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Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning
Erik Ayari, Manuel Traub, Martin V. Butz
Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones. Prior work, including tail dr…
Partially Observed Structural Causal Models
Turan Orujlu, Jordan Matelsky, Martin V. Butz +2
Here we introduce Partially Observed Structural Causal Models (POSCMs) as an extension of structural causal models (SCMs) to settings where upstream contexts co-determine both the…
Dynamics-Aligned Shared Hypernetworks for Contextual RL under Discontinuous Shifts
Jan Benad, Pradeep Kr. Banerjee, Frank Röder +3
Zero-shot generalization in contextual reinforcement learning remains a core challenge, particularly when the context is latent and must be inferred from data. A canonical failure…
Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
Turan Orujlu, Christian Gumbsch, Martin V. Butz +1
Most neural models of causality assume static causal graphs, failing to capture the dynamic and sparse nature of physical interactions where causal relationships emerge and dissolv…