2 votes

References for synthetic images generation from small datasets (~10-50 images)

The references you stated indeed are the right way to go regarding small dataset image synthesis. I'd research the space of few-shot image synthesis for what is out there, but something along the line ...
Robin van Hoorn's user avatar
2 votes

Are there public examples of AI models that predicted short-term price well?

AFAIK, unfortunately no. However, we can get a feel of these beasts from some clues: They do machine learning. E.g. see this competition of Two Sigma on Kaggle, and this competition by Optiver. The ...
lpounng's user avatar
  • 363
2 votes

Which type of Machine Learning is used in robots?

Robotics primarily uses RL and supervised learning. To give examples: Supervised Learning is used for vision and perception Reinforcement Learning is used for control Unsupervised Learning for ...
chessprogrammer's user avatar
1 vote
Accepted

Equivalent of symbolic regression but for code instead of math expression

Yes, it's called genetic programming. I've done a bit of research on the topic in the past. If you use Python, you could use deap, which is quite easy to use. You may also be interested in the PushGP ...
nbro's user avatar
  • 40.2k
1 vote

Where can I find good sources (textbook, lecture notes, etc) on multi-armed bandits?

There is the bandit book by Lattimore and Szepesvari, which is, I think, the main reference in the field: you can find pretty everything about Bandits, even more recent and research oriented topics. ...
Luca Anzalone's user avatar
1 vote
Accepted

Overcoming the quadratic scaling in transformer architecture

Yes. What you're describing seems to be similar to the Hourglass Transformer, the Funnel Transformer, among others. Looking at the first paper, they consider two ways of downsampling tokens (...
Alexander Wan's user avatar
1 vote

Paper to cite about normalizing the inputs to a neural network

I have found a related part in C. C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer International Publishing AG, 2018. isbn: 978-3-319-94462-3. doi: 10.1007/978-3-319-94463-0. ...
ha7ilm's user avatar
  • 109
1 vote
Accepted

What is the name of the reward function that utilizes the rewards of the next n steps?

Your function could be called the truncated return - i.e. the sum of rewards up to some time step in the future. It would be unusual to perform reward shaping by taking the orginal reward from an ...
Neil Slater's user avatar
  • 31.7k
1 vote

What are the techniques used to initialize weights for neural networks?

Both Keras and Torch provide a wide range of techniques for weight initialization. Like Zero initialization :- Initializing all weights to zero it is easy way to initialize the weights and biases but ...
Keval's user avatar
  • 111
1 vote

What are the techniques used to initialize weights for neural networks?

Both Keras and PyTorch provide a range of initialization classes and functions. Of these, probably the two most commonly used are the Glorot (Xavier in Pytorch) and He (Kaiming in pytorch) ...
Lynn's user avatar
  • 141
1 vote

Modern graduate-level machine learning books with focus on generative models

Berkeley CS294-158 is a graduate-level course on deep unsupervised learning. They cover a lot of architectures used in modern generative modeling. They have recorded lectures and slides online. ...
Alexander Wan's user avatar
1 vote
Accepted

Model-based RL algorithms for continuous state space and finite action space

In optimal control field to minimize certain well-defined costs especially in process industries, continuous state space model-based planning methods such as model predictive control (MPC) is a common ...
cinch's user avatar
  • 1,243
1 vote

Does LSTM provide any unique value or advantages compared to other algorithms, including "vanilla" RNN?

RNN's/LSTM's work better on smaller datasets compared to transformers, assuming neither are pretrained. RNN's perform worse than transformers in most tasks with enough data due to RNN's having a ...
jeb's user avatar
  • 11
1 vote

Are any AI systems available, or in development, for finding and analysing fallacious inference in natural language text?

Yes, current large language models since GPT-4 are able to find errors in reasoning, probably zero-shot, not much prompt engineering needed. Giving a precise probability for something being wrong is ...
Volker Siegel's user avatar

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