@beman70.bsky.social
🎉 Paper alert!
We explored how LLMs can generate engaging content in social networks, adapting to user opinions and network dynamics. Using RLSF, our approach aligns content to topics to maximize engagement.
arxiv.org/abs/2411.13187
#AI #LLMs #SocialEngagement
We explored how LLMs can generate engaging content in social networks, adapting to user opinions and network dynamics. Using RLSF, our approach aligns content to topics to maximize engagement.
arxiv.org/abs/2411.13187
#AI #LLMs #SocialEngagement
Engagement-Driven Content Generation with Large Language Models
Large Language Models (LLMs) exhibit significant persuasion capabilities in one-on-one interactions, but their influence within social networks remains underexplored. This study investigates the poten...
arxiv.org
November 26, 2024 at 10:53 AM
🎉 Paper alert!
We explored how LLMs can generate engaging content in social networks, adapting to user opinions and network dynamics. Using RLSF, our approach aligns content to topics to maximize engagement.
arxiv.org/abs/2411.13187
#AI #LLMs #SocialEngagement
We explored how LLMs can generate engaging content in social networks, adapting to user opinions and network dynamics. Using RLSF, our approach aligns content to topics to maximize engagement.
arxiv.org/abs/2411.13187
#AI #LLMs #SocialEngagement
Reposted
I made a notebook with a few notes on Diffusion Models for a "tutorial" in a project-workshop yesterday. Not really an introduction, but I give some insights that I usually don't see elsewhere. Feel free to reuse.
🔗https://colab.research.google.com/drive/1EyqALXFvgKGsTiFDALGEHH5-WnuGjOKU?usp=sharing
🔗https://colab.research.google.com/drive/1EyqALXFvgKGsTiFDALGEHH5-WnuGjOKU?usp=sharing
Google Colab
colab.research.google.com
November 23, 2024 at 11:32 AM
I made a notebook with a few notes on Diffusion Models for a "tutorial" in a project-workshop yesterday. Not really an introduction, but I give some insights that I usually don't see elsewhere. Feel free to reuse.
🔗https://colab.research.google.com/drive/1EyqALXFvgKGsTiFDALGEHH5-WnuGjOKU?usp=sharing
🔗https://colab.research.google.com/drive/1EyqALXFvgKGsTiFDALGEHH5-WnuGjOKU?usp=sharing
Reposted
BayesFlow is a library for amortized Bayesian inference with neural networks.
⋅ Multi-backend via Keras 3: Use PyTorch, TensorFlow, or JAX.
⋅ Modern nets: Flow matching, diffusion, consistency models, normalizing flows, transformers
⋅ Built-in diagnostics and plotting
🔗 github.com/bayesflow-or...
⋅ Multi-backend via Keras 3: Use PyTorch, TensorFlow, or JAX.
⋅ Modern nets: Flow matching, diffusion, consistency models, normalizing flows, transformers
⋅ Built-in diagnostics and plotting
🔗 github.com/bayesflow-or...
November 22, 2024 at 10:31 PM
BayesFlow is a library for amortized Bayesian inference with neural networks.
⋅ Multi-backend via Keras 3: Use PyTorch, TensorFlow, or JAX.
⋅ Modern nets: Flow matching, diffusion, consistency models, normalizing flows, transformers
⋅ Built-in diagnostics and plotting
🔗 github.com/bayesflow-or...
⋅ Multi-backend via Keras 3: Use PyTorch, TensorFlow, or JAX.
⋅ Modern nets: Flow matching, diffusion, consistency models, normalizing flows, transformers
⋅ Built-in diagnostics and plotting
🔗 github.com/bayesflow-or...