Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time. In our framework, each data instance is pushed into a cascade of deep neural networks with increasing sizes, and a selection module is used to sequentially determine when a sufficiently accurate classifier can be used for this data instance. The cascade of neural networks and the selection module are jointly trained in an end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between the computational cost and the predictive accuracy. Our method is able to simultaneously improve the accuracy and efficiency by learning to assign easy instances to fast yet sufficiently accurate classifiers to save computation and energy cost, while assigning harder instances to deeper and more powerful classifiers to ensure satisfiable accuracy. With extensive experiments on several image classification datasets using cascaded ResNet classifiers, we demonstrate that our method outperforms the standard well-trained ResNets in accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100 datasets and 66% on the ImageNet dataset, respectively

Cobalt-Catalyzed Aerobic Oxidative Cyclization Reactions of Bisnucleophiles: New Methodologies and the Role of Bisnucleophiles in O2 Activation

Over the past few decades, transition metals have found wide applications in the development of selective oxidative transformations mediated by molecular oxygen. Due to the benign nature of molecular oxygen as an oxidant and an increasing awareness of green chemistry practice, tremendous progress has been made towards the development of Cu-and Pd-catalyzed aerobic oxidation reactions. As a first-row transition metal alternative to copper, cobalt has been employed in aerobic catalytic transformations for its cost-efficiency and earth abundance; however, redox-active mediators such as benzoquinone (BQ), N-hydroxyphthalimide (NHPI) or salen-type ligands are usually required. To date, reactions mediated by Co/O2 catalytic systems in the absence of redox mediators are still limited. In this regard, we developed a highly efficient protocol employing a Co/O2 catalytic system without acquiring external mediators to turn-over the cycle. This dissertation offers a brief overview of advancements in cobalt-catalyzed aerobic oxidative reactions in chapter 1, where three major classes of oxidation reactions mediated by Co/O2 systems are particularly addressed. Chapterss 2 and 3 describe the development of a series of new cobalt-catalyzed aerobic cyclization reactions of bis-nucleophiles. When isonitriles are employed as coupling partners, a wide variety of functionalized 2-aminobenzoxazoles and 2-aminobenzimidazoles are afforded as pharmaceutically valuable structures. These protocols are additive-free, ligand-free, highly efficient, and require no external redox-active mediators. Mechanistic studies point to the dual function of the bis-nucleophile as both a substrate and a redox-active ligand, and its importance in activating molecular oxygen as the stoichiometric oxidant. Additionally, the bis-nucleophile may act as a hydrogen atom donor capable of participating in hydrogen atom transfer (HAT) reactions. Chapter 4 elucidates the synthesis and characterization of two unprecedented CoII and CoIII complexes consisting of the N-unsubstituted aminophenol ligand. As a continuous effort in deepening mechanistic investigations, these metal complexes are regarded as potential active reaction intermediates and have demonstrated success in enabling O2 activation thus mediating oxidative cyclization transformations afterward