39 research outputs found
Transfer nonnegative matrix factorization for image representation
Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different basis vector space and represented in different coding vector space, which may lead to low representation fidelity. In this paper, we investigate how to extend NMF to cross-domain scenario. We accomplish this goal through TNMF - a novel semi-supervised transfer learning approach. Specifically, we aim to minimize the distribution divergence between labeled and unlabeled images, and incorporate this criterion into the objective function of NMF to construct new robust representations. Experiments show that TNMF outperforms state-of-the-art methods on real dataset
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Graph convolutional networks (GCNs) are \emph{discriminative models} that
directly model the class posterior for semi-supervised
classification of graph data. While being effective, as a representation
learning approach, the node representations extracted from a GCN often miss
useful information for effective clustering, because the objectives are
different. In this work, we design normalizing flows that replace GCN layers,
leading to a \emph{generative model} that models both the class conditional
likelihood and the class prior . The resulting neural
network, GC-Flow, retains the graph convolution operations while being equipped
with a Gaussian mixture representation space. It enjoys two benefits: it not
only maintains the predictive power of GCN, but also produces well-separated
clusters, due to the structuring of the representation space. We demonstrate
these benefits on a variety of benchmark data sets. Moreover, we show that
additional parameterization, such as that on the adjacency matrix used for
graph convolutions, yields additional improvement in clustering.Comment: ICML 2023. Code is available at https://github.com/xztcwang/GCFlo
DyExplainer: Explainable Dynamic Graph Neural Networks
Graph Neural Networks (GNNs) resurge as a trending research subject owing to
their impressive ability to capture representations from graph-structured data.
However, the black-box nature of GNNs presents a significant challenge in terms
of comprehending and trusting these models, thereby limiting their practical
applications in mission-critical scenarios. Although there has been substantial
progress in the field of explaining GNNs in recent years, the majority of these
studies are centered on static graphs, leaving the explanation of dynamic GNNs
largely unexplored. Dynamic GNNs, with their ever-evolving graph structures,
pose a unique challenge and require additional efforts to effectively capture
temporal dependencies and structural relationships. To address this challenge,
we present DyExplainer, a novel approach to explaining dynamic GNNs on the fly.
DyExplainer trains a dynamic GNN backbone to extract representations of the
graph at each snapshot, while simultaneously exploring structural relationships
and temporal dependencies through a sparse attention technique. To preserve the
desired properties of the explanation, such as structural consistency and
temporal continuity, we augment our approach with contrastive learning
techniques to provide priori-guided regularization. To model longer-term
temporal dependencies, we develop a buffer-based live-updating scheme for
training. The results of our extensive experiments on various datasets
demonstrate the superiority of DyExplainer, not only providing faithful
explainability of the model predictions but also significantly improving the
model prediction accuracy, as evidenced in the link prediction task.Comment: 9 page
Efficient ab initio many-body calculations based on sparse modeling of Matsubara Green's function
This lecture note reviews recently proposed sparse-modeling approaches for
efficient ab initio many-body calculations based on the data compression of
Green's functions. The sparse-modeling techniques are based on a compact
orthogonal basis representation, intermediate representation (IR) basis
functions, for imaginary-time and Matsubara Green's functions. A sparse
sampling method based on the IR basis enables solving diagrammatic equations
efficiently. We describe the basic properties of the IR basis, the sparse
sampling method and its applications to ab initio calculations based on the GW
approximation and the Migdal-Eliashberg theory. We also describe a numerical
library for the IR basis and the sparse sampling method, irbasis, and provide
its sample codes. This lecture note follows the Japanese review article [H.
Shinaoka et al., Solid State Physics 56(6), 301 (2021)].Comment: 26 pages, 10 figure
First-principles study of oxygen vacancy defects in orthorhombic HfZrO/SiO/Si gate stack
The gate defect of the ferroelectric HfO-based Si field-effect transistor
(Si FeFET) plays a dominant role in its reliability issue. The first-principles
calculations are an effective method for the atomic-scale understanding of gate
defects. However, the first-principles study on the defects of FeFET gate
stacks, i.e., metal/orthorhombic-HfZrO/SiO/Si
structure, has not been reported so far. The key challenge is the construction
of metal/orthorhombic-HfZrO/SiO/Si gate stack models.
Here, we use the HfZrO(130) high-index crystal face as the
orthorhombic ferroelectric layer and construct a robust atomic structure of the
orthorhombic-HfZrO/SiO/Si gate stack without any gap
states. Its high structural stability is ascribed to the insulated interface.
The calculated band offsets show that this gate structure is of the type-I band
alignment. Furthermore, the formation energies and charge transition levels
(CTLs) of defects reveal that the oxygen vacancy defects are more favorable to
form compared with other defects such as oxygen interstitial and Hf/Zr vacancy,
and their CTLs are mainly localized near the Si conduction band minimum and
valence band maximum, in agreement with the reported experimental results. The
oxygen vacancy defects are responsible for charge trapping/de-trapping behavior
in Si FeFET. This work provides an insight into gate defects and paves the way
to carry out the first-principles study of ferroelectric HfO-based Si
FeFET.Comment: 18 pages, 5 figure
Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality
The interpolation, prediction, and feature analysis of fine-gained air
quality are three important topics in the area of urban air computing. The
solutions to these topics can provide extremely useful information to support
air pollution control, and consequently generate great societal and technical
impacts. Most of the existing work solves the three problems separately by
different models. In this paper, we propose a general and effective approach to
solve the three problems in one model called the Deep Air Learning (DAL). The
main idea of DAL lies in embedding feature selection and semi-supervised
learning in different layers of the deep learning network. The proposed
approach utilizes the information pertaining to the unlabeled spatio-temporal
data to improve the performance of the interpolation and the prediction, and
performs feature selection and association analysis to reveal the main relevant
features to the variation of the air quality. We evaluate our approach with
extensive experiments based on real data sources obtained in Beijing, China.
Experiments show that DAL is superior to the peer models from the recent
literature when solving the topics of interpolation, prediction, and feature
analysis of fine-gained air quality