arXiv subjects

Jaesik Choi

Publications and source records attributed to Jaesik Choi.

82 records · Page 5Linked to original sources

arXiv2018

Hawkes Process Kernel Structure Parametric Search with Renormalization Factors

Hawkes Processes are a type of point process for modeling self-excitation, i.e., when the occurrence of an event makes future events more likely to occur. The corresponding self-triggering function of this type of process may be inferred through an Unconstrained Optimization-based method for maximization of its corresponding Loglikelihood function. Unfortunately, the non-convexity of this procedure, along with the ill-conditioning of the initialization of the self- triggering function parameters, may lead to a consequent instability of this method. Here, we introduce Renormalization Factors, over four types of parametric kernels, as a solution to this instability. These factors are derived for each of the self-triggering function parameters, and also for more than one parameter considered jointly. Experimental results show that the Maximum Likelihood Estimation method shows improved performance with Renormalization Factors over sets of sequences of several different lengths.

stat.AP↗

arXiv2016

An End-to-End Robot Architecture to Manipulate Non-Physical State Changes of Objects

With the advance in robotic hardware and intelligent software, humanoid robot plays an important role in various tasks including service for human assistance and heavy job for hazardous industry. Recent advances in task learning enable humanoid robots to conduct dexterous manipulation tasks such as grasping objects and assembling parts of furniture. Operating objects without physical movements is an even more challenging task for humanoid robot because effects of actions may not be clearly seen in the physical configuration space and meaningful actions could be very complex in a long time horizon. As an example, playing a mobile game in a smart device has such challenges because both swipe actions and complex state transitions inside the smart devices in a long time horizon. In this paper, we solve this problem by introducing an integrated architecture which connects end-to-end dataflow from sensors to actuators in a humanoid robot to operate smart devices. We implement our integrated architecture in the Baxter Research Robot and experimentally demonstrate that the robot with our architecture could play a challenging mobile game, the 2048 game, as accurate as in a simulated environment.

cs.RO↗

arXiv2016

Global Deconvolutional Networks for Semantic Segmentation

Semantic image segmentation is a principal problem in computer vision, where the aim is to correctly classify each individual pixel of an image into a semantic label. Its widespread use in many areas, including medical imaging and autonomous driving, has fostered extensive research in recent years. Empirical improvements in tackling this task have primarily been motivated by successful exploitation of Convolutional Neural Networks (CNNs) pre-trained for image classification and object recognition. However, the pixel-wise labelling with CNNs has its own unique challenges: (1) an accurate deconvolution, or upsampling, of low-resolution output into a higher-resolution segmentation mask and (2) an inclusion of global information, or context, within locally extracted features. To address these issues, we propose a novel architecture to conduct the equivalent of the deconvolution operation globally and acquire dense predictions. We demonstrate that it leads to improved performance of state-of-the-art semantic segmentation models on the PASCAL VOC 2012 benchmark, reaching 74.0% mean IU accuracy on the test set.

cs.CV↗

arXiv2016

Automatic Generation of Probabilistic Programming from Time Series Data

Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute interesting probabilities of various large, real-world problems. When the structure of model is given, constructing a probabilistic program is rather straightforward. Thus, main focus have been to learn the best model parameters and compute marginal probabilities. In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given. The intuition behind of our method is to find a descriptive covariance structure of time series data in nonparametric Gaussian process regression. We report that such descriptive covariance structure efficiently derives a probabilistic programming description accurately.

stat.ML↗

arXiv2016

Improving Imprecise Compressive Sensing Models

Random sampling in compressive sensing (CS) enables the compression of large amounts of input signals in an efficient manner, which is useful for many applications. CS reconstructs the compressed signals exactly with overwhelming probability when incoming data can be sparsely represented with a few components. However, the theory of CS framework including random sampling has been focused on exact recovery of signal; impreciseness in signal recovery has been neglected. This can be problematic when there is uncertainty in the number of sparse components such as signal sparsity in dynamic systems that can change over time. We present a new theoretical framework that handles uncertainty in signal recovery from the perspective of recovery success and quality. We show that the signal recovery success in our model is more accurate than the success probability analysis in the CS framework. Our model is then extended to the case where the success or failure of signal recovery can be relaxed. We represent the number of components included in signal recovery with a right-tailed distribution and focus on recovery quality. Experimental results confirm the accuracy of our model in dynamic systems.

cs.IT↗

arXiv2016

Searching for Topological Symmetry in Data Haystack

Finding interesting symmetrical topological structures in high-dimensional systems is an important problem in statistical machine learning. Limited amount of available high-dimensional data and its sensitivity to noise pose computational challenges to find symmetry. Our paper presents a new method to find local symmetries in a low-dimensional 2-D grid structure which is embedded in high-dimensional structure. To compute the symmetry in a grid structure, we introduce three legal grid moves (i) Commutation (ii) Cyclic Permutation (iii) Stabilization on sets of local grid squares, grid blocks. The three grid moves are legal transformations as they preserve the statistical distribution of hamming distances in each grid block. We propose and coin the term of grid symmetry of data on the 2-D data grid as the invariance of statistical distributions of hamming distance are preserved after a sequence of grid moves. We have computed and analyzed the grid symmetry of data on multivariate Gaussian distributions and Gamma distributions with noise.

cs.LG↗

arXiv2016

The Automatic Statistician: A Relational Perspective

Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.

cs.LG↗

arXiv2015

Learning Execution Contexts from System Call Distributions for Intrusion Detection in Embedded Systems

Existing techniques used for intrusion detection do not fully utilize the intrinsic properties of embedded systems. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. We also present an architectural framework with minor processor modifications to aid in this process. Our prototype shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.

cs.CR↗

arXiv2012

Lifted Relational Variational Inference

Hybrid continuous-discrete models naturally represent many real-world applications in robotics, finance, and environmental engineering. Inference with large-scale models is challenging because relational structures deteriorate rapidly during inference with observations. The main contribution of this paper is an efficient relational variational inference algorithm that factors largescale probability models into simpler variational models, composed of mixtures of iid (Bernoulli) random variables. The algorithm takes probability relational models of largescale hybrid systems and converts them to a close-to-optimal variational models. Then, it efficiently calculates marginal probabilities on the variational models by using a latent (or lifted) variable elimination or a lifted stochastic sampling. This inference is unique because it maintains the relational structure upon individual observations and during inference steps.

cs.LG↗

arXiv2012

Lifted Inference for Relational Continuous Models

Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper presents a new exact lifted inference algorithm for RCMs, thus it scales up to large models of real world applications. The algorithm applies to Relational Pairwise Models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it substantially improves the efficiency of lifted inference with variables of continuous domains. When a relational model has Gaussian potentials, it takes only linear-time compared to cubic time of previous methods. Second, it is the first exact inference algorithm which handles RCMs in a lifted way. The algorithm is illustrated over an example from econometrics. Experimental results show that our algorithm outperforms both a groundlevel inference algorithm and an algorithm built with previously-known lifted methods.

cs.AI↗