arXiv subjects

Jiawei Zhang

Publications and source records attributed to Jiawei Zhang.

At least 361 records · Page 20Linked to original sources

arXiv2019

Fermi surface complexity, effective mass, and conduction band alignment in n-type thermoelectric Mg3Sb2-xBix from first principles calculations

Using first principles calculations, we study the conduction band alignment, effective mass, and Fermi surface complexity factor of n-type Mg3Sb2-xBix (x = 0, 1, and 2) from the full ab initio band structure. We find that with increasing the Bi content the K and M band minima moves away from the conduction band minimum CB1 while the singly-degenerate $Γ$ band minimum shifts rapidly downward and approaches the conduction band minimum. But the favorable six-fold degenerate CB1 band minimum keeps dominating the conduction band minimum and there is no band crossing between the $Γ$ and CB1 band minima. In addition, we show that the connection of the CB1 carrier pockets with the energy level close to the band minimum M can strongly enhance the carrier pocket anisotropy and Fermi surface complexity factor, which is likely the electronic origin for the local maximum in the theoretical power factor. Our calculations also show that the band gap, density of states effective mass, Seebeck coefficient, and Fermi surface complexity factor decrease with increasing the Bi content, which is unfavorable to the electrical transport. However, reducing the conductivity effective mass with increasing the Bi content is beneficial to the electrical transport by improving carrier mobility and weighted mobility as long as the detrimental bipolar effect is insignificant. As a result, in comparison with n-type Mg3Sb2, n-type Mg3SbBi shows higher power factors and a much lower optimal carrier concentration for the theoretical power factor at 300 K, which can be easily achieved by the experiment.

cond-mat.mtrl-sci↗

arXiv2019

A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM

Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gradient descent (GD) is impractical for processing big data due to its $O(n^3)$ complexity. Many sophisticated global or local approximation models, for instance, sparse GP, distributed GP, have been proposed to address such complexity issue. In this paper, we propose two novel and general-purpose GP hyper-parameter training schemes (GPCV-ADMM) by replacing ML with cross-validation (CV) as the fitting criterion and replacing GD with a non-linearly constrained alternating direction method of multipliers (ADMM) as the optimization method. The proposed schemes are of $O(n^2)$ complexity for any covariance matrix without special structure. We conduct various experiments based on both synthetic and real data sets, wherein the proposed schemes show excellent performance in terms of convergence, hyper-parameter estimation accuracy, and computational time in comparison with the traditional ML based routines given in the GPML toolbox.

stat.ML↗

arXiv2019

Secrets of the Brain: An Introduction to the Brain Anatomical Structure and Biological Function

In this paper, we will provide an introduction to the brain structure and function. Brain is an astonishing living organ inside our heads, weighing about 1.5kg, consisting of billions of tiny cells. The brain enables us to sense the world around us (to touch, to smell, to see and to hear, etc.), to think and to respond to the world as well. The main obstacles that prevent us from creating a machine which can behavior like real-world creatures are due to our limited knowledge about the brain in both its structure and its function. In this paper, we will focus introducing the brain anatomical structure and biological function, as well as its surrounding sensory systems. Many of the materials used in this paper are from wikipedia and several other neuroscience introductory articles, which will be properly cited in this article. This is the first of the three tutorial articles about the brain (the other two are [26] and [27]). In the follow-up two articles, we will further introduce the low-level composition basis structures (e.g., neuron, synapse and action potential) and the high-level cognitive functions (e.g., consciousness, attention, learning and memory) of the brain, respectively.

q-bio.NC↗

arXiv2019

Basic Neural Units of the Brain: Neurons, Synapses and Action Potential

As a follow-up tutorial article of [29], in this paper, we will introduce the basic compositional units of the human brain, which will further illustrate the cell-level bio-structure of the brain. On average, the human brain contains about 100 billion neurons and many more neuroglia which serve to support and protect the neurons. Each neuron may be connected to up to 10,000 other neurons, passing signals to each other via as many as 1,000 trillion synapses. In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another neuron or to the target effector cell. Such signals will be accumulated as the membrane potential of the neurons, and it will trigger and pass the signal pulse (i.e., action potential) to other neurons when the membrane potential is greater than a precisely defined threshold voltage. To be more specific, in this paper, we will talk about the neurons, synapses and the action potential concepts in detail. Many of the materials used in this paper are from wikipedia and several other neuroscience introductory articles, which will be properly cited in this paper. This is the second of the three tutorial articles about the brain (the other two are [29] and [28]). The readers are suggested to read the previous tutorial article [29] to get more background information about the brain structure and functions prior to reading this paper.

q-bio.NC↗

arXiv2019

Cognitive Functions of the Brain: Perception, Attention and Memory

This is a follow-up tutorial article of [17] and [16], in this paper, we will introduce several important cognitive functions of the brain. Brain cognitive functions are the mental processes that allow us to receive, select, store, transform, develop, and recover information that we've received from external stimuli. This process allows us to understand and to relate to the world more effectively. Cognitive functions are brain-based skills we need to carry out any task from the simplest to the most complex. They are related with the mechanisms of how we learn, remember, problem-solve, and pay attention, etc. To be more specific, in this paper, we will talk about the perception, attention and memory functions of the human brain. Several other brain cognitive functions, e.g., arousal, decision making, natural language, motor coordination, planning, problem solving and thinking, will be added to this paper in the later versions, respectively. Many of the materials used in this paper are from wikipedia and several other neuroscience introductory articles, which will be properly cited in this paper. This is the last of the three tutorial articles about the brain. The readers are suggested to read this paper after the previous two tutorial articles on brain structure and functions [17] as well as the brain basic neural units [16].

q-bio.NC↗

arXiv2019

Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae

Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology. This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces. We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.

cs.CL↗

arXiv2019

Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of NP-GLM in solving continuous-time relationship prediction problem vis-a-vis competitive baselines

cs.SI↗

arXiv2019

Derivative-Free Global Optimization Algorithms: Bayesian Method and Lipschitzian Approaches

In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning model training is mostly based on the back propagation algorithm, which updates the model variables layers by layers with the gradient descent algorithm or its variants. However, the objective functions of deep learning models to be optimized are usually non-convex and the gradient descent algorithms based on the first-order derivative can get stuck into the local optima very easily. To resolve such a problem, various local or global optimization algorithms have been proposed, which can help improve the training of deep learning models greatly. The representative examples include the Bayesian methods, Shubert-Piyavskii algorithm, Direct, LIPO, MCS, GA, SCE, DE, PSO, ES, CMA-ES, hill climbing and simulated annealing, etc. One part of these algorithms will be introduced in this paper (including the Bayesian method and Lipschitzian approaches, e.g., Shubert-Piyavskii algorithm, Direct, LIPO and MCS), and the remaining algorithms (including the population based optimization algorithms, e.g., GA, SCE, DE, PSO, ES and CMA-ES, and random search algorithms, e.g., hill climbing and simulated annealing) will be introduced in the follow-up paper [18] in detail.

cs.LG↗

arXiv2019

Derivative-Free Global Optimization Algorithms: Population based Methods and Random Search Approaches

In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning model training is mostly based on the back propagation algorithm, which updates the model variables layers by layers with the gradient descent algorithm or its variants. However, the objective functions of deep learning models to be optimized are usually non-convex and the gradient descent algorithms based on the first-order derivative can get stuck into the local optima very easily. To resolve such a problem, various local or global optimization algorithms have been proposed, which can help improve the training of deep learning models greatly. The representative examples include the Bayesian methods, Shubert-Piyavskii algorithm, Direct, LIPO, MCS, GA, SCE, DE, PSO, ES, CMA-ES, hill climbing and simulated annealing, etc. This is a follow-up paper of [18], and we will introduce the population based optimization algorithms, e.g., GA, SCE, DE, PSO, ES and CMA-ES, and random search algorithms, e.g., hill climbing and simulated annealing, in this paper. For the introduction to the other derivative-free optimization algorithms, please refer to [18] for more information.

cs.LG↗

arXiv2019

DAVANet: Stereo Deblurring with View Aggregation

Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles. However, they also suffer from blurry images in dynamic scenes which leads to visual discomfort and hampers further image processing. Previous works have succeeded in monocular deblurring, yet there are few studies on deblurring for stereoscopic images. By exploiting the two-view nature of stereo images, we propose a novel stereo image deblurring network with Depth Awareness and View Aggregation, named DAVANet. In our proposed network, 3D scene cues from the depth and varying information from two views are incorporated, which help to remove complex spatially-varying blur in dynamic scenes. Specifically, with our proposed fusion network, we integrate the bidirectional disparities estimation and deblurring into a unified framework. Moreover, we present a large-scale multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp stereo image pairs from 135 diverse sequences and their corresponding bidirectional disparities. The experimental results on our dataset demonstrate that DAVANet outperforms state-of-the-art methods in terms of accuracy, speed, and model size.

cs.CV↗

arXiv2019

Gradient Descent based Optimization Algorithms for Deep Learning Models Training

In this paper, we aim at providing an introduction to the gradient descent based optimization algorithms for learning deep neural network models. Deep learning models involving multiple nonlinear projection layers are very challenging to train. Nowadays, most of the deep learning model training still relies on the back propagation algorithm actually. In back propagation, the model variables will be updated iteratively until convergence with gradient descent based optimization algorithms. Besides the conventional vanilla gradient descent algorithm, many gradient descent variants have also been proposed in recent years to improve the learning performance, including Momentum, Adagrad, Adam, Gadam, etc., which will all be introduced in this paper respectively.

cs.LG↗

arXiv2019

GADAM: Genetic-Evolutionary ADAM for Deep Neural Network Optimization

Deep neural network learning can be formulated as a non-convex optimization problem. Existing optimization algorithms, e.g., Adam, can learn the models fast, but may get stuck in local optima easily. In this paper, we introduce a novel optimization algorithm, namely GADAM (Genetic-Evolutionary Adam). GADAM learns deep neural network models based on a number of unit models generations by generations: it trains the unit models with Adam, and evolves them to the new generations with genetic algorithm. We will show that GADAM can effectively jump out of the local optima in the learning process to obtain better solutions, and prove that GADAM can also achieve a very fast convergence. Extensive experiments have been done on various benchmark datasets, and the learning results will demonstrate the effectiveness and efficiency of the GADAM algorithm.

cs.LG↗

arXiv2018

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.

cs.CV↗

arXiv2018

Data-driven Blockbuster Planning on Online Movie Knowledge Library

In the era of big data, logistic planning can be made data-driven to take advantage of accumulated knowledge in the past. While in the movie industry, movie planning can also exploit the existing online movie knowledge library to achieve better results. However, it is ineffective to solely rely on conventional heuristics for movie planning, due to a large number of existing movies and various real-world factors that contribute to the success of each movie, such as the movie genre, available budget, production team (involving actor, actress, director, and writer), etc. In this paper, we study a "Blockbuster Planning" (BP) problem to learn from previous movies and plan for low budget yet high return new movies in a totally data-driven fashion. After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper. From the investment perspective, BigMovie maximizes the estimated gross of the planned movies with a given budget. It is able to accurately estimate the movie gross with a 0.26 mean absolute percentage error (and 0.16 for budget). Meanwhile, from the production team's perspective, BigMovie is able to formulate an optimized team with people/movie genres that team members are acquainted with. Historical collaboration records are utilized to estimate acquaintance scores of movie configuration factors via an acquaintance tensor. We formulate the BP problem as a non-linear binary programming problem and prove its NP-hardness. To solve it in polynomial time, BigMovie relaxes the hard binary constraints and addresses the BP problem as a cubic programming problem. Extensive experiments conducted on IMDB movie database demonstrate the capability of BigMovie for an effective data-driven blockbuster planning.

cs.AI↗

arXiv2018

A Self-Organizing Tensor Architecture for Multi-View Clustering

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-view correlations are only considered at the view-level. These approaches, however, fail to explore the explicit correlations between features across multiple views. In this paper, we introduce a tensor-based approach to incorporate the higher-order interactions among multiple views as a tensor structure. Specifically, we propose a multi-linear multi-view clustering (MMC) method that can efficiently explore the full-order structural information among all views and reveal the underlying subspace structure embedded within the tensor. Extensive experiments on real-world datasets demonstrate that our proposed MMC algorithm clearly outperforms other related state-of-the-art methods.

cs.LG↗

arXiv2018

Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks

We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from traditional super-resolution formulation, the low-resolution input is further degraded by noises and blurring. This complicated setting makes supervised learning and accurate kernel estimation impossible. To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-to-image translation applications. With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps. First, the noisy and blurry input is mapped to a noise-free low-resolution space. Then the intermediate image is up-sampled with a pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end manner to get the high-resolution output. Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the state-of-the-art supervised models.

cs.CV↗

arXiv2018

Spectral Collaborative Filtering

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the \textit{cold-start} problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the \textit{spectral domain}, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the \textit{spectral domains} of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly outperforms state-of-the-art models. Code and data are available at \url{https://github.com/lzheng21/SpectralCF}.

cs.IR↗

arXiv2018

Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.

cs.LG↗