Sep 10, 2026

Probabilistic Inference for Controlling Diffusion Models

An in-depth presentation on controlling diffusion models during inference, categorizing techniques into sequential and parallel families. The talk explores both approximate and exact probabilistic inference methods, highlighting their underlying principles, computational trade-offs, and applications in areas like rare event sampling and protein design. A practical roadmap is provided for selecting the most suitable control strategy based on objective and computational resources.

Sep 09, 2026

Can AI help us better predict the weather?

Peter Battaglia of Google DeepMind discusses how AI is revolutionizing weather forecasting. From predicting Category 5 hurricanes like Melissa with unprecedented accuracy to enabling hourly global forecasts with WeatherNext 3, machine learning models are leveraging decades of physics data to provide critical early warnings, optimize renewable energy, and transform agricultural planning, fundamentally reshaping our understanding of atmospheric dynamics.

Aug 20, 2026

Going In Deep On Data | YC Paper Club

Three experts discuss the evolving landscape of data in AI, covering its critical role in model performance, challenges in sourcing and evaluating expert data, the development of advanced data generation techniques for diffusion models, and the complexities of multilingual pre-training.

Aug 18, 2026

Training Krea 2: What matters in generative model training — Sangwu Lee, Krea.ai

The talk by Sangwha Lee discusses Krea 2's open-source medium variant, emphasizing stylistic diversity and rapid iteration over the consistency-focused approach of larger models. A significant portion details their robust data curation pipeline, including unique methods for deduplication, filtering out AI-generated images, utilizing sparse autoencoders for unsupervised tagging, and ensuring world knowledge coverage. He outlines an LLM-inspired multi-stage training process, culminating in a prompt expander, and shares insights on fast iteration and future directions for image generation, highlighting the increasing integration of VLM advancements.

Aug 10, 2026

Why Deep Networks Don’t Need to Memorize Everything — Matthieu Wyart

Matthieu Wyart, a statistical physicist, argues that deep networks discover abstractions by recovering hidden data hierarchies, allowing them to escape the curse of dimensionality. He explains how this mechanism, combined with predicting latent representations instead of raw tokens, can significantly improve sample efficiency. The discussion also covers the physics of rough loss landscapes, machine creativity, diffusion models, and a theoretical framework for neural scaling laws.

Aug 08, 2026

Why Robotics Still Isn't Solved - But Could Be Soon | YC Paper Club

This Paper Club delves into the current state of robotics, addressing roadblocks like the sim-to-real gap and embodiment drift. Speakers present advancements in multi-scale memory for long-horizon tasks, self-supervised embodied reasoning, zero-shot dexterous manipulation via massive simulation, and the economic imperative of teleoperation-first robotics companies, concluding with optimizations for efficient, real-time World Action Models.