Turan Orujlu
@turanorujlu.bsky.social
PhD Student @unituebingen.bsky.social. Interested in intuitive physics, world models, causality, and reinforcement learning.
Reposted by Turan Orujlu
Proud of the work from hmc-lab.com & collaborators @ #CogSci2025 this year, but sad I cant be there myself
@hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social
@hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social
July 29, 2025 at 2:53 PM
Proud of the work from hmc-lab.com & collaborators @ #CogSci2025 this year, but sad I cant be there myself
@hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social
@hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social
Reposted by Turan Orujlu
SCMs often assume overly-dense causal graphs in dynamic settings 👉⚽🥎, since any object interaction is a potential causal edge, making them hard to scale. In joint work with @turanorujlu.bsky.social @cgumbsch.bsky.social & Martin Butz we propose a new Causal Process Model to tackle this.
Thread👇
Thread👇
July 24, 2025 at 4:26 PM
SCMs often assume overly-dense causal graphs in dynamic settings 👉⚽🥎, since any object interaction is a potential causal edge, making them hard to scale. In joint work with @turanorujlu.bsky.social @cgumbsch.bsky.social & Martin Butz we propose a new Causal Process Model to tackle this.
Thread👇
Thread👇
Reframing attention as an RL problem for causal discovery
AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.
AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.
July 22, 2025 at 6:59 PM
Reframing attention as an RL problem for causal discovery
AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.
AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.