4 papers
Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
Samuel Chun-Hei Lam, Justin Sirignano, Konstantinos Spiliopoulos
Recurrent neural networks (RNNs) are commonly trained with the truncated backpropagation-through-time (TBPTT) algorithm. For the purposes of computational tractability, the TBPTT a…
Weak Convergence Analysis of Online Neural Actor-Critic Algorithms
Samuel Chun-Hei Lam, Justin Sirignano, Ziheng Wang
We prove that a single-layer neural network trained with the online actor critic algorithm converges in distribution to a random ordinary differential equation (ODE) as the number…
Deep Neural Network Initialization with Sparsity Inducing Activations
Ilan Price, Nicholas Daultry Ball, Samuel C. H. Lam +2
Inducing and leveraging sparse activations during training and inference is a promising avenue for improving the computational efficiency of deep networks, which is increasingly im…
Kernel Limit for a Class of Recurrent Neural Networks Trained on Ergodic Data Sequences
Samuel Chun-Hei Lam, Justin Sirignano, Konstantinos Spiliopoulos
Mathematical methods are developed to characterize the asymptotics of recurrent neural networks (RNN) as the number of hidden units, data samples in the sequence, hidden state upda…