Why are very few schools involved in deep learning research? Why are they still hooked on to Bayesian methods?

First, this question assumes that every university should have a "deep learning" person.  Deep learning is mostly used in vision (and to a lesser extent NLP), and many universities don't have such researchers, so they wouldn't have a deep learning researcher either.

One thing that people often forget is that academics have long careers (thanks to tenure, this is by design).  So if you hire a bunch of researchers now who do deep learning, they're going to be around for decades.  Academia tends to be conservative, so it's not going to stock up on deep learning researchers just because it's cool today.  If this were the norm, CS departments would be full of fuzzy logic researchers hired in the 90s.

There's nothing magical about deep learning.  It's one tool of many (including Bayesian methods, discriminative methods, etc.) you should have in your toolbox.  Departments try to hire bright people, not those who slavishly follow every fad. Obviously, there will be more of these people on faculties who do deep learning in the near future.  (If Facebook, Google, and Baidu don't all hire them first, that is.)

That said, there are lots of folks working in this area.  Of the schools mentioned in the question, Noah Smith at UW and Katrin Erk at Texas.  Other places (off the top of my head) that work in this area: UMass, JHU, Maryland, NYU, Montreal, Michigan, and TTI.  I'm more upset that Princeton and Caltech (where I did my PhD and undergrad) don't have professors in CS who do language research.  That's the bigger crime in my opinion, and is correlated with their lack of deep learning folks.

Blatant self-promotion ... Colorado has three folks working in this area: me, Mike Mozer, and Jim Martin.

  
Updated Mon. 11,170 views. Asked to answer by Nishant Prateek.
Cui Caihao, PhD Candidate in CS & IT

 
 
There is no conflict between these two methods,  deep learning and Bayesian methods are both useful Machine Learning Tools to solve the real problem in our life.  Deep learning allows computational model that are composed of multiple layer to learn representations of data with multiple level of abstraction, this is a automatic feature extractor which can save a lot of engineering skills and domain expertise.

Bayesian method is also used in some part of deep learning, like Bayesian Nets etc.  Some school may looks like that they haven't involved in deep learning research but actually they share the same knowledge base and philosophy in this area.  If one is good at Machine Learning or Statistical Learning, he will feel no pressure to do some research on Deep Learning.

Here is a  paper about deep learning published last month on nature  : Page on nature.com . The authors are so famous in the world right now and  my friend, if you met a guy doing research in AI or ML, and he told you that he had never heard one of them,  you have an obligation to wake him up, LOL~

Here is a reply from  Yann LeCun | Facebook

  
Written Mon. 1,362 views.
Jane Lee, Data mining for businesses and manage... (more)

2 upvotes by Haider Ali and Pss Srivignessh
 
 
I just wanna quote Yann Lecun's answer in Facebookhttps://www.facebook.com/yann.le... 
The key ideas are: first, there's no opposition between "deep" and "Bayesian". Second, it takes time to acquire skills and talents to be professional in deep learning research.
fw

  
Written 1am. 388 views.
 
 
There was a big hype in the 80s around what we call now "shallow" neural networks. I don't know why but bio-inspired models in artificial intelligence seem to follow a cycle of popularity-discontent, whereas pure statistical methods seem to be less hyped but more constant in popularity.

Anyway they are not so distant. The basic component of Hinton's Deep belief network is the restricted Boltzmann machine, which is a flavour of the Boltzmann machine, which is a probabilistic model.
You can always see the state of a neuron to be conditioned by the state of its inputs, statistically speaking. The whole network state can be described in a probabilistic fashion.

What is universally important for artificial intelligence is linear algebra (vector spaces), calculus (gradient descent), and probability theory (bayes). Be worried only when these topics are neglected... :)
Also, I really see graph theory as a common feature of all advanced models in AI.

Piero,
PhD quitter who still loves neural models

  
Written Mon. 662 views.
 
 
I'm actually quite disturbed by the current use of the term. It reminds me of all the "high level" stuff in the 1980s, what wasn't really high level in any particular absolute sense, just relatively high compared to what proceeded it. Now we have something being called "deep" just because it's a bit heavier than something else and "learning" just because it's a fashionable word to use. Why is everybody working toward a job in marketing these days?

Why are very few schools involved in deep learning research? Why are they still hooked on to Bayesian methods?的更多相关文章

  1. (转) Deep Learning Research Review Week 2: Reinforcement Learning

      Deep Learning Research Review Week 2: Reinforcement Learning 转载自: https://adeshpande3.github.io/ad ...

  2. (转)Deep Learning Research Review Week 1: Generative Adversarial Nets

    Adit Deshpande CS Undergrad at UCLA ('19) Blog About Resume Deep Learning Research Review Week 1: Ge ...

  3. 深度学习研究组Deep Learning Research Groups

    Deep Learning Research Groups Some labs and research groups that are actively working on deep learni ...

  4. [DEEP LEARNING An MIT Press book in preparation]Deep Learning for AI

    动人的DL我们有六个月的时间,积累了一定的经验,实验,也DL有了一些自己的想法和理解.曾经想扩大和加深DL相关方面的一些知识. 然后看到了一个MIT按有关的对出版物DL图书http://www.iro ...

  5. [C3] Andrew Ng - Neural Networks and Deep Learning

    About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep l ...

  6. 贝叶斯深度学习(bayesian deep learning)

      本文简单介绍什么是贝叶斯深度学习(bayesian deep learning),贝叶斯深度学习如何用来预测,贝叶斯深度学习和深度学习有什么区别.对于贝叶斯深度学习如何训练,本文只能大致给个介绍. ...

  7. Conclusions about Deep Learning with Python

     Conclusions about Deep Learning with Python  Last night, I start to learn the python for deep learn ...

  8. What are some good books/papers for learning deep learning?

    What's the most effective way to get started with deep learning?       29 Answers     Yoshua Bengio, ...

  9. 《Deep Learning》(深度学习)中文版 开发下载

    <Deep Learning>(深度学习)中文版开放下载   <Deep Learning>(深度学习)是一本皆在帮助学生和从业人员进入机器学习领域的教科书,以开源的形式免费在 ...

随机推荐

  1. 第八章 jQuery与Ajax应用

    Ajax(Asynchronous JavaScript and XML),异步JavaScript和XML,它实现的无刷新更新页面,能够进行异步提交. jQuery对Ajax进行了封装,最底层的是$ ...

  2. Unity3d,OnMouseDown()不执行的原因总结

    1.代码:看代码有没有附加上要点击的物体上: 2.碰撞:要点击的物体加了碰撞,位置大小都对:而且鼠标屏幕点击的点和它之间没有其他的碰撞遮挡(OnMouseDown()原理利用了射线): 3.相关的摄像 ...

  3. hiho 1182 : 欧拉路·三

    1182 : 欧拉路·三 这时题目中给的提示: 小Ho:是这样的,每次转动一个区域不是相当于原来数字去掉最左边一位,并在最后加上1或者0么. 于是我考虑对于"XYYY",它转动之后 ...

  4. js解析json读取List中的实体对象示例

    1.由后台action 传给前台是需要将map 转成json格式 复制代码代码如下: Map<String, List> resultMap: JSONObject json = JSON ...

  5. TSQL基础(四) - 日期处理

    日期类型-DateTime DateTime是sql中最常用的日期类型. 存储大小为:8个字节: 日期范围:1753-01-01到9999-12-31: 精确度:3.33毫秒: 常用的日期函数 Get ...

  6. JavaScript最佳实践:可维护性

    代码约定 一.可读性 代码缩进 包含注释 二.变量和函数命名 变量名应为名词如car或person 函数名应该以动词开始,如getName().返回布尔类型值的函数一般以is开头,如isEnable( ...

  7. 方法:Linux 下用JAVA获取CPU、内存、磁盘的系统资源信息

    CPU使用率: InputStream is = null; InputStreamReader isr = null; BufferedReader brStat = null; StringTok ...

  8. Oracle 常用命令

    一 管理用户 查询用户集合 select username from dba_users; A 查询某个用户是否存在 select username from dba_users where user ...

  9. Axure RP 各个版本中文版 汉化包 破解版 下载地址及注册码

    导读:Axure RP Pro是一个产品经理必备的交互原型设计工具,能够高效率制作产品原型,快速绘制线框图.流程图.网站架构图.示意图.HTML模版等.Axure RP已被一些大公司采用.Axure ...

  10. IDEA 2016.2.2激活地址(2016-08-22)

    http://idea.imsxm.com/ 2016/10/13  http://idea.iteblog.com/key.php