Thisoptimizationcan beperformedinparallelforeachs, keeping t 1 fixed.

We summarize our methods for computing/estimating the gradient of the log determinant arising inmaximum likelihood training ofrectangular flows. Algorithm 2showstheexactmethod, where jvp(f,z,)denotes computingJ[f](z) usingforward-mode AD,and i Rd isthei-thstandard basis vector, i.e. a one-hot vector with a1 on its i-th coordinate. Note that / θlogdetAθ is computed using backpropagation. Thefor loop is easily parallelized in practice.

Andtheexisting convergence rates are also limited to linear convergence.

Asuccessful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large language models provide new prospects towards this goal.

We present a scalable approach for learningopen-world object-goal navigation (ObjectNav) - the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e.g.,"find a sink").

To estimate the impact of removing a parameter, these methods often use importance measures that were originally designed to prune neural networks. If this hypothesis is true, it has great potential to covert the inefficient training process on a large network to the scalable training process over a small one with comparable test accuracy. Most of existing LTH techniques provide empirical evidence to verify the LTH, although these methods raise very intriguing observations [71, 12, 1, 47, 69, 54, 5, 53, 26, 8, 7, 11]. However, multiple cycles of training and pruning over large neural networks are time-consuming. Tworecent worksanalyze the LTH transferability, i.e., the ticket discovered from one source task can be transferred to another targettask[44,43].