151 citations · 203 across the 25 of their papers we have counts for
7 papers · 1 filter
Schema-learning and rebinding as mechanisms of in-context learning and emergence
Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju +3
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that u…
Neuroscience needs Network Science
Dániel L Barabási, Ginestra Bianconi, Ed Bullmore +17
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has…
Fast exploration and learning of latent graphs with aliased observations
Miguel Lazaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan +2
We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent t…
Graph schemas as abstractions for transfer learning, inference, and planning
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra +6
Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neur…
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
Guangyao Zhou, Nishad Gothoskar, Lirui Wang +5
The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understandi…
PushWorld: A benchmark for manipulation planning with tools and movable obstacles
Ken Kansky, Skanda Vaidyanath, Scott Swingle +3
While recent advances in artificial intelligence have achieved human-level performance in environments like Starcraft and Go, many physical reasoning tasks remain challenging for m…
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George +1
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical models which express rich statistical dependencies in binary data. Variational inference (VI) has been the…