutional neural networks
Elsken, T., Metzen, J.-H., and Hutter, F · 2017
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Efficient architecture search by network transformation
Cai, H., Chen, T., Zhang, W., Yu, Y., and Wang, J · 2018
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Neuronal cell death
Fricker, M., Tolkovsky, A. M., Borutaite, V., Coleman, M., and Brown, G. C · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T.-J., and Choi, E · 2018
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Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2018
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Compnet: Neural networks growing via the compact network morphism
Lu, J., Ma, W., and Faltings, B · 2018
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Nisp: Pruning networks using neuron importance score propagation
Yu, R., Li, A., Chen, C.-F., Lai, J.-H., Morariu, V. I., Han, X., Gao, M., Lin, C.-Y., and Davis, L. S · 2018
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Pytorch: An imperative style, high-performance deep learning library
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Energy and policy considerations for deep learning in nlp
Strubell, E., Ganesh, A., and McCallum, A · 2019
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., and Maida, A · 2019
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Single-cell reconstruction of emerging population activity in an entire developing circuit
Wan, Y., Wei, Z., Looger, L. L., Koyama, M., Druckmann, S., and Keller, P. J · 2019
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Pc-darts: Partial channel connections for memory-efficient architecture search
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.-J., Tian, Q., and Xiong, H · 2019
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Nest: A neural network synthesis tool based on a grow-and-prune paradigm
Dai, X., Yin, H., and Jha, N. K · 2019
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Jin, H., Song, Q., and Hu, X · 2019
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Adult neurogenesis in humans: a review of basic concepts, history, current research, and clinical implications
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Importance estimation for neural network pruning
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Incremental learning using a grow-and-prune paradigm with efficient neural networks
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Gradmax: Growing neural networks using gradient information
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