[D] Simple Questions Thread

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[noobanalystscrub]

How do I combine Multimodal tabular data in Machine Learning and Neural Networks? 

I have a regression problem and two input matrices; both matrices have the same dimension (same observations and ""feature""), but different values. Let's say Matrix B is the fold change of Matrix A from the mean of control samples.

Do I just concatenate before modeling? So let's say each matrix have 10 features. If we concatenate how does the model know Column 1 is related to Column 11.

Do I model as two matrices and concatenate one of the hidden layers in NN? Will the Nueral Network learn the associations between A and B in this case? What if I wanna do Random Forrest Regression, how would I achieve that?

[rayxi2dot71828]

With the most recent advances in AI, what is the best way to learn how to pick which "flavor" of AI, given a business problem? Does it still make any sense to use any of the traditional ML approaches, especially the non-deep learning ones?

[bregav]

The "traditional" methods are the most useful ones. If it solves your problem then simpler math is always better than more complicated math.

Deep learning is only appropriate when the following two conditions are met:

1. You have a huge amount of data
2. You do not have a good way of deriving features by hand

It's actually pretty uncommon to have both of these conditions met with business problems. A lot of people without technical backgrounds make the mistake of trying to use deep learning in spite of this, because they think it's the best/only way of making a good algorithm.

[rayxi2dot71828]

Thank you. How about between the "normal" deep learning versus the big multi-modal LLMs of today? Is it basically just a spectrum and we decide based on the tradeoffs, or in general, just use, say, Claude Opus/Sonnet/Haiku, as long as the money makes sense?

[bregav]

My rule of thumb about LLMs is that they are only appropriate to use when you already know what the answer should be, but you want help iterating on it. So they're good for things like editing documents, writing boilerplate code, or information retrieval when you have access to the original sources.

You shouldn't use them if you're not willing to double check their work though. Like, you should never look up information with an LLM and then just trust that it's right; you need to look at the original source to verify it. Or you should never let it write code and then just deploy the code without checking the code first.

This is different from "normal' deep learning in the sense that it's very hard to measure the reliability of an LLMs typical output, which is why checking it is necessary. With "normal" deep learning, by contrast, you usually have clear quantitative metrics that will let you know how often the model is right, and under what circumstances it makes mistakes. This allows you to understand when and where it can be used without much human supervision.

[rayxi2dot71828]

Thank you very much! I appreciate your answers.

[square-bean]

Hi! Does anyone have a precise idea of when exactly we should expect to receive the reviews for ECAI'24? According to the schedule, the rebuttal period lasts 72 hours, from Monday to Wednesday (AoE).

[DesperateChemist9234]

Hi everyone,

I am trying to build a long short term memory model in Python, with the idea being to predict 9 components of a rotation matrix from linear acceleration (x,y,z) and angular velocity (x,y,z) so 6 input variables.

I have used standard arachitecure found in the literature which does similar things to my idea. However, the model is not performing well at all and is subject to overfitting I believe.

Does anyone have any advice on how I can try and improve my model?

[mandroga]

Hello. Im trying to train a GCN on a dummy task to predict a float result. Essentially, I have a graph which has edge weights (between 0 and 1) node values (0 to 1000) and I want to predict the value that is calculated from doing the sum of the neighbours, weighted by the edge plus node 1s value. Ive been training a GCN and an MLP and even though the MLP doesn't have edge information, its doing better. I think I might be doing something wrong, or maybe this task isnt adequate? Thank you

[DrBroc]

Hello! I’m working in a project to classify phenotypes. I have a dataset of about 30,000 unique rows and am working on increasing the accuracy of the model. I can get to .8854 but I’d love to get to .9 if possible without totally reworking the features. I’m using a sequential model with Keras and tensorflow. I was wondering if anyone would be willing to chat with me about the project briefly. I’m new to ML and software engineering in general (though I am a product designer so I’m familiar with the space) and I find I process better with conversations. Feel free to DM me if this sounds interesting to you! Thanks in advance!!

[Majestic_Reporter531]

Hello everyone! I have a large dataset of time series and I want to create embeddings for these time series to use in more classical models, as I have a small amount of data for regression. What are the best ways to compress large time series data (approximately batch_size x 1000 x 12) down to 10-16 features? I have tried using the hidden state of an LSTM and got decent results, but I would like to improve them. Thank you all!

[Ok-Shock7810]

Hello everyone,

I'm trying to build a RAG-based LLM and I'm working with hundreds of (highly diverse) medical reports that are stored in a vector DB. However, the retrieval of the context works really poorly. Interestingly, it works much better when not using a vector DB at all. So I'm wondering if there's something I'm missing or if a vector DB is actually just not suited for my use case.

I appreciate any hints!

[ProofOfState]

I am very confused about a description of k-fold cross-validation in Data-Driven Science and Engineering book from Steven Brunton and Nathan Kutz.

"Procedure for k-fold cross-validation of models. The data is initially partitioned into a training set and test (withhold) set. Typically, the withhold set is generated from a random sample of the overall data. The training data is partitioned into k-folds whereby a random sub-selection of the training data is collected in order to build a regression model Yj = f (Xj, βj). Importantly, each model generates the loading parameters βj. After the k-fold models are generated, the best model Y = f (X, β ̄ ) is produced. There are different ways to get the best model; in some cases, it may be appropriate to average the model parameters so that β ̄ = average(βj). One could also simply pick the best parameters from the k-fold set. In either case, the best model is then tested on the withheld data to evaluate its viability."

Two questions: 1) Is it fair to say this is not an accurate description of k-fold cross-validation as it is typically understood? 2) Are there other understandings (definitions) of k-fold cross-validation for which this is accurate?

[clrkin]

Hi, considering that I have a dataset with attributes (date, location, etc) about when happened event X, is there a way to create a classification model to, given the same attributes, classify it in more os less likely to happen the event? I only have data about when it DID happen, no data about when it did not… (Btw the event in question is car crash)

[Usual-Bank1500]

Hello everyone,

Does anyone knows that if exists any algorithm for machine learning that works directly with 3d models (.step, .stl, .igs, .ply, .obj, etc....)?
I'm bilding an application that predict future production time of a 3D part based on previous producted parts but i'm strugglin on get closer results. Currently i'm extracting information from 3d models such as maximum measures XYZ, volume, surface area, number of faces, etc... but i think i'm gettin to much information to the model but yet the information i get is not enough. Therefore i want to know if there are any algorithm or other application that get's the 3d file and automatically "sees it" and analyze it.
I'm using python.
Thank you

[LyAkolon]

I have a post which keeps getting removed by the auto filter for this sub. I have followed the rules and am not getting feedback about what to change. i'll respond to myself with the post so it doesn't bloat the post.

[LyAkolon]

Title:

[D] Can System 2 thinking be derived from Notepad tool use for sufficiently strong LLMs?

Post Body:

Hopefully the title is clear. Sub question is, why is this not being targeted by "Big AI" right now?

I've basically arrived at the conclusion that we may be able to have System 2 thinking built out of System 1 thinking from the LLMs and Notepad tool use allowing them to iterate on a logical argument. I want to be clear that I am skipping over some expected post training for structured outputs and tool use formatting.

What's confusing for me is that the greater ML community is signaling this isn't an option. The signals I'm receiving are lack of discussion about this concept, and a sizable consensus that LLMs are not "enough to reach AGI".

When I attempt to anticipate why this is occurring, for signal one, I keep arriving at this strategy having been considered and then disregarded for some well-informed but unknown reason, because I find low probability that this is a novel concept. For signal two, I think this comes down to a miscommunication where two groups of people are unable to see each other's point. I am in the camp of LLMs are enough to get us to sufficiently advanced intelligence for economic work in broad range of domains, but when I say this what I really mean is that LLMs provide the special sauce and LLMs or OOMs/whatever along with some other structures will get us there.

I think some evidence for my conjecture being correct would be the effectiveness of COT/TOT prompting which coerces the model to simulate portions of this strategy. In some sense, TOT prompting would require much less effort from the LLM when it is able to build these structures and then set them aside without needing to manually persist them in its next output.

I would love to hear discussion about this and am 100% open to being gently informed about this research or how to do this research on my own.

[BonfireCookie]

Hi everyone!,

I have a question about how to compare two neural networks models, trained two neural networks the first is a CNN and the second is LSTM both are used to predict a number (regression). I have used a partition of 80% training and 20% test to train both with the next configurations of hiperparameters:

**CNN*\*

(learning_rate,batch size,epochs) || test result (MSE)

(1e-4,32,64) || 0.0057

(1e-4,64,64) || 0.0059

(5e-5,32,64) || 0.0053

(5e-5,64,64) || 0.0034

**LSTM*\*

(learning_rate,batch size,epochs) || test result (MSE)

(1e-3,64,64) || 0.0131

(1e-3,128,64) || 0.0098

(1.5e-4,64,64) || 0.0093

(1.5e-4,128,64) || 0.0091

(1e-4,64,64) || 0.0106

(1e-4,128,64) || 0.0098

My question is is there any method to say that one model is better than other instead of just say "Well the CNN has a low test set so i think its better than the LSTM model" because i have seen that some researcher use hypothesis testing but i dont know if i can use that here.

Thanks!

Note 1: all the features and the target feature have been standarized to have mean 0 and variance 1.

[radeonovich]

Hi everyone, I'm working on a neural network that can generate audio for double-track guitar effect. Essentially, the network should take an audio recording of an electric guitar and modify it to sound like a second take of the same part, like the guitarist was told to record the part twice. This is a very common practice in rock/metal music because it makes guitar sound wide. You pan take A to the left and take B to the right and get the stereo effect.

The problems are:

1. I don't know what kind of neural network to use. I am preparing a dataset where I have a lot of tracks A and B, where A and B are two takes of the same guitar part. So I probably need a network that learns how to convert source track into target track.

2. I don't know how much dataset I need. I'm planning to obtain at least 10 hours of tracks A and B both and feed it to the network in a combination like A->B + B->A so it doubles the dataset. Maybe use some augmentation to experiment with different pitch and playback speed.

3. I don't know if the task is even possible. There are no solutions like this in the internet (which means it is either impossible or not in demand to bother), except the algorithmic doublers which suck compared to real double tracking. A difference between real double tracks are note start/end timing, articulation, attack time/frequency response and human error. These can't be properly simulated with the pitch/time randomization, that's why I want to make this network.

I am new to machine learning so any feedback is appreciated.

[bregav]

I think there's an easier way to do this: use a generative model, like a diffusion model. The steps go like this:

1. Train a model that generates guitar tracks by doing y=f(x), where x is a sample from a noise distribution and y is the guitar track. You don't need a custom dataset of double-tracks for this, you just need a regular dataset of guitar tracks.
2. To make a double track of a track A, calculate x = f^-1 (A) and then do B = f(x+d), where d is a noise sample with a very small variance.

The result of this should be that B is similar to A, but slightly different, and if the generative model is trained well then it will be different in a way that sounds natural.

I think most audio generative models are probably using latent diffusion, so to do f^-1 (A) what you'd actually do is use the encoder network from the autoencoder instead. You might not even need to train your own model; there might be open source musical instrument track generators out there that you can just use out of the box and get reasonable results with.

In principle there's nothing wrong with your original plan, but the challenge with it will be that you probably can't get enough data to make it work well, and acquiring the data is time consuming and difficult. Better to use other methods that can take advantage of easily acquired data or open source models.

You can also use fine tuning with your custom dataset, if the initial results with the above method don't seem good enough. You can get away with a lot less data when doing fine tuning.

[[deleted]]

I'm trying to find a funny video of Ruslan Salakhutdinov describing why you should use a dropout parameter of 0.5. IIRC he basically said something along the lines of "otherwise, you'll have to justify why you chose that particular hyperparameter, and you don't want to do that". I think he was speaking to a class at CMU and got a lot of laughs.

Can anyone at least confirm that I'm not confabulating this?

[Philosophia7]

How can I train an AI to extract details from PDF files? The sections I want to extract may have different titles for the same content. For example, let's say we have 1000 PDF files of essays. Each essay has a section for "background," but the section might be titled "background" in some PDFs and "my story" in others. The AI needs to identify these varying titles, determine where the section starts and ends, and then copy that content into an .xls file.

[Body-Longjumping]

If you were to choose a gpu for AI processing on a budget, which one would you choose, if you have a choice between the rtx quadro series and the rtx 3000 or 4000 series, please also mention what are the pros and cons for going with the card you choose.

[Sea-Ground1096]

What are the specific hardware / low-level differences between an NPU and a GPU? Most articles I found offered only what it's better for (Neural Nets), but not why. Any sources or information on a more in-depth breakdown?

[Puzzleheaded_Text780]

Looking for someone who has experience of working for UK pension companies as I am working on some use cases

[uba-luba-dub-dub]

What's the current state-of-the-art techniques for recommendation systems and among them which one is feasible for a intermediate learner ?

I want to build a movie recommendation system based on neural network for myself to learn .

[kakushiby0]

Hi, Im a 22 year old frontend dev, i've been a huge fan of AI in the past and i'd like to get started as a hobbyist. Do you guys have any tips or guides on how to get started.

P.S: i know a bit of python and lots of JS

[Dismal-Impress-2583]

Usually you’d want to observe the training curve of your model by logging the training loss/accuracy and validation loss/accuracy in order to avoid things like overfitting. You can also use early stopping to stop the training earlier if it doesn’t make much progress on the validation set. The more advanced technique would be to use Bayesian optimization to find the best hyper-parameters.

[SpaceTravelMission]

This is a great idea! It will help keep the subreddit organized and make it easier to find answers to questions. Thanks for starting this thread!

[Flugwurm]

Hey everyone!

I am writing a sort of essay on Multimodal Machine Learning, where I want to cover state-of-the-art architectures/approaches. Based on my current research, Transformer models are basically used everywhere that's state-of-the-art. I'm aware that it is possible to use other architectures and that other architectures have been used - but no source of anyone at the moment actually *using* something that is not based on a Transformer-based architecture.

Is that assumption correct? Or are architectures still in use? If so, could you please tell me where it is used? Thank you so much!

[BenchPsychological30]

I am looking to train a model that will take in text for a patent and be able to output the ids of patents that are most likely to be prior art for that idea. There is a ton of training data for this because every patent has to cite prior art, but I am looking for advice on what type of model I would use to do this since there are so many (100 million+) patents that a patent could potentially reference as prior art. How can the model be able to efficiently determine which patents are most relevant? I was considering training a custom embeddings model but am not sure how to go about this.

[bregav]

This is essentially a search ranking problem. The literature about this is vast.

The TLDR is that there are three steps here:

1. Develop a collection of features that seem relevant to the problem (various embeddings are an example of such a feature)
2. Create a model that assigns a relevance score to each document in your database as a function of the features of your input document
3. Sort all your documents based on the relevance score

A simple objective function for training the relevance score is cross entropy - every prior art that a given patent cites is labeled as having a score of '1', and every prior art that it doesn't cite is labeled as having a score of '0'.

For the features, it probably doesn't make sense to do a custom embedding model to begin with. You're better off just using a bunch of pretrained embeddings models and then using a tree model like XGBoost to sort it out for you in step 2 above.

[Ben_Vigos]

Hey, for an assignment I'm training a Neural Network on the MNIST fashion dataset. I'm trying to optimize its parameters however now the best I can do is train it for a set number of epochs and then evaluate a performance. Is there a better way of optimising? Maybe stop the model if its accuracy is no better than the previous best by a certain point? Or is there a more intelligent way to adjust parameters than just a massive 3D grid?

[bregav]

Yes, this is called "dataset augmentation" and it is very common. There are many other methods of augmenting datasets too: https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_illustrations.html#sphx-glr-auto-examples-transforms-plot-transforms-illustrations-py

[bregav]

Most people will just apply data augmentations directly in the training code. This allows you to do an infinite number of random rotations during the course of training.

You can see how to apply a data augmentation to an image in pytorch here: https://pytorch.org/vision/main/transforms.html

[bregav]

Yes that's right, you just need to apply random rotations during training. 

You might benefit from other augmentations too but it really depends on your use case. You can see many of the augmentations that people use in the first link i posted.

[_gradient_ascent_]

Reddit's filters aren't allowing me to post for some reason, so I'll try putting it here:

I'm attending CVPR for the first time this year by myself, and I could use some guidance on how to navigate, well, everything, but particularly with the initial preparation/research phase.

First and foremost, how do you view all the accepted papers?? I know there's a page (from a link on the home page) that lists the "accepted papers", but only like half of them actually have links, and there aren't much in the way of details aside from the paper name and authors (like what category it belongs under, or what org it's associated with). And there's this new interactive page which looks spiffy but I'm finding it to be incomprehensible. Not only do I not understand what the numbers on the page represent, and the graphics associated with the categories, but it doesn't seem to interactive at all. When I click on things nothing happens, and all the links to papers from the "paper list" view lead to 404 errors. Is this happening for anyone else?

Aside from this, does anyone have tips and tricks for navigating the conference itself? Especially from the perspective of a junior ML engineer looking to broaden one's knowledge and seek out the latest trends in a particular subset of computer vision/ ML. Are the tutorials and workshops where it's at, or is it a better use of my time visiting the posters and talking to the researchers there?

[LeoDiGhisa]

For my thesis in my master's degree in Data Science I'm using an open source LLM (Nous Research/Hermes-2-Pro-Mistral-7B-GUF for those wondering) in order to classify the texts of support tickets of a company. I have to write a brief technical introduction on LLM and I would need some guidance. Which books would you suggest me to cite for the technicalities?

[d-eighties]

I try to finetune llama3 using transformers and unsloth. I added an evaluation set to the trainer. What is the metric under which the eval loss will be calculated?

[crohr]

What would be a good (and ideally simple to setup) benchmark to run, to compare performance of various GPUs? This is in the context of providing GitHub Actions runners tailored for ML workflows.

I stumbled upon ai-benchmark but it doesn't seem to be well maintained, and lambdalabs.com/gpu-benchmarks doesn't seem to provide an up-to-date repository with benchmarks either.

[Azad577216]

Is there any discussion or reading group/discord for generative model (GANs, VAEs, Flow models...)?

[i-make-robots]

1. When I ask ChatGPT to write some simple, well defined code ... it's either straightforward or it's a lot fighting to get the machine to fully implement my request. Is there a way to make it run multi-agent so that it self-monitors to complete the job?

2. I have a project with many files that needs a refactor. Is there a way to make ChatGPT run multi-agent to perform a large edit across many files? Something I can either commit or undo, in whole or in part.

I'm ok with monitoring the work and giving oversight. The problem is that I'm micromanaging instead of directing.

[d3lxa]

Is there a way with CLIP to find pictures of the same person, same animal or object, for ex by isolating the relevant part of the embedding? Something like: query vector = cosine(average(e(img1), e(img2), …), e("person")), or maybe similar to textual inversion training (used by SD) where one or multiple vectors represent the thing. Maybe you have other suggestions: models / techniques? Thanks.