Search Results for author:
Found 24 papers, 11 papers with code
Neural Parameterization for Dynamic Human Head Editing
Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene.
TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing
Extensive experiments on face recognition show that TVConv reduces the computational cost by up to 3. 1x and improves the corresponding throughput by 2. 3x while maintaining a high accuracy compared to the depthwise convolution.
Deblur-NeRF: Neural Radiance Fields from Blurry Images
We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes.
Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation
As a result, LDA can produce inference which is highly sensitive to the specification of the number of subcommunities and often divides a single subcommunity into multiple artificial ones.
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image.
Ranked #7 on
Out-of-Distribution Detection
on CIFAR-10
SciConceptMiner: A system for large-scale scientific concept discovery
In this paper, we introduce a self-supervised end-to-end system, SciConceptMiner, for the automatic capture of emerging scientific concepts from both independent knowledge sources (semi-structured data) and academic publications (unstructured documents).
Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration
One solution to this problem is to model the click and the reading duration simultaneously under the multi-task learning (MTL) framework.
Unsupervised tree boosting for learning probability distributions
We propose an unsupervised tree boosting algorithm for inferring the underlying sampling distribution of an i. i. d.\ sample based on fitting additive tree ensembles in a fashion analogous to supervised tree boosting.
Stability property and Dirichlet problem for translating solitons
In this paper, we prove that the infimum of the mean curvature is zero for a translating solitons of hypersurface in $\re^{n+k}$.
Differential Geometry Analysis of PDEs translating solitons, stability, non-splitting, Dirichlet problem
Yamabe metrics, Fine solutions to the Yamabe flow, and local L1-stability
In this paper, we study the existence of complete Yamabe metric with zero scalar curvature on an n-dimensional complete Riemannian manifold $(M, g_0)$, $n\geq 3$.
Differential Geometry Analysis of PDEs 53C44, 53C20, 35Jxx, 35K50, 58J20
Two solutions to Kazdan-Warner's problem on surfaces
In this paper, we study the sign-changing Kazdan-Warner's problem on two dimensional closed Riemannian manifold with negative Euler number $\chi(M)<0$.
Differential Geometry Analysis of PDEs 53C20, 35J60, 58G99
Dirichlet-tree multinomial mixtures for clustering microbiome compositions
A common routine in microbiome research is to identify reproducible patterns in the population through unsupervised clustering of samples.
Methodology Applications
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Machine learning (ML) methods have been widely used in genomic studies.
Cryptography and Security
CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images
Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos.
Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images
An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
Multiscale Fisher's Independence Test for Multivariate Dependence
Identifying dependency in multivariate data is a common inference task that arises in numerous applications.
Bayesian graphical compositional regression for microbiome data
An important task in microbiome studies is to test the existence of and give characterization to differences in the microbiome composition across groups of samples.
Methodology Applications Computation
Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive Partitioning
Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images.
Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation
We introduce two techniques for incorporating these features in modeling related data samples using kernel mixtures.
Recursive partitioning and multi-scale modeling on conditional densities
We introduce a nonparametric prior on the conditional distribution of a (univariate or multivariate) response given a set of predictors.
Methodology Statistics Theory Computation Statistics Theory
Efficient functional ANOVA through wavelet-domain Markov groves
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model.
