490 citations · 1k across the 74 of their papers we have counts for
8 papers · 1 filter
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes
Nishad Gothoskar, Matin Ghavami, Eric Li +8
Robots cannot yet match humans' ability to rapidly learn the shapes of novel 3D objects and recognize them robustly despite clutter and occlusion. We present Bayes3D, an uncertaint…
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman +2
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic space of…
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
Inferring the Goals of Communicating Agents from Actions and Instructions
Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka +1
When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan. H…
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Lionel Wong, Gabriel Grand, Alexander K. Lew +4
How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines…
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand +1
Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone. We propose a new infere…