Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don't have to be symmetric with the weights used for propagation the activation forward. In fact, random feedback weights work evenly well, because the network learns how to make the feedback useful. In this work, the feedback alignment principle is used for training hidden layers more independently from the rest of the network, and from a zero initial condition. The error is propagated through fixed random feedback connections directly from the output layer to each hidden layer. This simple method is able to achieve zero training error even in convolutional networks and very deep networks, completely without error back-propagation. The method is a step towards biologically plausible machine learning because the error signal is almost local, and no symmetric or reciprocal weights are required. Experiments show that the test performance on MNIST and CIFAR is almost as good as those obtained with back-propagation for fully connected networks. If combined with dropout, the method achieves 1.45% error on the permutation invariant MNIST task.
A Statistical Framework for Alignment with Biased AI Feedback
Center for Data Science, Zhejiang University（浙江大学数据科学中心）；Department of Electrical and Computer Engineering, Rutgers University（罗格斯大学电气与计算机工程系）；Department of Statistics, Rutgers University（罗格斯大学统计学系）；Faculty of Business and Economics, The University of Hong Kong（香港大学商学院）
Scalable Valuation of Human Feedback through Provably Robust Model Alignment
The University of Osaka（大阪大学）；Lattice Lab, Toyota Motor Corporation（丰田公司Lattice实验室）；Machine Learning Research Group, University of Oxford（牛津大学机器学习研究组）；RIKEN AIP（理化学研究所AIP）
正在进行的研究已经确定了生物网络和深度人工神经网络中神经表征之间的相似性。这使得人们对开发用于训练人工网络的反向传播学习算法与大脑中的突触可塑性规则之间的类比重新产生了兴趣。这些努力受到反向传播的生物学上难以置信的特征的挑战，其中之一是对对称的前向和后向突触权重的依赖。已经提出了许多不依赖于权重对称的方法，但到目前为止，这些方法未能扩展到深度卷积网络和复杂数据。我们确定的主要障碍，这种算法的可扩展性，并介绍了几种技术来减轻它们。我们证明，修改后的反馈对齐方法，强制执行较弱形式的权重对称性，一个需要协议的重量符号，但不幅度，可以实现与反向传播的性能竞争。我们的研究结果补充了Bartunov et al.（2018）和Xiao et al.（2018 b）的研究结果，并表明促进前馈和反馈权重对齐的机制对于深度网络中的学习至关重要。
Align, then memorise: the dynamics of learning with feedback alignment
Ecole Normale Supérieure（巴黎高等师范学院）；EPFL（洛桑联邦理工学院）；Facebook AI Research（脸书人工智能研究院）；LightOn；International School of Advanced Studies (SISSA)（国际高等研究学院（SISSA））
