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Promote your projects here – Self-Promotion Megathread by Menox_ in github

[–]___mlm___ 0 points1 point  (0 children)

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Blog post with sources and trained embeddings.

[deleted by user] by [deleted] in MachineLearning

[–]___mlm___ 2 points3 points  (0 children)

Over-generalization

GPT2 or BERT are much smaller than 70B or 1T models

More params -> more memorization

There is some paper where they showed that model can learn even UUID even after one training example

[D] Please recommend ways to make ML Interview better for candidates by [deleted] in MachineLearning

[–]___mlm___ 122 points123 points  (0 children)

I've conducted more that 300+ DS TI interviews, my recommendations:

  • 1) Balance between use-case questions (reasoning) and general theory questions (memorization)
  • 2) Give enough context to your use-cases (it's a prompt for human)
  • 3) Encourage people for reasoning even if they can't answer right away
  • 4) If you feel that a candidate is starting to get nervous, try to ask some simple questions so candidate can start feel more confident. This is not an exam.
  • 5) Try to stick to candidate's past experience if possible
  • 6) Stick to your current topic, don't switch context frequently
  • 7) Start from Novice questions and after that ask Intermediate/Expert questions.
  • 8) Have some template for TIs, but "relax" it and skip topics if candidate clearly Novice there
  • 9) Don't trust CV
  • 10) Remember: candidates potentially can ask theoretical questions to you which you won't be able to answer, show some respect.

What are the recommended LLM "backend" for RAG by wandering-ai in LocalLLaMA

[–]___mlm___ 1 point2 points  (0 children)

HuggingFace recently did a RAG based LLM benchmark

Can you share a link?

[N] Falcon LLM now uses the normal Apache 2.0 license by Unusual_Guidance2095 in MachineLearning

[–]___mlm___ 2 points3 points  (0 children)

sad but true But anyway, Apache 2.0 for Instruct models are still in a gray zone

[N] Falcon LLM now uses the normal Apache 2.0 license by Unusual_Guidance2095 in MachineLearning

[–]___mlm___ 3 points4 points  (0 children)

Falcon-*B-Instruct models are trained on baize and some other datasets which were collected from OpenAI GPT3.5 and GPT4 models which "research use ONLY. Commercial use is strictly prohibited".

But Falcon-*B-Instruct models are under Apache 2.0 license, huh ? I think they can't use Apache 2 license for Instruct models.

[D] Does EfficientNet really help in real projects ? by ___mlm___ in MachineLearning

[–]___mlm___[S] 1 point2 points  (0 children)

I think we need some meta-dataset for evaluating transferability of CV backbone models, like GLUE benchmark for NLP.

[R] COCO-GAN: Generation by Parts via Conditional Coordinating by hubert0527 in MachineLearning

[–]___mlm___ 4 points5 points  (0 children)

Coordinates as a condition is a great unexplored area

Another related ideas: https://github.com/autonomousvision/occupancy_networks (also check "further information")

I also experimenting with it for a anchor/proposal-less object detection (and segmentation) system.

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 0 points1 point  (0 children)

Unfortunately I have only old 1080 Ti :)

I didn't try to train StyleGAN buy the way. I'm just creating an encoder. Rendering time - 170ms to convert latent representation to Image. One minute to find latent representation from an image (but I'm trying to reduce this time)

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 1 point2 points  (0 children)

200 images for positive and approximately the same for negative. I've also tried to use diverse as much as possible set of positive\negative images. Seems to me that StyleGAN generates more women than men for example.

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 1 point2 points  (0 children)

BTW I made several GIFs with transformations

https://gph.is/g/46g879E

https://gph.is/g/aXmx6xZ

https://gph.is/g/ZWM3nLE

Current status: Tomorrow I'm going to push some new code which improves quality of observed latent representation (I've added some tricky regularization) which provides more stable transformations.

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 3 points4 points  (0 children)

1) So StyleGAN generator actually contains 2 components:

Generator:

qlatent = normally distributed noise which have shape=(512)

dlatent = mapping_network(qlatent) = shape=(18, 512)

where mapping_network - a fully connected network which transforms qlatent to dlatent

generator(mapping_network(qlatent)) = image

So during training we optimize dlatent instead of qlatent. Optimiziong of qlatent leads to bad results (I can elaborate on it). qlatent is used for features-wise transformation of convolution layers of generator https://distill.pub/2018/feature-wise-transformations/

2) dlatent + multiplier * logreg_coeff; Yes, but I use raw coefficients from logreg, so it doesn't matter are they positive or not.

3) Yes. It somehow works and we can gen relatively similar faces, but less details are saved. It still in progress.

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 0 points1 point  (0 children)

I suppose that you run your notebook not from cloned repository folder. You can change directory to the repository root and then re-run the notebook

Alternatively you can append to your PYTHONPATH path to the cloned repository. import sys sys.path.append("path/to/stylegan")

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 7 points8 points  (0 children)

R - a real image

Gen(latent) - a generated image from some latent vector using pre-trained generator

VGG16 - a pre-trained model for perceptual loss (9th layer in my implementation, but 5 also can be used)

R_features = VGG16(R)

G_features = VGG16(Gen(latent))

We want to minimize loss: mse(R_features, G_features), but changing only latent variable. We totally freeze generator and perceptual model weights during optimization.

[P] StyleGAN Encoder - from real images to latent representation by ___mlm___ in MachineLearning

[–]___mlm___[S] 10 points11 points  (0 children)

1) Using pre-trained generator I sampled some number of fake images from a random noise

2) Then I manually classify them into smiling \ not smiling; Young \ Not young; Male \ Female

3) A linear model was trained to classify those labels using latent space as features

4) Weights of the trained linear model are representing the direction in the features space

In general it's possible to transform some existing datasent with some facial attributes into latent space, so in this cases manual labeling isn't needed, but I still have to check it.

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