10 citations · 29 across the 10 of their papers we have counts for
11 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 (…
Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Hamiltonian Monte Carlo and underdamped Langevin Monte Carlo are leading methods for sampling from high-dimensional distributions with differentiable densities. Both rely on numeri…
BlackJAX: Composable Bayesian inference in JAX
Alberto Cabezas, Adrien Corenflos, Junpeng Lao +19
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by tak…
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…
Lost in Machine Translation: A Method to Reduce Meaning Loss
Reuben Cohn-Gordon, Noah Goodman
A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same se…
An Incremental Iterated Response Model of Pragmatics
Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts
Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. T…
Pragmatically Informative Image Captioning with Character-Level Inference
Reuben Cohn-Gordon, Noah Goodman, Christopher Potts
We combine a neural image captioner with a Rational Speech Acts (RSA) model to make a system that is pragmatically informative: its objective is to produce captions that are not me…