Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

With the flourish of differentiable neural architecture search (NAS), automatically searching latency-constrained architectures gives a new perspective to reduce human labor and expertise. However, the searched architectures are usually suboptimal in accuracy and may have large jitters around the target latency. In this paper, we rethink three freedoms of differentiable NAS, i.e. operation-level, depth-level and width-level, and propose a novel method, named Three-Freedom NAS (TF-NAS), to achieve both good classification accuracy and precise latency constraint. For the operation-level, we present a bi-sampling search algorithm to moderate the operation collapse. For the depth-level, we introduce a sink-connecting search space to ensure the mutual exclusion between skip and other candidate operations, as well as eliminate the architecture redundancy. For the width-level, we propose an elasticity-scaling strategy that achieves precise latency constraint in a progressively fine-grained manner. Experiments on ImageNet demonstrate the effectiveness of TF-NAS. Particularly, our searched TF-NAS-A obtains 76.9% top-1 accuracy, achieving state-of-the-art results with less latency. The total search time is only 1.8 days on 1 Titan RTX GPU. Code is available at this https URL.

TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search

This paper presents a new approach for real-world gender recognition, where images are captured under uncontrolled environments with various poses, illuminations and expressions. While a large number of gender recognition methods have been introduced in recent years, most of them describe each image in a single feature space or simple combination of multiple individual spaces, which can not be powerful enough to alleviate the noise in real-world scenarios. To address this, we propose exploring multiple order local binary patterns (MOLBP) as features for learning, and develop a localized multi-boost learning (LMBL) algorithm to combine the different features for classification. Experimental results show that the proposed algorithm outperforms state-of-the-art methods in two real-world datasets.

Real-world gender recognition using multi-order LBP and localized multi-boost learning

Deep learning based face parsing methods have attained state-of-the-art performance in recent years. Their superior performance heavily depends on the large-scale annotated training data. However, it is expensive and time-consuming to construct a large-scale pixel-level manually annotated dataset for face parsing. To alleviate this issue, we propose a novel Dual-Structure Disentangling Variational Generation (D2VG) network. Benefiting from the interpretable factorized latent disentanglement in VAE, D2VG can learn a joint structural distribution of facial image and its corresponding parsing map. Owing to these, it can synthesize large-scale paired face images and parsing maps from a standard Gaussian distribution. Then, we adopt both manually annotated and synthesized data to train a face parsing model in a supervised way. Since there are inaccurate pixel-level labels in synthesized parsing maps, we introduce a coarseness-tolerant learning algorithm, to effectively handle these noisy or uncertain labels. In this way, we can significantly boost the performance of face parsing. Extensive quantitative and qualitative results on HELEN, CelebAMask-HQ and LaPa demonstrate the superiority of our methods.

Dual-Structure Disentangling Variational Generation for Data-Limited Face Parsing

Deep momentum uncertainty hashing

A capsule is a group of neurons whose activity vector models different properties of the same entity. This paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity, making it possible to integrate image analysis and image synthesis into a unified framework. Variational capsules model an image as a composition of entities in a probabilistic model. Different capsules' divergence with a specific prior distribution represents the presence of different entities, which can be applied in image analysis tasks such as classification. In addition, variational capsules encode multiple entities in a semantically-disentangling way. Diverse instantiations of capsules are related to various properties of the same entity, making it easy to generate diverse samples with fine-grained semantic attributes. Extensive experiments demonstrate that deep networks designed with variational capsules can not only achieve promising performance on image analysis tasks (including image classification and attribute prediction) but can also improve the diversity and controllability of image synthesis.

Ran He

Papers

TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search

Real-world gender recognition using multi-order LBP and localized multi-boost learning

Dual-Structure Disentangling Variational Generation for Data-Limited Face Parsing

Deep momentum uncertainty hashing

Variational Capsules for Image Analysis and Synthesis.