5 citations · 9 across the 4 of their papers we have counts for
9 papers
Counterdiabatic Hamiltonian Monte Carlo
Reuben Cohn-Gordon, Uroš Seljak, Dries Sels
Hamiltonian Monte Carlo (HMC) is a state of the art method for sampling from distributions with differentiable densities, but can converge slowly when applied to challenging multim…
Machine-Learned Sampling of Conditioned Path Measures
Qijia Jiang, Reuben Cohn-Gordon
We propose algorithms for sampling from posterior path measures under a general prior process. This leverages ideas from (1) controlled equilibrium dyn…
Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (…
Pragmatic Issue-Sensitive Image Captioning
Allen Nie, Reuben Cohn-Gordon, Christopher Potts
Image captioning systems have recently improved dramatically, but they still tend to produce captions that are insensitive to the communicative goals that captions should meet. To…
Lexical Learning as an Online Optimal Experiment: Building Efficient Search Engines through Human-Machine Collaboration
Jacopo Tagliabue, Reuben Cohn-Gordon
Information retrieval (IR) systems need to constantly update their knowledge as target objects and user queries change over time. Due to the power-law nature of linguistic data, le…
Communication-based Evaluation for Natural Language Generation
Benjamin Newman, Reuben Cohn-Gordon, Christopher Potts
Natural language generation (NLG) systems are commonly evaluated using n-gram overlap measures (e.g. BLEU, ROUGE). These measures do not directly capture semantics or speaker inten…