Recent comments in /f/MachineLearning

spiritualquestions OP t1_j57f0az wrote

Reply to comment by suflaj in [D] Not sure if time series or multiple classifications? by spiritualquestions

Thanks for getting back to me!

Would this be considered multi output regression? Also why would I not want to use multi output classification? For clarification the scores are discrete so there is no score of 1.2, rather they are either 1,2,3, or 4. Or even could be treated as "severe", "bad", "medium", "good".

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suflaj t1_j57ce83 wrote

Reply to [D] Not sure if time series or multiple classifications? by spiritualquestions

This looks like something for XGBoost. In that case you're looking at the XGBRegressor class.

Your X are the first 4 features, your Y are the 3 outputs. You will need to convert the medication to a one-hot vector representation, and the diet will presumably be enumerated into whole numbers sorted by healthiness.

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currentscurrents t1_j573tug wrote

Reply to [D] Did YouTube just add upscaling? by Avelina9X

They announced upscaling support in Chrome at CES 2023.

>The new feature will work within the Chrome and Edge browsers, and also requires an Nvidia RTX 30-series or 40-series GPU to function. Nvidia didn't specify what exactly is required from those two GPU generations to get the new upscaling feature working, nor if there's any sort of performance impact, but at least this isn't a 40-series only feature.

Interesting though that it's working with your GTX 1660 Ti. Maybe Chrome is implementing a simpler upscaler as a fallback for older GPUs?

Check your chrome://flags for anything that looks related.

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AmalgamDragon t1_j56uj5c wrote

Reply to comment by tennismlandguitar in [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often? by tennismlandguitar

I recently started using RL in my personal work on automated futures trading. After reviewing the libraries available in the RL space, I did try the one you linked too. Some of the samples were broken. While I did tweak the code to get the samples to work, I found it to be more straightforward to get up and running using PPO from stable-baselines3.

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laaweel t1_j56s7g8 wrote

Reply to comment by ramya_1995 in [D] GCN datasets by ramya_1995

I didn't know it either but I found this blog post:

https://medium.com/mlearning-ai/ultimate-guide-to-graph-neural-networks-1-cora-dataset-37338c04fe6f#:~:text=Interesting!%20The%20training%20data%20contains%2020%20data%20for%20each%20class.%20The%20validation%20and%20test%20data%20do%20not%20have%20equal%20proportions%20of%20classes%2C%20but%20the%20two%20have%20a%20similar%20distribution.%20And%20these%20are%20similar%20to%20the%20percentages%20of%20classes%20in%20the%20overall%20data%20seen%20in%20the%20Classes%20section.

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ramya_1995 OP t1_j56ovcx wrote

Reply to comment by laaweel in [D] GCN datasets by ramya_1995

u/laaweel I have another quick question. Cora dataset splits the labels into 140 trains, 500 for valid and 1000 for test (according to DGL website). I found that these numbers correspond to the number of nodes (node classification problem). But any thought why the sum (140+500+1000) does not match the total node number in Cora dataset (2708 nodes)? Is it because the rest of the nodes are unlabeled? Thank you!

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Low-Mood3229 OP t1_j56lqee wrote

Reply to comment by clvnmllr in [R] Is there a way to combine a knowledge graph and other types of data for ML purposes? by Low-Mood3229

I did look at resources about graph embedding but they all seem to talk about using it in a link prediction or graph completion sense. My use case is more classification of datapoints(containing many seemingly unimportant features that may or may not have some relationship to each other. Relationships that are captured in the knowledge graph )

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BadassGhost t1_j55rxme wrote

Reply to comment by currentscurrents in [D] is it time to investigate retrieval language models? by hapliniste

I think the biggest reason to use retrieval is to solve the two biggest problems:

  • Hallucination
  • long-term memory.

Make the retrieval database MUCH smaller than Retro, and constrain it to respectable sources (textbooks, nonfiction books, scientific papers, and Wikipedia. You could either not do textbooks/books, or you could make deals with publishers. Then add to the dataset (or have a second dataset) everything it sees in a certain context in production. For example, add all user chat history to the dataset for ChatGPT.

Could use cross-attention in RETRO (maybe with some RLHF like ChatGPT), or just software engineer some prompt manipulation based on embedding similarities.

You could imagine ChatGPT variants that have specialized knowledge that you can pay for. Maybe an Accounting ChatGPT has accounting textbooks and documents in its retrieval dataset, and accounting companies pay a premium for it.

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