Technology
On the Generative Utility of Cyclic Conditionals Chang Liu 1, Haoyue T ang 2, Tao Qin
Work done during an internship at Microsoft Research Asia.
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Thisoptimizationcan beperformedinparallelforeachs, keeping t 1 fixed.
AInjectiveChange-of-VariableFormulaandStacking InjectiveFlows Wefirstderive(5)from(3). Bythechainrule,wehave: J[gฯ ] g
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 / ฮธlogdetAฮธ is computed using backpropagation. Thefor loop is easily parallelized in practice.
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao
Andtheexisting convergence rates are also limited to linear convergence.
d0c6bc641a56bebee9d985b937307367-Paper-Conference.pdf
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.
