I have a data set with a document-summary pair and I want to train it on something like XLNet to do abstractive summarization. I’m not completely versed in machine learning to develop it from scratch and was wondering how I would go about this? Would it be something similar to a translation task?

I created a spark pipeline where the first stage is a custom transformer, which only filters data on a particular attribute for a column

class getPOST(Transformer): @keyword_only def __init__(self, inputCol=None, outputCol=None): super(getPOST, self).__init__() kwargs = self._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, inputCol=None, outputCol=None): kwargs = self._input_kwargs return self._set(**kwargs) def _transform(self, dataset): return dataset.filter(dataset.method=='POST') 

The model works great, I’m getting good performance, but when I go to save the model, I’m met with:

ValueError: ('Pipeline write will fail on this pipeline because stage %s of type %s is not MLWritable', 'getPOST_23cb579f79db', <class '__main__.getPOST'>) 

I’ve been reading up and I don’t think a transformer is the most applicable use in this case as I’m not appending any columns onto the dataset and not messing with any values or parameters that need declared, such as I found in this link. I can’t find other examples that allow you to filter out data in the Spark ML pipelines.

This is the last stage of a project I’m working on and I’d greatly appreciate any push in the right direction. Thank you for taking the time to read this!

Hello everybody!

In this clearly philosophical paper, we express our opinion on the neural network’s understanding issue. We were motivated by Timothy P. Lillicrap and Konrad P. Kording paper https://arxiv.org/pdf/1907.06374.pdf and we disagree with many statements presented in this work. We propose understanding requirements and based on them, describes the state when we can say that we understand the neural networks.

Abstract: We can say that we understand neural networks then and only then if you will come to me and say that the best model ever for some task has a 100 layers, and I will answer “No! 101 layers model is the best!”.

Some highlights:

2.05. The more emergence in the system, the more heuristics it requires to deal with it.

2.07. Knowledge destroys heuristics.

2.13. The more superficial knowledge of the phenomena, the more irrational it is, and the easier it is to make mistakes.

3.09. A description is a specification of the causal relationship between a set of system facts noted as simple facts

3.16. First requirement: system described in the all possibly abstract levels. (We call it descriptive completeness).

3.17. Second requirement: description of the system of the abstract descriptions reduces emergence to zero. (We call it descriptive emergence).

3.18. The difficulty of understanding is in finding all abstract levels of the system.

3.19. If after describing the system by one or more abstract levels, emergence persists in the system, it is necessary to get more abstract level descriptions, to bridge the gap.

3.20. The greater the gap between abstract description levels, the bigger the emergence between facts of these levels.

3.21. Third requirement: proposed description does not contradict to the new coming facts. (We call it descriptive power)

3.22. We cannot evaluate the description if we have no facts beyond the scope of this description
3.23. If a satisfied lower level requirement does not lead to satisfying a higher requirement, this description is not true.

5.200. To understand the code of neural network, doesn’t mean to understand the neural network.

5.204. All abstract levels of code are known. We have a zero emergence between different abstract levels of program code, from low to high.

5.205. If we made a mistake in the code, we can always describe how these mistakes affect the other processes, and why it’s a mistake, there is no emergence in the system.

5.206. If we made a mistake in deep neural network architecture, we can’t always describe how these mistakes affect the other processes, and why it’s a mistake.

And so on…

Full paper: https://philpapers.org/archive/IERWDI.pdf

The problem of “understanding” is very hotly debated in the machine learning community in the last time. https://www.reddit.com/r/MachineLearning/comments/chm065/d_why_ml_community_so_negatively_opposed_to/. https://www.reddit.com/r/MachineLearning/comments/do6xx5/d_what_does_it_mean_for_a_machine_to_understand/

We hope that our article will draw even more attention to this problem.

7th International Conference on Artificial Intelligence and Applications (AIAPP 2020)

January 25 ~ 26, 2020, Zurich, Switzerland

https://cosit2020.org/aiapp/index.html

Scope & Topics

7th International Conference on Artificial Intelligence and Applications (AIAPP 2020) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence forum for presenting new advances and research results in the fields of Artificial Intelligence.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to :

Topics of interest include, but are not limited to, the following:

· AI Algorithms

· Artificial Intelligence Tools & Applications

· Automatic Control

· Bioinformatics

· Natural Language Processing

· CAD Design & Testing

· Computer Vision and Speech Understanding

· Data Mining and Machine Learning Tools

· Fuzzy Logic

· Heuristic and AI Planning Strategies and Tools

· Computational Theories of Learning

· Hybrid Intelligent Systems

· Information Retrieval

· Intelligent System Architectures

· Knowledge Representation

· Knowledge-based Systems

· Mechatronics

· Multimedia & Cognitive Informatics

· Neural Networks

· Parallel Processing

· Pattern Recognition

· Pervasive Computing and Ambient Intelligence

· Programming Languages

· Reasoning and Evolution

· Recent Trends and Developments

· Robotics

· Semantic Web Techniques and Technologies

· Soft Computing Theory and Applications

· Software and Hardware Architectures

· Web Intelligence Applications and Search

Paper Submission

Authors are invited to submit papers through the conference Submission system

Here’s where you can reach us : [aiapp@cosit2020.org](mailto:aiapp@cosit2020.org) or [aiapp_secretary@yahoo.com](mailto:aiapp_secretary@yahoo.com)

Hello again. Like I mentioned in another post I’m currently working on my capstone project proposal and I was interesting in researching deep learning models and do something about that. I appreciate the feedback I got regarding the stock price prediction proposal and in fact I got more or less the same feedback from my teachers.

I came up with the following project proposal and I was wondering if you can give me any further feedback about it, since my teachers have been stonewalling me on this matter: I want to use advanced sports statistics (basically FootballOutsiders’ statistics) along with traditional statistics and RNN to predict how many fantasy football points each player is going to score for a given week. I’m focusing on daily fantasy football since it’s easier to do without great point fluctuations that I would get trying to predict their season-long performance (due to injury, scheme change, coaching changes, etc). Then I would apply an optimization algorithm to come up with an optimal team that meets the salary cap constraint that most DFF competitions have.

I’ve researched a bit and didn’t find many studies dealing with this subject, and those that did focused on more basic models like linear regression.

Do you think it’s a worthwhile subject to work on?

Thank you

Hey all,

Just wanted to share this awesome resource for anyone learning or working with machine learning or deep learning. Gradient Community Notebooks from Paperspace offers a free GPU you can use for ML/DL projects with Jupyter notebooks. With containers that come with everything pre-installed (like fast.ai, PyTorch, TensorFlow, and Keras), this is basically the lowest barrier to entry in addition to being totally free.

They also have an ML Showcase where you can use runnable templates of different ML projects and models. I hope this can help someone out with their projects 🙂

Comment

Hi,

I am an undergrad student at Tier 1 college in India, with a major in Material Science. I want to apply for this year’s residency program and was wondering how should I write my cover letter, since I think that being from a non-CS major can be harmful for my application. I do research experience though, and have authored 2 papers, 1 submitted to a journal and the other accepted as NeurIPS workshop paper.

I was also wondering which location to apply for, like MV, Seattle, Cambridge or NYC! Or is it, that I apply for 1 location and it is automatically considered for all the locations?

Thanks!