Deep Generative Modeling in Network Science with Applications to Public Policy Research | RAND
Why machine learning struggles with causality – TechTalks
Artificial intelligence: A powerful paradigm for scientific research - ScienceDirect
How can generative adversarial networks learn real-life distributions easily - Microsoft Research
GANshare: Creating and Curating Art with AI | Towards Data Science
Generative adversarial networks: What GANs are and how they've evolved | VentureBeat
Frontiers | Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It
Artificial Intelligence Research | Qualcomm
Comparative Study of Deep Generative Models on Chemical Space Coverage | Journal of Chemical Information and Modeling
Bringing AI research to wireless communication and sensing
Deep generative models could offer the most promising developments in AI | VentureBeat
Recent advances and applications of machine learning in solid-state materials science | npj Computational Materials
AI scientists are studying the “emergent” abilities of large language models – TechTalks
Combining generative artificial intelligence and on-chip synthesis for de novo drug design | Science Advances
Abductive inference: The blind spot of artificial intelligence – TechTalks
Protein design and variant prediction using autoregressive generative models | Nature Communications
Artificial intelligence: A powerful paradigm for scientific research - ScienceDirect
At Tech's Leading Edge, Worry About a Concentration of Power - The New York Times
Generative Adversarial Networks: Build Your First Models – Real Python
Image Generation in 10 Minutes with Generative Adversarial Networks | Towards Data Science
Frontiers | Generative Models of Brain Dynamics
Improving Diffusion Models as an Alternative To GANs, Part 1 | NVIDIA Technical Blog
Generative AI Models Explained | AltexSoft
Frontiers | Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
