Deep Learning 32
- Sim-and-Real Co-Training: A Simple Recipe
- Digital Cousins: Robust Sim-to-Real Without Exact Replicas
- Data Scaling Laws in Imitation Learning
- CLIP: Connecting Vision to Language at Scale
- ViT: An Image Is Worth 16x16 Words
- NeRF: Representing Scenes as Neural Radiance Fields
- Articulate-Anything: Building Articulated 3D Assets with VLM Agents
- ROBOMETER: Scaling General-Purpose Robotic Reward Models
- TOPReward: Zero-Shot Robot Rewards from Token Probabilities
- Cosmos WFM (3/3): Applications — From Simulation to Reality
- Cosmos WFM (2/3): Training — Diffusion, Autoregressive, and Post-Training Recipes
- Cosmos WFM (1/3): Foundations — World Models, Data, and Tokenization
- Why Should I Trust You? LIME Explained
- Message Passing on Graphs: GCN and GAT
- Self-Supervised Learning for Computer Vision
- Learning to Learn: MAML and Prototypical Networks
- Direction in Noise
- 1000 Steps Back: The Math Behind DDPM
- Finding Signal in the Static
- Reading Both Ways: BERT and the End of Left-to-Right
- The Right Half: Decoder-Only Transformers
- Where Do Facts Go to Live? MLPs, Superposition, and a Basketball Player Named Michael
- Attention Is a Third of What You Need: QKV, Dot Products, and the Other Two-Thirds
- Transformers: More Than Meets the Eye
- SIREN: Teaching Networks to Think in Sine Waves
- GANs: From a Thought Experiment to Photorealistic Faces
- When Neural Networks Lie: Adversarial Examples and the Art of Fooling AI
- From Autoencoders to VAEs: Learning to Generate, Not Just Compress
- LSTMs: How We Taught Neural Networks to Remember
- ML Normalization; A Primer
- ML Regularization; A Primer
- ML Optimization; A Primer