Deep Generative Models
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Deep generative models can be either generative or discriminative. Generative models directly model the joint distribution of inputs and outputs, while discriminative models directly model the conditional distribution of outputs given inputs. Common deep generative models include restricted Boltzmann machines, deep belief networks, variational autoencoders, generative adversarial networks, and deep convolutional generative adversarial networks. These models use different network architectures and training procedures to generate new examples that resemble samples from the training data distribution.
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Generative Adversarial Networks (GANs) are a class of deep learning models that are trained using an adversarial process. GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator tries to generate new samples from a latent space to fool the discriminator, while the discriminator tries to distinguish real samples from fake ones. GANs can learn complex high-dimensional distributions and have been applied to image generation, video generation, and other domains. However, training GANs is challenging due to issues like non-convergence and mode collapse. Recent work has explored techniques like minibatch discrimination, conditional GANs, and unrolled GANs to help address these training issues.
generalized_nbody_acs_2015_challacombe
1. The document discusses barriers to scaling electronic structure methods to large systems, such as the inability of sparse matrix multiplication kernels to access strong parallel scaling and entrenched data structures that limit innovation.
2. It proposes a fast, generic, and data local N-body solver approach using new mathematics that is not constrained by row-column data structures and allows a single programming model.
3. Key aspects of this approach include exploiting locality in higher dimensional product volumes through techniques like occlusion-culling, resolving identity iteratively to compress matrices by orders of magnitude, and developing optimized sparse matrix multiplication kernels.
4 high performance large-scale image recognition without normalization
1) A new state-of-the-art image recognition model achieves 86.5% top-1 accuracy on ImageNet without using batch normalization, which is typically used for training deep neural networks.
2) The proposed method uses adaptive gradient clipping to stabilize training without normalization. This allows training with larger batch sizes while maintaining performance compared to batch-normalized models.
3) Experimental results show the proposed normalizer-free models match or exceed the performance of equivalent batch-normalized models while training faster.
Introduction to deep learning
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
K-means and GMM
This document discusses clustering methods using the EM algorithm. It begins with an overview of machine learning and unsupervised learning. It then describes clustering, k-means clustering, and how k-means can be formulated as an optimization of a biconvex objective function solved via an iterative EM algorithm. The document goes on to describe mixture models and how the EM algorithm can be used to estimate the parameters of a Gaussian mixture model (GMM) via maximum likelihood.
High performance large-scale image recognition without normalization
High
Performance Large Scale Image
Recognition Without Normalization 논문 리뷰 입니다
리뷰에 이미지 처리팀 박동훈님이 리뷰 도와주셨습니다
문의 : tfkeras@kakao.com
Image-Based E-Commerce Product Discovery: A Deep Learning Case Study - Denis ...
1) Macy's is launching an image-based "More Like This" deep learning feature to provide similar product recommendations to customers on their website.
2) The feature implementation uses transfer learning with pretrained models like MobileNet that are fine-tuned using a triplet loss approach and indexed in Lucene to support fast nearest neighbor search.
3) Engineering decisions around model architecture, embeddings, feature fusion, distance metrics, and indexing were required to deploy the solution at scale for Macy's large product catalog.
Iiwas19 yamazaki slide
The slide of our research "Fast RankClus Algorithm via Dynamic Score Tracking on Bi-type Information Networks"
Dueling network architectures for deep reinforcement learning
1. The document proposes a dueling network architecture for deep reinforcement learning that separately estimates state value and state-dependent action advantages without extra supervision.
2. It introduces a dueling deep Q-network that uses a single network with two streams - one that produces a state value and the other that produces state-dependent action advantages, which are then combined to estimate the state-action value function.
3. Experiments on Atari games show that the dueling network outperforms traditional deep Q-networks, achieving better performance in both random starts and starts from human demonstrations.
Principles of soft computing-Associative memory networks
The document discusses various types of associative memory networks including auto-associative, hetero-associative, bidirectional associative memory (BAM), and Hopfield networks. It describes the architecture, training algorithms, and testing procedures for each type of network. The key points are: Auto-associative networks store and recall patterns using the same input and output vectors, while hetero-associative networks use different input and output vectors. BAM networks perform bidirectional retrieval of patterns. Hopfield networks are auto-associative single-layer recurrent networks that can converge to stable states representing stored patterns. Hebbian learning and energy functions are important concepts in analyzing the storage and recall capabilities of these associative memory networks.
Similar to Deep Generative Models (20)
PR-305: Exploring Simple Siamese Representation Learning
PR-305: Exploring Simple Siamese Representation Learning
Super resolution in deep learning era - Jaejun Yoo
Super resolution in deep learning era - Jaejun Yoo
4 high performance large-scale image recognition without normalization
4 high performance large-scale image recognition without normalization
High performance large-scale image recognition without normalization
High performance large-scale image recognition without normalization
Image-Based E-Commerce Product Discovery: A Deep Learning Case Study - Denis ...
Image-Based E-Commerce Product Discovery: A Deep Learning Case Study - Denis ...
Dueling network architectures for deep reinforcement learning
Dueling network architectures for deep reinforcement learning
Principles of soft computing-Associative memory networks
Principles of soft computing-Associative memory networks
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一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Deep Generative Models
- 2. Discriminative vs. Generative Learning Discriminative Learning Generative Learning Learn 𝑝(𝑦|𝑥) directly Model 𝑝 𝑦 , 𝑝 𝑥 𝑦 first, Then derive the posterior distribution: 𝑝 𝑦 𝑥 = 𝑝 𝑥 𝑦 𝑝(𝑦) 𝑝(𝑥) 2
- 3. Undirected Graph vs. Directed Graph Undirected Directed • Boltzmann Machines • Restricted Boltzmann Machines • Deep Boltzmann Machines • Sigmoid Belief Networks • Variational Autoencoders (VAE) • Generative Adversarial Networks (GAN) 3 a b c a b c Deep Belief Networks
- 4. Boltzmann Machines • Stochastic Recurrent Neural Network and Markov Random Field invented by Hinton and Sejnowski in 1985 • 𝑷 𝒙 = 𝐞𝐱𝐩(−𝑬 𝒙 ) 𝒁 > E(x): Energy function > Z: partition function where σ 𝑥 𝑃 𝑥 = 1 • Energy-based model: positive values all the time • Single visible layer and single hidden layer • Fully connected: not practical to implement 4
- 5. Restricted Boltzmann Machines • Dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling • 𝑷 𝐯 = 𝒗, 𝐡 = 𝒉 = 𝟏 𝒁 𝐞𝐱𝐩(−𝑬 𝒗, 𝒉 ) • Two layers like BMs • Building blocks of deep probabilistic models • Gibbs sampling with Contrastive Divergence (CD) or Persistent CD 5
- 6. Comparison btw BMs and RBMs Boltzmann Machines Restricted Boltzmann Machines v1 v2 v3 h1 h2 h3 h4 v1 v2 v3 h1 h2 h3 h4 6 𝑬 𝒗, 𝒉 = −𝒗 𝑻 𝑹𝒗 − 𝒉 𝑻 𝑺𝒉 − 𝒗 𝑻 𝑾𝒉 − 𝒃 𝑻 𝒗 − 𝒄 𝑻 𝒉 𝑬 𝒗, 𝒉 = −𝒃 𝑻 𝒗 − 𝒄 𝑻 𝒉 − 𝒗 𝑻 𝑾𝒉
- 7. Deep Belief Networks • Unsupervised • Small dataset • Stacked RBMs • Pre-train each RBM • Undirected + Directed 7 RBM Sigmoid Belief Net
- 8. • 𝑃 v, h1, h2, h3 = 𝑃 v h1 𝑃 h1 h2 𝑃(h2, h3) • 𝑃 v h1 = ς𝑖 𝑃(𝑣𝑖|h1) • 𝑃 h1 h2 = ς 𝑗 𝑃(ℎ𝑗 1 |h2 ) • 𝑃 h2 , h3 = 1 𝑍(𝑊3) exp(h2𝑇 𝑊3 h3 ) 8 RBM Sigmoid Belief Net ℎ1 ℎ2 ℎ3 v 𝑊3 𝑊2 𝑊1 Sigmoid Belief Net RBM
- 9. Limitations of DBN (By Ruslan Salakhutdinov) • Explaining away • Greedy layer-wise pre-training > no optimization over all layers • Approximation inference is feed-forward > no bottom-up and top-down 9 http://www.slideshare.net/zukun/p05-deep-boltzmann-machines-cvpr2012-deep-learning-methods-for-vision
- 10. Deep Boltzmann Machines • Unsupervised • Small dataset • Stacked RBMs • Pre-train each RBM • Undirected 10
- 11. • 𝑃 𝜃 v = 1 𝑍(𝜃) σh1,h2,h3 exp( v 𝑇 𝑊1h1 + h1𝑇 𝑊2h2 + h2𝑇 𝑊3h3) • 𝜃 = {𝑊1, 𝑊2, 𝑊3} • Bottom-up and Top-down: • 𝑃 ℎ𝑗 2 = 1|h1, h3 = 𝜎(σ 𝒌 𝑾 𝒌𝒋 𝟑 𝒉 𝒌 𝟑 + σ 𝒎 𝑾 𝒎𝒋 𝟐 𝒉 𝒎 𝟏 ) 11 ℎ1 ℎ2 ℎ3 v 𝑊3 𝑊2 𝑊1
- 12. Variational Autoencoders (VAE) 12 Encoder Decoderx x’ μ σ Z 𝑞(𝑧|𝑥) 𝑝(𝑥|𝑧) Sampling Reconstruct ℒ 𝑞 = −𝑫 𝑲𝑳 𝒒 𝒛 𝒙 ∥ 𝒑 𝒎𝒐𝒅𝒆𝒍 𝒛 + 𝔼 𝒛~𝒒 𝒛 𝒙 𝐥𝐨𝐠 𝒑 𝒎𝒐𝒅𝒆𝒍(𝒙|𝒛) 𝔃 ~ 𝓝(𝝁, 𝝈) http://www.slideshare.net/KazukiNitta/variational-autoencoder-68705109
- 13. Stochastic Gradient Variational Bayes (SGVB) Estimator 13 Encoder Decoderx x’ μ σ Z Back Prop. Feed Forward 𝝐 ~ 𝓝 𝟎, 𝑰 𝔃 = 𝝁 + 𝝐𝝈
- 15. Generative Adversarial Networks(GAN) 15 Data Sample Discriminator Generator Sample Noise Yes or No Generator https://ishmaelbelghazi.github.io/ALI
- 16. Convolutional Neural Networks 16 Image Classification
- 17. Deep Convolutional Generative Adversarial Networks (DCGAN) 17 https://openai.com/blog/generative-models/
- 18. Real Images vs. Generated images 18 http://kenkihara.com/projects/GAN.html