Convergence of flow-based generative models via proximal gradient descent in Wasserstein space

Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to the accumulating theoretical studies on related score-based diffusion models, analysis of flow-based models, which are deterministic in both forward (data-to-noise) and reverse (noise-to-data) directions, remain sparse. In this paper, we provide a theoretical guarantee of generating data distribution by a progressive flow model, the so-called JKO flow model, which implements the Jordan-Kinderleherer-Otto (JKO) scheme in a normalizing flow network. Leveraging the exponential convergence of the proximal gradient descent (GD) in Wasserstein space, we prove the Kullback-Leibler (KL) guarantee of data generation by a JKO flow model to be $O(\varepsilon^2)$ when using $N \lesssim \log (1/\varepsilon)$ many JKO steps ($N$ Residual Blocks in the flow) where $\varepsilon$ is the error in the per-step first-order condition. The assumption on data density is merely a finite second moment, and the theory extends to data distributions without density and when there are inversion errors in the reverse process where we obtain KL-$W_2$ mixed error guarantees. The non-asymptotic convergence rate of the JKO-type $W_2$-proximal GD is proved for a general class of convex objective functionals that includes the KL divergence as a special case, which can be of independent interest

Does adoption mean the same to every user? A study of active and passive usage of mobile instant messaging applications

This research-in-progress paper studies the active and passive usage of mobile instant messaging (MIM) applications. Grounded on two-factor theory and three-factor theory, we propose the features of MIM applications influence the active/passive usage of MIM applications through users’ satisfaction and dissatisfaction. The proposed features are categorized into three factors: exciting factors which contain design aesthetics, customization and enjoyment, performance factors which include sociability, convenience and privacy assurance, and basic factors which are application costs and technical functionality. To test hypothetical relationships in this study, we plan to use a survey method. The potential implications to both literature and practice are discussed

Are carbon-based materials good supports for the catalytic reforming of ammonia?

Carbon-based materials are commonly used in catalysis as metal-free catalysts and as supports for metal particles. We investigated a series of graphene point defects using the density functional theory (DFT) and shed light on their role in the catalytic reforming of ammonia. The adsorption of molecules and reaction intermediates on carbon vacancies, lattice reconstruction, partial oxidation, and dopants was analyzed to provide details on the most favorable interactions. Thermochemical investigations revealed the structures active for NH3 adsorption and dehydrogenation. Based on transition-state theory, we implemented microkinetic simulations and found that the rate-determining step is either NH3 activation or the desorption of reformed molecules, depending on the defect type. However, investigated defects are ineffective to desorb the reaction products, i.e., N2 and H2. Batch reaction simulations within wide temperature and time ranges indicated that although NH3 dehydrogenation may occur, the active sites become poisoned by the H or N anchored atoms; therefore, in the long term, carbon-based materials are inert toward NH3 reforming

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or exposure artifacts due to their sampling principle. By contrast, event-based cameras are novel bio-inspired sensors that offer advantages to overcome such limitations. They report pixelwise intensity changes asynchronously, which enables them to acquire visual information at exactly the same rate as the scene dynamics. We develop a method to identify independently moving objects acquired with an event-based camera, i.e., to solve the event-based motion segmentation problem. We cast the problem as an energy minimization one involving the fitting of multiple motion models. We jointly solve two subproblems, namely event cluster assignment (labeling) and motion model fitting, in an iterative manner by exploiting the structure of the input event data in the form of a spatio-temporal graph. Experiments on available datasets demonstrate the versatility of the method in scenes with different motion patterns and number of moving objects. The evaluation shows state-of-the-art results without having to predetermine the number of expected moving objects. We release the software and dataset under an open source licence to foster research in the emerging topic of event-based motion segmentation

Reinforcement Learning, Bit by Bit

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. We develop concepts and establish a regret bound that together offer principled guidance. The bound sheds light on questions of what information to seek, how to seek that information, and it what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that demonstrate improvements in data efficiency