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This paper presents a nonparametric Bayesian inverse reinforcement learning approach using a Dirichlet process prior to infer multiple latent reward types from expert demonstrations, implementing a collapsed Gibbs sampler with parallelization via Ray for scalability.
This paper proposes CWUTM, a topic model based on co-occurrence word networks designed to detect scarce topics in unbalanced short text datasets. It outperforms baselines in early and accurate identification of emerging topics on social platforms.
This paper presents a programmable probabilistic computer with one million p-bits by networking FPGAs, achieving Gibbs sampling at over a trillion flips per second for Ising models while introducing a design rule for scaling beyond single-chip limits.