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no code implementations • 8 Jun 2023 • Ryan P. Adams, Peter Orbanz

The linear representation generalizes the Fourier basis to crystallographically invariant basis functions.

no code implementations • 31 Jan 2023 • Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams

In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it.

no code implementations • 3 Nov 2022 • Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams

Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or geometric domains.

no code implementations • 4 Oct 2022 • Diana Cai, Ryan P. Adams

A key challenge in applying MCMC to scientific domains is computation: the target density of interest is often a function of expensive computations, such as a high-fidelity physical simulation, an intractable integral, or a slowly-converging iterative algorithm.

1 code implementation • 22 Dec 2021 • Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge

In practice, we have inaccurate knowledge of the system dynamics, which can lead to unsafe behaviors due to unmodeled residual dynamics.

no code implementations • NeurIPS 2021 • David Zoltowski, Diana Cai, Ryan P. Adams

Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions; it only requires a density function that can be evaluated point-wise up to a normalization constant, making it applicable to a variety of inference problems and unnormalized models.

no code implementations • ICLR 2022 • Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards.

no code implementations • NeurIPS 2021 • Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world.

1 code implementation • NeurIPS 2021 • Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams

We compare our approach to direct optimization of the design using the learned surrogate, and to supervised learning of the synthesis problem.

1 code implementation • 26 Mar 2021 • Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints.

1 code implementation • NeurIPS 2020 • Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams

One of the appeals of the GP framework is that the marginal likelihood of the kernel hyperparameters is often available in closed form, enabling optimization and sampling procedures to fit these hyperparameters to data.

1 code implementation • ICLR 2021 • Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams

The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives.

1 code implementation • 16 Jul 2020 • Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.

no code implementations • NeurIPS 2020 • Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

We use a neural network to model the stored potential energy in a component given boundary conditions.

no code implementations • ICLR 2020 • Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.

1 code implementation • NeurIPS 2020 • Jordan T. Ash, Ryan P. Adams

We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point.

no code implementations • 25 Sep 2019 • Jordan T. Ash, Ryan P. Adams

We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point.

1 code implementation • NeurIPS 2019 • Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs.

no code implementations • 24 Jun 2019 • Jad Rahme, Ryan P. Adams

The central object in the statistical physics abstraction is the idea of a partition function $\mathcal{Z}$, and here we construct a partition function from the ensemble of possible trajectories that an agent might take in a Markov decision process.

no code implementations • NeurIPS 2019 • Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough

The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment.

1 code implementation • 16 May 2019 • Alex Beatson, Ryan P. Adams

We consider optimization problems in which the objective requires an inner loop with many steps or is the limit of a sequence of increasingly costly approximations.

no code implementations • NeurIPS 2018 • Diana Cai, Michael Mitzenmacher, Ryan P. Adams

The count-min sketch is a time- and memory-efficient randomized data structure that provides a point estimate of the number of times an item has appeared in a data stream.

no code implementations • 21 Nov 2018 • Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule.

no code implementations • 18 Jul 2018 • Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl

Advances in machine learning have led to broad deployment of systems with impressive performance on important problems.

1 code implementation • ICLR 2019 • Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Our main technical result is a generalization bound for compressed networks based on the compressed size.

1 code implementation • 28 Feb 2018 • Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.

no code implementations • 9 Feb 2018 • Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson

However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors.

1 code implementation • NeurIPS 2017 • Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

We provide theoretical guarantees on the quality of point (MAP) estimates, the approximate posterior, and posterior mean and uncertainty estimates.

1 code implementation • NeurIPS 2017 • Andrew C. Miller, Nicholas J. Foti, Alexander D'Amour, Ryan P. Adams

Optimization with noisy gradients has become ubiquitous in statistics and machine learning.

no code implementations • 17 Apr 2017 • Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

Professional-grade software applications are powerful but complicated$-$expert users can achieve impressive results, but novices often struggle to complete even basic tasks.

1 code implementation • ICML 2017 • Andrew C. Miller, Nicholas Foti, Ryan P. Adams

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class.

1 code implementation • 26 Oct 2016 • Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.

2 code implementations • NeurIPS 2016 • Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow

Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties.

10 code implementations • 7 Oct 2016 • Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.

no code implementations • 19 Jun 2016 • Akash Srivastava, James Zou, Ryan P. Adams, Charles Sutton

A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria.

3 code implementations • NeurIPS 2016 • Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams

We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths.

no code implementations • 16 Feb 2016 • Elaine Angelino, Matthew James Johnson, Ryan P. Adams

Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge.

no code implementations • NeurIPS 2015 • Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams

We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e. g., stars, galaxies, and quasars) at extremely different spectral resolutions.

no code implementations • NeurIPS 2015 • Scott Linderman, Matthew Johnson, Ryan P. Adams

For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions.

1 code implementation • 30 Nov 2015 • José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently.

no code implementations • 17 Nov 2015 • Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams

The results show that PESMO produces better recommendations with a smaller number of evaluations of the objectives, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.

no code implementations • 8 Nov 2015 • Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

Using the ground truth log-ML estimates obtained from our method, we quantitatively evaluate a wide variety of existing ML estimators on several latent variable models: clustering, a low rank approximation, and a binary attributes model.

8 code implementations • NeurIPS 2015 • David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams

We introduce a convolutional neural network that operates directly on graphs.

Ranked #2 on Drug Discovery on HIV dataset

1 code implementation • 12 Jul 2015 • Scott W. Linderman, Ryan P. Adams

We build on previous work that has taken a Bayesian approach to this problem, specifying prior distributions over the latent network structure and a likelihood of observed activity given this network.

1 code implementation • 18 Jun 2015 • Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams

Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions.

no code implementations • NeurIPS 2015 • Oren Rippel, Jasper Snoek, Ryan P. Adams

In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful representation in which to model and train convolutional neural networks (CNNs).

Ranked #167 on Image Classification on CIFAR-100

1 code implementation • 6 Apr 2015 • Dougal Maclaurin, David Duvenaud, Ryan P. Adams

By tracking the change in entropy over this sequence of transformations during optimization, we form a scalable, unbiased estimate of the variational lower bound on the log marginal likelihood.

4 code implementations • 19 Feb 2015 • Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.

Ranked #155 on Image Classification on CIFAR-100 (using extra training data)

3 code implementations • 18 Feb 2015 • José Miguel Hernández-Lobato, Ryan P. Adams

In principle, the Bayesian approach to learning neural networks does not have these problems.

1 code implementation • 18 Feb 2015 • José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani

Unknown constraints arise in many types of expensive black-box optimization problems.

2 code implementations • 11 Feb 2015 • Dougal Maclaurin, David Duvenaud, Ryan P. Adams

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable.

no code implementations • NeurIPS 2014 • Scott W. Linderman, Christopher H. Stock, Ryan P. Adams

Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry.

no code implementations • 28 Mar 2014 • Elaine Angelino, Eddie Kohler, Amos Waterland, Margo Seltzer, Ryan P. Adams

We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms.

1 code implementation • 22 Mar 2014 • Michael A. Gelbart, Jasper Snoek, Ryan P. Adams

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions.

no code implementations • 22 Mar 2014 • Dougal Maclaurin, Ryan P. Adams

Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference.

2 code implementations • 24 Feb 2014 • David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance.

no code implementations • 20 Feb 2014 • Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar

Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired.

1 code implementation • 5 Feb 2014 • Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions.

1 code implementation • 5 Feb 2014 • Oren Rippel, Michael A. Gelbart, Ryan P. Adams

To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network.

no code implementations • 4 Feb 2014 • Scott W. Linderman, Ryan P. Adams

Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts.

no code implementations • NeurIPS 2013 • Nils E. Napp, Ryan P. Adams

We show algebraically that the steady state concentration of these species correspond to the marginal distributions of the random variables in the graph and validate the results in simulations.

no code implementations • NeurIPS 2013 • Jasper Snoek, Richard Zemel, Ryan P. Adams

Point processes are popular models of neural spiking behavior as they provide a statistical distribution over temporal sequences of spikes and help to reveal the complexities underlying a series of recorded action potentials.

1 code implementation • NeurIPS 2013 • Kevin Swersky, Jasper Snoek, Ryan P. Adams

We demonstrate the utility of this new acquisition function by utilizing a small dataset in order to explore hyperparameter settings for a large dataset.

Ranked #94 on Image Classification on STL-10

no code implementations • NeurIPS 2013 • James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams

In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another.

no code implementations • 8 Apr 2013 • Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka

Applied to mixture modeling, our approach enables the Dirichlet process to simultaneously learn clusters that describe the data and superclusters that define the granularity of parallelization.

no code implementations • NeurIPS 2012 • Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan R. Salakhutdinov, Ryan P. Adams

The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features.

no code implementations • NeurIPS 2012 • James T. Kwok, Ryan P. Adams

We show how to perform MAP inference with DPP priors in latent Dirichlet allocation and in mixture models, leading to better intuition for the latent variable representation and quantitatively improved unsupervised feature extraction, without compromising the generative aspects of the model.

no code implementations • 28 Oct 2012 • Robert Nishihara, Iain Murray, Ryan P. Adams

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them.

4 code implementations • NeurIPS 2012 • Jasper Snoek, Hugo Larochelle, Ryan P. Adams

In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP).

Ranked #187 on Image Classification on CIFAR-10

no code implementations • NeurIPS 2010 • Iain Murray, Ryan P. Adams

The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model.

no code implementations • NeurIPS 2010 • Zoubin Ghahramani, Michael. I. Jordan, Ryan P. Adams

Many data are naturally modeled by an unobserved hierarchical structure.

no code implementations • NeurIPS 2008 • Iain Murray, David Mackay, Ryan P. Adams

Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior.

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