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Machine learning research has advanced in multiple aspects, including model structures and learning methods.
Random search and reproducibility for neural architecture search
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Convolutional networks for images, speech, and time series
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Long short-term memory
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The mnist database of handwritten digits, 1998
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Parallelism and evolutionary algorithms
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Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
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Evolving neural networks through augmenting topologies
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Optimal ordered problem solver
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Rectified linear units improve restricted boltzmann machines
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Indirectly encoding neural plasticity as a pattern of local rules
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Sequential model-based optimization for general algorithm configuration
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Reading digits in natural images with unsupervised feature learning
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Practical recommendations for gradient-based training of deep architectures
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Random search for hyper-parameter optimization
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Practical bayesian optimization of machine learning algorithms
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Dropout: A simple way to prevent neural networks from overfitting
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Beyond convexity: Stochastic quasi-convex optimization
Hazan, E., Levy, K., and Shalev-Shwartz, S · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Deep clustered convolutional kernels
Kim, M. and Rigazio, L · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Neural programmer: Inducing latent programs with gradient descent
Learned optimizers that scale and generalize
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
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Genetic CNN
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2018
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Memory augmented policy optimization for program synthesis and semantic parsing
Liang, C., Norouzi, M., Berant, J., Le, Q. V., and Lao, N · 2018
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Progressive neural architecture search
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Neelakantan, A., Le, Q. V., and Sutskever, I · 2015
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Flashmeta: a framework for inductive program synthesis
Polozov, O. and Gulwani, S · 2015
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Neural programmer-interpreters
Reed, S. E. and de Freitas, N · 2015
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Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., and de Freitas, N · 2016
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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The power of normalization: Faster evasion of saddle points
Levy, K. Y · 2016
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Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Liang, C., Berant, J., Le, Q. V., Forbus, K. D., and Lao, N · 2016
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Liu, C., Zoph, B., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., and Murphy, K · 2018
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Houdini: Lifelong learning as program synthesis
Valkov, L., Chaudhari, D., Srivastava, A., Sutton, C. A., and Chaudhuri, S · 2018
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Evolving simple programs for playing atari games
Wilson, D. G., Cussat-Blanc, S., Luga, H., and Miller, J. F · 2018
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Taking human out of learning applications: A survey on automated machine learning
Yao, Q., Wang, M., Chen, Y., Dai, W., Yi-Qi, H., Yu-Feng, L., Wei-Wei, T., Qiang, Y., and Yang, Y · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Zela, A., Klein, A., Falkner, S., and Hutter, F · 2018
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Practical block-wise neural network architecture generation
Zhong, Z., Yan, J., Wu, W., Shao, J., and Liu, C.-L · 2018
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
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Meta-learning curiosity algorithms
Alet, F., Schneider, M. F., Lozano-Perez, T., and Kaelbling, L. P · 2019
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Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
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Weight agnostic neural networks
Gaier, A. and Ha, D · 2019
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Nas-fpn: Learning scalable feature pyramid architecture for object detection
Ghiasi, G., Lin, T.-Y., and Le, Q. V · 2019
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Meta-learning update rules for unsupervised representation learning
Metz, L., Maheswaranathan, N., Cheung, B., and Sohl-Dickstein, J · 2019
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Evolving deep neural networks
Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., et al · 2019
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Towards modular and programmable architecture search
Negrinho, R., Gormley, M., Gordon, G. J., Patil, D., Le, N., and Ferreira, D · 2019
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Asap: Architecture search, anneal and prune
Noy, A., Nayman, N., Ridnik, T., Zamir, N., Doveh, S., Friedman, I., Giryes, R., and Zelnik-Manor, L · 2019
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Specaugment: A simple data augmentation method for automatic speech recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., and Le, Q. V · 2019
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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The evolved transformer
So, D. R., Liang, C., and Le, Q. V · 2019
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Designing neural networks through neuroevolution
Stanley, K. O., Clune, J., Lehman, J., and Miikkulainen, R · 2019
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Evolving deep convolutional neural networks for image classification
Sun, Y., Xue, B., Zhang, M., and Yen, G. G · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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Meta-learning
Vanschoren, J · 2019
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Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions
Wang, R., Lehman, J., Clune, J., and Stanley, K. O · 2019
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Exploring randomly wired neural networks for image recognition
Xie, S., Kirillov, A., Girshick, R., and He, K · 2019
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Nas-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., and Hutter, F · 2019
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Multiplicative interactions and where to find them
Jayakumar, S. M., Menick, J., Czarnecki, W. M., Schwarz, J., Rae, J., Osindero, S., Teh, Y. W., Harley, T., and Pascanu, R · 2020
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Atomnas: Fine-grained end-to-end neural architecture search
Mei, J., Li, Y., Lian, X., Jin, X., Yang, L., Yuille, A., and Yang, J · 2020
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Nas evaluation is frustratingly hard
Yang, A., Esperança, P. M., and Carlucci, F. M · 2020
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