Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Sountsov, Pavel"'
How do we infer a 3D scene from a single image in the presence of corruptions like rain, snow or fog? Straightforward domain randomization relies on knowing the family of corruptions ahead of time. Here, we propose a Bayesian approach-dubbed robust i
Externí odkaz:
http://arxiv.org/abs/2402.01915
Autor:
Phan, Du, Hoffman, Matthew D., Dohan, David, Douglas, Sholto, Le, Tuan Anh, Parisi, Aaron, Sountsov, Pavel, Sutton, Charles, Vikram, Sharad, Saurous, Rif A.
Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt. One can also improve LLMs' performance on a specific task by supervised fine-
Externí odkaz:
http://arxiv.org/abs/2312.02179
Autor:
Hoffman, Matthew D., Le, Tuan Anh, Sountsov, Pavel, Suter, Christopher, Lee, Ben, Mansinghka, Vikash K., Saurous, Rif A.
The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge there may still be uncertainty about the shapes of occluded parts of obje
Externí odkaz:
http://arxiv.org/abs/2210.17415
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonance which decreases the efficiency of the algorithm and makes it very se
Externí odkaz:
http://arxiv.org/abs/2210.12200
Autor:
Margossian, Charles C., Hoffman, Matthew D., Sountsov, Pavel, Riou-Durand, Lionel, Vehtari, Aki, Gelman, Andrew
Publikováno v:
Bayesian Analysis 2024
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such as GPUs. While each chain still needs to forget its initial point dur
Externí odkaz:
http://arxiv.org/abs/2110.13017
Autor:
Sountsov, Pavel, Hoffman, Matt D.
Hamiltonian Monte Carlo (HMC) is a premier Markov Chain Monte Carlo (MCMC) algorithm for continuous target distributions. Its full potential can only be unleashed when its problem-dependent hyperparameters are tuned well. The adaptation of one such h
Externí odkaz:
http://arxiv.org/abs/2110.11576
Autor:
Nijkamp, Erik, Gao, Ruiqi, Sountsov, Pavel, Vasudevan, Srinivas, Pang, Bo, Zhu, Song-Chun, Wu, Ying Nian
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usu
Externí odkaz:
http://arxiv.org/abs/2006.06897
Autor:
Lao, Junpeng, Suter, Christopher, Langmore, Ian, Chimisov, Cyril, Saxena, Ashish, Sountsov, Pavel, Moore, Dave, Saurous, Rif A., Hoffman, Matthew D., Dillon, Joshua V.
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-variance bounds using only unnormalized probability functions make it i
Externí odkaz:
http://arxiv.org/abs/2002.01184
Constant-memory algorithms, also loosely called Markov chains, power the vast majority of probabilistic inference and machine learning applications today. A lot of progress has been made in constructing user-friendly APIs around these algorithms. Suc
Externí odkaz:
http://arxiv.org/abs/2001.05035
Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) algorithm for estimating expectations with respect to continuous un-normalized probability distributions. MCMC estimators typically have higher variance than classical Monte
Externí odkaz:
http://arxiv.org/abs/2001.05033