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In this paper, we introduce a new type of generalized neural network where neurons and synapses maintain multiple states.
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Towards biologically plausible deep learning
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Deep residual learning for image recognition
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Difference target propagation
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Learning to learn by gradient descent by gradient descent
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Ha, D., Dai, A., and Le, Q. V · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
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Deep learning without weight transport
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Greedy layerwise learning can scale to imagenet
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Putting an end to end-to-end: Gradient-isolated learning of representations
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Understanding and correcting pathologies in the training of learned optimizers
Metz, L., Maheswaranathan, N., Nixon, J., Freeman, D., and Sohl-Dickstein, J · 2019
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Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
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Nøkland, A · 2016
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Neuromodulation improves the evolution of forward models
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Training neural networks without gradients: A scalable ADMM approach
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Instance normalization: The missing ingredient for fast stylization
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EMNIST: an extension of MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
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Notes on chain recurrence and lyapunonv functions
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Optimization as a model for few-shot learning
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Munkhdalai, T., Sordoni, A., Wang, T., and Trischler, A · 2019
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Bidirectional learning for robust neural networks
Pontes-Filho, S. and Liwicki, M · 2019
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GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
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Continual learning with deep artificial neurons
Camp, B., Mandivarapu, J. K., and Estrada, R · 2020
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A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
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Meta learning backpropagation and improving it
Kirsch, L. and Schmidhuber, J · 2020
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Learning to learn with feedback and local plasticity
Lindsey, J. and Litwin-Kumar, A · 2020
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Reverse engineering learned optimizers reveals known and novel mechanisms
Maheswaranathan, N., Sussillo, D., Metz, L., Sun, R., and Sohl-Dickstein, J · 2020
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Metz, L., Maheswaranathan, N., Freeman, C. D., Poole, B., and Sohl-Dickstein, J · 2020
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Meta-learning through hebbian plasticity in random networks
Najarro, E. and Risi, S · 2020
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Mplp: Learning a message passing learning protocol
Randazzo, E., Niklasson, E., and Mordvintsev, A · 2020
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ZORB: A derivative-free backpropagation algorithm for neural networks
Ranganathan, V. and Lewandowski, A · 2020
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Automl-zero: evolving machine learning algorithms from scratch
Real, E., Liang, C., So, D., and Le, Q · 2020
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Descending through a crowded valley - benchmarking deep learning optimizers
Schmidt, R. M., Schneider, F., and Hennig, P · 2020
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LoCo: Local contrastive representation learning
Xiong, Y., Ren, M., and Urtasun, R · 2020
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