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The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision.
The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 1902
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Stabilizing the lottery ticket hypothesis
Frankle, J., Dziugaite, G. K., Roy, D. M., and Carbin, M · 1903
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The difficulty of training sparse neural networks
Evci, U., Pedregosa, F., Gomez, A. N., and Elsen, E · 1906
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Sparse networks from scratch: Faster training without losing performance
Dettmers, T. and Zettlemoyer, L · 1907
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., de Oliveira Pinto, H. P., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F., and Zhang, S · 1912
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Neural net pruning-why and how
Sietsma, J. and Dow, R. J · 1988
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Mozer, M. C. and Smolensky, P · 1989
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Learning from Delayed Rewards
Watkins, C. J. C. H · 1989
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Puterman, M. L · 1994
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Crafting papers on machine learning
Langley, P · 2000
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Evolving neural networks through augmenting topologies
Stanley, K. O. and Miikkulainen, R · 2002
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What is the state of neural network pruning?
Blalock, D., Ortiz, J. J. G., Frankle, J., and Guttag, J · 2003
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The large learning rate phase of deep learning: the catapult mechanism
Lewkowycz, A., Bahri, Y., Dyer, E., Sohl-Dickstein, J., and Gur-Ari, G · 2003
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Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L. K., and Ganguli, S · 2006
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Evolutionary function approximation for reinforcement learning
Whiteson, S. and Stone, P · 2006
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Pruning neural networks at initialization: Why are we missing the mark?
Frankle, J., Dziugaite, G. K., Roy, D. M., and Carbin, M · 2009
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Convergence rates of inexact proximal-gradient methods for convex optimization
Schmidt, M., Roux, N. L., and Bach, F · 2011
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Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
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The Arcade Learning Environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
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EIE: Efficient Inference Engine on compressed deep neural network
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., and Dally, W. J · 2016
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M. I., and Abbeel, P · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Cited alongside, same era.
Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
Cited alongside, same era.
Variational Dropout Sparsifies Deep Neural Networks
Molchanov, D., Ashukha, A., and Vetrov, D. P · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
Hasani, R., Lechner, M., Amini, A., Rus, D., and Grosu, R · 2020
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What do compressed deep neural networks forget
Hooker, S., Courville, A. C., Clark, G., Dauphin, Y., and Frome, A · 2020
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Soft threshold weight reparameterization for learnable sparsity
Kusupati, A., Ramanujan, V., Somani, R., Wortsman, M., Jain, P., Kakade, S., and Farhadi, A · 2020
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Li, Z., Wallace, E., Shen, S., Lin, K., Keutzer, K., Klein, D., and Gonzalez, J · 2020
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Finding trainable sparse networks through neural tangent transfer
Liu, T. and Zenke, F · 2020
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Cited alongside, same era.
Deep rewiring: Training very sparse deep networks
Bellec, G., Kappel, D., Maass, W., and Legenstein, R. A · 2018
Cited alongside, same era.
Dopamine: A research framework for deep reinforcement learning
Castro, P. S., Moitra, S., Gelada, C., Kumar, S., and Bellemare, M. G · 2018
Cited alongside, same era.
IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K · 2018
Cited alongside, same era.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Cited alongside, same era.
Efficient neural audio synthesis
Kalchbrenner, N., Elsen, E., Simonyan, K., Noury, S., Casagrande, N., Lockhart, E., Stimberg, F., Oord, A., Dieleman, S., and Kavukcuoglu, K · 2018
Cited alongside, same era.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., and Liotta, A · 2018
Cited alongside, same era.
Pops: Policy pruning and shrinking for deep reinforcement learning
Livne, D. and Cohen, K · 2020
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Stabilizing transformers for reinforcement learning
Parisotto, E., Song, F., Rae, J., Pascanu, R., Gulcehre, C., Jayakumar, S., Jaderberg, M., Kaufman, R. L., Clark, A., Noury, S., Botvinick, M., Heess, N., and Hadsell, R · 2020
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Neuroevolution of self-interpretable agents
Tang, Y., Nguyen, D., and Ha, D · 2020
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Picking winning tickets before training by preserving gradient flow
Wang, C., Zhang, G., and Grosse, R · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A., and Bellemare, M. G · 2021
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Single-shot pruning for offline reinforcement learning
Arnob, S. Y., Ohib, R., Plis, S., and Precup, D · 2021
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Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., and Peste, A · 2021
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
Kumar, A., Agarwal, R., Ghosh, D., and Levine, S · 2021
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Gst: Group-sparse training for accelerating deep reinforcement learning
Lee, J., Kim, S., Kim, S., Jo, W., and Yoo, H.-J · 2021
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Do we actually need dense over-parameterization? in-time over-parameterization in sparse training
Liu, S., Yin, L., Mocanu, D. C., and Pechenizkiy, M · 2021
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On the effects of pruning on evolved neural controllers for soft robots
Nadizar, G., Medvet, E., Pellegrino, F. A., Zullich, M., and Nichele, S · 2021
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Efficient transformers in reinforcement learning using actor-learner distillation
Parisotto, E. and Salakhutdinov, R · 2021
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Ac/dc: Alternating compressed/decompressed training of deep neural networks
Peste, A., Iofinova, E., Vladu, A., and Alistarh, D · 2021
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Rrl: Resnet as representation for reinforcement learning
Shah, R. M. and Kumar, V · 2021
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Dynamic sparse training for deep reinforcement learning
Sokar, G., Mocanu, E., Mocanu, D. C., Pechenizkiy, M., and Stone, P · 2021
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On lottery tickets and minimal task representations in deep reinforcement learning
Vischer, M. A., Lange, R., and Sprekeler, H · 2021
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Magnetic control of tokamak plasmas through deep reinforcement learning
Degrave, J., Felici, F., Buchli, J., Neunert, M., Tracey, B., Carpanese, F., Ewalds, T., Hafner, R., Abdolmaleki, A., de las Casas, D., Donner, C., Fritz, L., Galperti, C., Huber, A., Keeling, J., Tsimpoukelli, M., Kay, J., Merle, A., Moret, J.-M., Noury, S., Pesamosca, F., Pfau, D., Sauter, O., Sommariva, C., Coda, S., Duval, B., Fasoli, A., Kohli, P., Kavukcuoglu, K., Hassabis, D., and Riedmiller, M · 2022
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Gradient flow in sparse neural networks and how lottery tickets win
Evci, U., Ioannou, Y. A., Keskin, C., and Dauphin, Y · 2022
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The primacy bias in deep reinforcement learning
Nikishin, E., Schwarzer, M., D’Oro, P., Bacon, P.-L., and Courville, A · 2022
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