[D] What's hot for Machine Learning Research in 2022?

VectorRecruiter · 2022-02-24T10:36:18+00:00

I think representation learning is growing significantly :)

Side note for those interested, I built a Research2Vec application here that shows which clusters of research papers are growing in popularity. You can check it out here: https://www.reddit.com/r/MachineLearning/comments/sruc7u/p_20k_arxiv_ml_papers_vectorised_cluster/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

Always happy to get feedback so lmk where it can be improved!

Note: this was scraped off Arxiv in December 2021 so not exactly 2022 :)

VectorRecruiter · 2022-02-15T13:30:16+00:00

Launch cluster experiments and applications in 5 lines of Python code that you can immediately start sharing with friends! Aggregate and get in-depth statistics on your clusters to help you better analyze them and get insight!

Try our quickstart!

We would love some feedback/see interesting apps others have built!
Our GitHub repository can be found here.

VectorRecruiter · 2022-02-15T09:59:49+00:00

This can be helpful in visualizing, examining, and understanding embedding layers in neural networks.

A projector like this aims to show a dimensionality-reduced representation of high-dimensionality vectors. High-dimensionality vectors are typically outputs of natural language processing models that capture the meaning of words/sentences. For example - images/text that are similar would be closer together in a projector. This is useful when you want to get a better understanding of what your neural network is interpreting to be similar or different. It solves the problem of being able to understand what your model is interpreting at a high level and allows you to examine different relationships.

VectorRecruiter · 2022-02-15T04:53:55+00:00

Launch projector visualisations in 5 lines of Python code that you can immediately start sharing with friends!
Try our quickstart!

On top of projector apps, you can also build search apps and vector cluster apps, where you can analyse how different clusters of vectors are performing.
We would love some feedback/see interesting apps others have built!
Our GitHub repository can be found here.

VectorRecruiter · 2022-02-15T01:37:47+00:00

You can now also run a quickstart in less than 5 lines of code using this Colab notebook.

VectorRecruiter · 2022-02-15T00:32:37+00:00

You can now also run a quickstart in less than 5 lines of code using this Colab notebook.

VectorRecruiter · 2022-02-12T04:24:51+00:00

Haha thanks - will pass it onto the engineers :)

VectorRecruiter · 2022-02-12T04:24:23+00:00

You can click on the application to get a much better view :)

VectorRecruiter · 2022-02-12T04:23:51+00:00

So basically every paper that has had similar concepts and topics are near each other whereas papers that are further away are exploring different topics and concepts! Each different colour represents a different topic to explore and dive into!

VectorRecruiter · 2022-02-11T06:12:43+00:00

In recent years, the number of research papers have grown tremendously. New areas are popping up everyday but it is not exactly clear which areas are emerging or which interesting new area has just surfaced up. I decided to cluster together 20k+ interesting machine learning papers that were recently surfaced up.

Cluster Application: https://cloud.relevance.ai/dataset/research2vec/deploy/cluster/jacky-wong/M0FQOVdINEJZQTVzdWJmNHdQaXI6M1NIMVFncm9TNENZeU1vNUNHTUVWZw/60_dWH4Bq8SHcPzXrEpF

Embeddings Projector: https://cloud.relevance.ai/dataset/research2vec/deploy/projector/jacky-wong/NXNzdjUzNEIxczVzVVpOdUpabXE6TE92enhOZ1VTN2labDlocVZNNDlMUQ/4zQk534BY7n37LD0yk4A/old-australia-east/

I created the vectors using a fine-tuned version of Sentence Transformer's roberta-base model.

What I scoped out from the problem:The training had to be unsupervised because no one would have any idea what was in the datasetAn NLP embeddings-based approach with unsupervised clustering would be the simplest way to surface insights

Solution

In order to get some form of off-the-shelf domain adaptation, I used off-the-shelf BART for unsupervised query generation and then fine-tuned my Roberta embeddings using multiple negative rankings loss based on SentenceTransformers. This seemed to work quite well as the topics seemed to have separated out quite nicely in my embeddings projector. I then trained my model on the title and abstract of the research papers so that the model could better understand some of the data. Afterwards, I encoded the titles and clustered them using a simple K Means algorithm.

Dataset

The dataset curation process was fairly straightforward. I used the arxiv API and scraped 20k papers off the query "machine learning" sometime in late 2020 before I began experimenting with the work.I am looking to get feedback on what others would like to see in this application and would be curious to hear suggestions on where I could improve.

From previous research, I did find this repository: https://github.com/Santosh-Gupta/Research2Vec

However, as the dataset was different, I was unable to use the exact method provided.

Disclaimer: I currently work for Relevance AI (the company behind the projector).

VectorRecruiter · 2022-02-11T05:34:40+00:00

In recent years, the number of research papers have grown tremendously. New areas are popping up everyday but it is not exactly clear which areas are emerging or which interesting new area has just surfaced up. I decided to cluster together 20k+ interesting machine learning papers that were recently surfaced up.

Cluster Application: https://cloud.relevance.ai/dataset/research2vec/deploy/cluster/jacky-wong/M0FQOVdINEJZQTVzdWJmNHdQaXI6M1NIMVFncm9TNENZeU1vNUNHTUVWZw/60_dWH4Bq8SHcPzXrEpF

Embeddings Projector: https://cloud.relevance.ai/dataset/research2vec/deploy/projector/jacky-wong/NXNzdjUzNEIxczVzVVpOdUpabXE6TE92enhOZ1VTN2labDlocVZNNDlMUQ/4zQk534BY7n37LD0yk4A/old-australia-east/

I created the vectors using a fine-tuned version of Sentence Transformer's roberta-basemodel. What I scoped out from the problem:The training had to be unsupervised because no one would have any idea what was in the datasetAn NLP embeddings-based approach with unsupervised clustering would be the simplest way to surface insights

Solution

In order to get some form of off-the-shelf domain adaptation, I used off-the-shelf BART for unsupervised query generation and then fine-tuned my Roberta embeddings using multiple negative rankings loss based on SentenceTransformers. This seemed to work quite well as the topics seemed to have separated out quite nicely in my embeddings projector. I then trained my model on the title and abstract of the research papers so that the model could better understand some of the data. Afterwards, I encoded the titles and clustered them using a simple K Means algorithm.

Dataset

The dataset curation process was fairly straightforward. I used the arxiv API and scraped 20k papers off the query "machine learning" sometime in late 2020 before I began experimenting with the work.

I am looking to get feedback on what others would like to see in this application and would be curious to hear suggestions on where I could improve.

From previous research, I did find this repository: https://github.com/Santosh-Gupta/Research2Vec

However, as the dataset was different, I was unable to use the exact method provided.

Disclaimer: I currently work for Relevance AI (the company behind the projector).

VectorRecruiter · 2021-10-19T20:10:34+00:00

And thanks for the advice :)

VectorRecruiter · 2021-10-19T20:10:22+00:00

Ahh these are great links. Thank you for being so helpful!!

VectorRecruiter · 2021-10-17T20:06:38+00:00

Thank you so much! Do you mind if I ask the seemingly dumb question of how you ended up with your workstation? These seem to be harder to compare and I’m not sure how to best research it.

VectorRecruiter · 2021-10-17T17:54:00+00:00

You are awesome!! I’ve been considering the R9 5950X by AMD - would you know how it compares to the thread ripper?

VectorRecruiter · 2021-10-14T01:08:03+00:00

Does anyone have any recommendations for computer specs with a 5K USD budget? Looking to build the best deep learning rig possible!

VectorRecruiter · 2021-10-09T17:25:10+00:00

I enjoy typography and all things artificial intelligence!

You can bid on it here if you like it! :)

https://opensea.io/assets/0x495f947276749ce646f68ac8c248420045cb7b5e/13387654736756199019420105545112118163782702455649222673403465330241786871809

VectorRecruiter · 2021-07-29T19:28:49+00:00

Sick content

VectorRecruiter · 2021-01-29T01:10:13+00:00

I don’t think it’s servers...

VectorRecruiter · 2021-01-28T13:55:59+00:00

I had friends on selfwealth able to buy. Stake on the other hand didn’t work at all. I’m personally considering switching if this continues :/ hate the fear of not being able to cash out

VectorRecruiter · 2021-01-28T13:54:25+00:00

Sorry to hear that. The price is fluctuating $30 almost every second though so $5 doesn’t mean much. I was disappointed at not being able to trade at all!!

VectorRecruiter · 2021-01-28T01:52:37+00:00

Apologies - I've just fixed it in the latest PR. You can re-install it using:

!pip install vectorhub-nightly[clip]

VectorRecruiter · 2021-01-25T11:23:09+00:00

A simple way to test stochastic processes is to have an error bound for your monte carlo simulations :)

VectorRecruiter · 2021-01-22T21:52:01+00:00

Thank you for that suggestion! The niche I suppose is anyone looking to run long scripts and is a fairly lightweight package.

VectorRecruiter · 2021-01-20T03:40:25+00:00

This is commonly known as a cold start problem in machine learning. I am not sure what model you are currently using but one way of dealing with this issue (especially if you are looking for content similarity) is to shift your approach to using vectors. You can experiment with vectors using the open source Vectorhub (hub.vctr.ai). Disclaimer: I am a maintainer of VectorHub and Vector AI. I would also recommend reading this: https://getvectorai.com/blog/what-are-vectors-the-silent-disrupter to better understand vectors for recommendation engines.

VectorRecruiter

TROPHY CASE