State estimation for nonlinear state space models is a challenging task. Existing assimilation methodologies predominantly assume Gaussian posteriors on physical space, where true posteriors become inevitably non-Gaussian. We propose Deep Bayesian Filtering (DBF) for data assimilation on nonlinear state space models (SSMs). DBF constructs new latent variables $h_t$ on a new latent (``fancy'') space and assimilates observations $o_t$. By (i) constraining the state transition on fancy space to be linear and (ii) learning a Gaussian inverse observation operator $q(h_t|o_t)$, posteriors always remain Gaussian for DBF. Quite distinctively, the structured design of posteriors provides an analytic formula for the recursive computation of posteriors without accumulating Monte-Carlo sampling errors over time steps. DBF seeks the Gaussian inverse observation operators $q(h_t|o_t)$ and other latent SSM parameters (e.g., dynamics matrix) by maximizing the evidence lower bound. Experiments show that DBF outperforms model-based approaches and latent assimilation methods in various tasks and conditions.

A problem that plagues robotic grasping is the misalignment of the object and gripper due to difficulties in precise localization, actuation, etc. Under-actuated robotic hands with compliant mechanisms are used to adapt and compensate for these inaccuracies. However, these mechanisms come at the cost of controllability and coordination. For instance, adaptive functions that let the fingers of a two-fingered gripper adapt independently may affect the coordination necessary for grasping small objects. In this work, we develop a two-fingered robotic hand capable of grasping objects that are offset from the gripper's center, while still having the requisite coordination for grasping small objects via a novel gear-type synchronization mechanism with a magnet. This gear synchronization mechanism allows the adaptive finger's tips to be aligned enabling it to grasp objects as small as toothpicks and washers. The magnetic component allows this coordination to automatically turn off when needed, allowing for the grasping of objects that are offset/misaligned from the gripper. This equips the hand with the capability of grasping light, fragile objects (strawberries, creampuffs, etc) to heavy frying pan lids, all while maintaining their position and posture which is vital in numerous applications that require precise positioning or careful manipulation.

Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental conditions. Machine learning approaches are promising for modeling their relationships and offering actionable suggestions. In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities. VHGM is a deep generative model trained with masked modeling to learn the joint distribution of attributes conditioned on known ones. Using heterogeneous tabular datasets, VHGM learns more than 1,800 attributes efficiently. We numerically evaluate the performance of VHGM and its training techniques. As a proof-of-concept of VHGM, we present several applications demonstrating user scenarios, such as virtual measurements of healthcare attributes and hypothesis verifications of lifestyles.

However, While VAEs are nowadays omnipresent in the field of machine in practice, they suffer from a problem called learning, it is also widely recognized that there remain posterior collapse, which occurs when the encoder in practice some major challenges that still require effective coincides, or collapses, with the prior taking no solutions. Notably, they suffer from the problem of information from the latent structure of the input posterior collapse, which occurs when the distribution corresponding data into consideration. In this work, we introduce to the encoder coincides, or collapses, with the an inverse Lipschitz neural network into the prior taking no information from the latent structure of the decoder and, based on this architecture, provide a input data into consideration. Also known as KL vanishing new method that can control in a simple and clear or over-pruning, this phenomenon makes VAEs incapable manner the degree of posterior collapse for a wide to produce pertinent representations and has been reportedly range of VAE models equipped with a concrete observed in many fields (e.g., Bowman et al. (2016); Fu et al. theoretical guarantee. We also illustrate the effectiveness (2019); Wang & Ziyin (2022); Yeung et al. (2017)). There of our method through several numerical exists now a large body of literature that examines its underlying experiments.

We show that, when we cast meta-learning problem as BRM, the optimal solution Meta-Learning is a family of methods that use is given by the predictive distribution computed from a set of interrelated tasks to learn a model that the posterior distribution of the latent variable conditioned can quickly learn a new query task from a possibly against the contextual dataset. This result justifies the use of small contextual dataset. In this study, we the predictive distribution in many previous studies of meta use a probabilistic framework to formalize what learning, such as (Edwards & Storkey, 2017; Gordon et al., it means for two tasks to be related and reframe 2018; Garnelo et al., 2018). However, the optimality of the the meta-learning problem into the problem of predictive distribution cannot be guaranteed if one uses an Bayesian risk minimization (BRM). In our formulation, approximation of the posterior distribution that violates the the BRM optimal solution is given by the way the posterior distribution changes with the contextual predictive distribution computed from the posterior dataset, and this is unfortunately the case for most of the distribution of the task-specific latent variable aforementioned works. For example, the variance of the conditioned on the contextual dataset, and this posterior in these works do not converge to 0 as we take justifies the philosophy of Neural Process.

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP problem without making any compromise on the safety constraints. We achieve this by decomposing the CMDP into a pair of MDPs; reconnaissance MDP and planning MDP. The purpose of reconnaissance MDP is to evaluate the set of actions that are safe, and the purpose of planning MDP is to maximize the return while using the actions authorized by reconnaissance MDP. RMDP can define a set of safe policies for any given set of safety constraint, and this set of safe policies can be used to solve another CMDP problem with different reward. Our method is not only computationally less demanding than the previous simulator-based approaches to CMDP, but also capable of finding a competitive reward-seeking policy in a high dimensional environment, including those involving multiple moving obstacles.

Tensor decomposition methods are one of the primary approaches for model compression and fast inference of convolutional neural networks (CNNs). However, despite their potential diversity, only a few typical decompositions such as CP decomposition have been applied in practice; more importantly, no extensive comparisons have been performed between available methods. This raises the simple question of how many decompositions are possible, and which of these is the best. In this paper, we first characterize a decomposition class specific to CNNs by adopting graphical notation, which is considerably flexible. When combining with the nonlinear activations, the class includes renowned CNN modules such as depthwise separable convolution and bottleneck layer. In the experiments, we compare the tradeoff between prediction accuracy and time/space complexities by enumerating all the possible decompositions. Also, we demonstrate, using a neural architecture search, that we can find nonlinear decompositions that outperform existing decompositions.

Robustness to adversarial perturbations has become an essential feature in the design of modern classifiers --in particular, of deep neural networks. This phenomenon originates from several empirical observations, such as [1] and [2], which show deep networks are vulnerable to adversarial attacks in the input space. So far, plenty of novel methodologies have been introduced to compensate for this shortcoming. Adversarial Training (AT) [3], Virtual AT [4] or Distillation [5] are just examples of some promising methods in this area. The majority of these approaches seek an effective defense against a point-wise adversary, who shifts input data-points toward adversarial directions, in a separate manner. However, as shown by [6], a distributional adversary who can shift the data distribution instead of the input data-points is provably more detrimental to learning. This suggests that one can greatly improve the robustness of a classifier by improving its defense against a distributional adversary rather than a point-wise one. This motivation has led to the development of Distributionally Robust Learning (DRL) [7], which has attracted intensive research interest over the last few years [8, 9, 10, 11]. Despite of all the advancements in supervised or unsupervised DRL, the amount of researches tackling this problem from a semi-supervised angle is slim to none [12].

Recently, Graph Neural Networks (GNNs) are trending in the machine learning community as a family of architectures that specializes in capturing the features of graph-related datasets, such as those pertaining to social networks and chemical structures. Unlike for other families of the networks, the representation power of GNNs has much room for improvement, and many graph networks to date suffer from the problem of underfitting. In this paper we will introduce a Graph Warp Module, a supernode-based auxiliary network module that can be attached to a wide variety of existing GNNs in order to improve the representation power of the original networks. Through extensive experiments on molecular graph datasets, we will show that our GWM indeed alleviates the underfitting problem for various existing networks, and that it can even help create a network with the state-of-the-art generalization performance.

Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predictions of graph convolutional networks?" A possible approach to answer this question is to visualize evidence substructures responsible for the predictions. For chemical property prediction tasks, the sample size of the training data is often small and/or a label imbalance problem occurs, where a few samples belong to a single class and the majority of samples belong to the other classes. This can lead to uncertainty related to the learned parameters of the machine learning model. To address this uncertainty, we propose BayesGrad, utilizing the Bayesian predictive distribution, to define the importance of each node in an input graph, which is computed efficiently using the dropout technique. We demonstrate that BayesGrad successfully visualizes the substructures responsible for the label prediction in the artificial experiment, even when the sample size is small. Furthermore, we use a real dataset to evaluate the effectiveness of the visualization. The basic idea of BayesGrad is not limited to graph-structured data and can be applied to other data types.