2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Emergent Specialization in Populations of Self-Supervised Collaborative Vision Experts Without a Shared Gate or Cross-Agent Gradients
Aram Davtyan, Pablo Acuaviva, Sebastian Stapf +1
Can a population of neural networks develop a useful division of labor without a shared gate or gradients between agents? We study a setting where each network has its own weights,…
World Model Self-Distillation: Training World Models to Solve General Tasks
Sebastian Stapf, Pablo Acuaviva Huertos, Aram Davtyan +1
Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed textual descriptions limits their di…
Composition of Memory Experts for Diffusion World Models
Sebastian Stapf, Pablo Acuaviva Huertos, Aram Davtyan +1
World models aim to predict plausible futures consistent with past observations, a capability central to planning and decision-making in reinforcement learning. Yet, existing archi…
COMiT: Learning Structured Visual Tokens through Sequential Communication
Aram Davtyan, Yusuf Sahin, Yasaman Haghighi +4
Discrete image tokenizers provide a sequential interface for vision and multimodal models, but are typically optimized for reconstruction or compression and therefore tend to encod…