b'Ryan P. Adams'

research

∙ 09/10/2023

A rapid and automated computational approach to the design of multistable soft actuators

We develop an automated computational modeling framework for rapid gradi...

0 Mehran Mirramezani, et al. ∙

research

∙ 07/23/2023

JAX FDM: A differentiable solver for inverse form-finding

We introduce JAX FDM, a differentiable solver to design mechanically eff...

0 Rafael Pastrana, et al. ∙

research

∙ 06/08/2023

Representing and Learning Functions Invariant Under Crystallographic Groups

Crystallographic groups describe the symmetries of crystals and other re...

0 Ryan P. Adams, et al. ∙

research

∙ 01/31/2023

Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity

Intelligent biological systems are characterized by their embodiment in ...

0 Deniz Oktay, et al. ∙

research

∙ 11/03/2022

Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh

Partial differential equations (PDEs) are often computationally challeng...

0 Tian Qin, et al. ∙

research

∙ 10/04/2022

Multi-fidelity Monte Carlo: a pseudo-marginal approach

Markov chain Monte Carlo (MCMC) is an established approach for uncertain...

14 Diana Cai, et al. ∙

research

∙ 05/31/2022

More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites

In 3D printing, stiff fibers (e.g., carbon fiber) can reinforce thermopl...

0 Xingyuan Sun, et al. ∙

research

∙ 12/22/2021

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Safety-critical applications require controllers/policies that can guara...

12 Athindran Ramesh Kumar, et al. ∙

research

∙ 09/29/2021

Vitruvion: A Generative Model of Parametric CAD Sketches

Parametric computer-aided design (CAD) tools are the predominant way tha...

9 Ari Seff, et al. ∙

research

∙ 07/13/2021

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Generalization is a central challenge for the deployment of reinforcemen...

5 Dibya Ghosh, et al. ∙

research

∙ 06/16/2021

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

In design, fabrication, and control problems, we are often faced with th...

1 Xingyuan Sun, et al. ∙

research

∙ 03/26/2021

Active multi-fidelity Bayesian online changepoint detection

Online algorithms for detecting changepoints, or abrupt shifts in the be...

16 Gregory W. Gundersen, et al. ∙

research

∙ 07/20/2020

Randomized Automatic Differentiation

The successes of deep learning, variational inference, and many other fi...

38 Deniz Oktay, et al. ∙

research

∙ 07/16/2020

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

Parametric computer-aided design (CAD) is the dominant paradigm in mecha...

26 Ari Seff, et al. ∙

research

∙ 05/13/2020

Learning Composable Energy Surrogates for PDE Order Reduction

Meta-materials are an important emerging class of engineered materials i...

0 Alex Beatson, et al. ∙

research

∙ 04/01/2020

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...

8 Yucen Luo, et al. ∙

research

∙ 10/18/2019

On the Difficulty of Warm-Starting Neural Network Training

In many real-world deployments of machine learning systems, data arrive ...

18 Jordan T. Ash, et al. ∙

research

∙ 07/18/2019

Discrete Object Generation with Reversible Inductive Construction

The success of generative modeling in continuous domains has led to a su...

3 Ari Seff, et al. ∙

research

∙ 06/24/2019

A Theoretical Connection Between Statistical Physics and Reinforcement Learning

Sequential decision making in the presence of uncertainty and stochastic...

6 Jad Rahme, et al. ∙

research

∙ 05/28/2019

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

The vast majority of processors in the world are actually microcontrolle...

5 Igor Fedorov, et al. ∙

research

∙ 05/16/2019

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums

We consider optimization problems in which the objective requires an inn...

4 Alex Beatson, et al. ∙

research

∙ 11/21/2018

Predicting Electron-Ionization Mass Spectrometry using Neural Networks

When confronted with a substance of unknown identity, researchers often ...

4 Jennifer N. Wei, et al. ∙

research

∙ 07/18/2018

Motivating the Rules of the Game for Adversarial Example Research

Advances in machine learning have led to broad deployment of systems wit...

0 Justin Gilmer, et al. ∙

research

∙ 04/16/2018

Compressibility and Generalization in Large-Scale Deep Learning

Modern neural networks are highly overparameterized, with capacity to su...

0 Wenda Zhou, et al. ∙

research

∙ 02/28/2018

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical c...

0 Jeffrey Regier, et al. ∙

research

∙ 02/09/2018

Estimating the Spectral Density of Large Implicit Matrices

Many important problems are characterized by the eigenvalues of a large ...

0 Ryan P. Adams, et al. ∙

research

∙ 09/26/2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

Generalized linear models (GLMs) -- such as logistic regression, Poisson...

0 Jonathan H. Huggins, et al. ∙

research

∙ 05/22/2017

Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics an...

0 Andrew C. Miller, et al. ∙

research

∙ 04/17/2017

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Professional-grade software applications are powerful but complicated-ex...

0 Ardavan Saeedi, et al. ∙

research

∙ 11/20/2016

Variational Boosting: Iteratively Refining Posterior Approximations

We propose a black-box variational inference method to approximate intra...

0 Andrew C. Miller, et al. ∙

research

∙ 10/26/2016

Recurrent switching linear dynamical systems

Many natural systems, such as neurons firing in the brain or basketball ...

0 Scott W. Linderman, et al. ∙

research

∙ 10/26/2016

Bayesian latent structure discovery from multi-neuron recordings

Neural circuits contain heterogeneous groups of neurons that differ in t...

0 Scott W. Linderman, et al. ∙

research

∙ 06/19/2016

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation

A good clustering can help a data analyst to explore and understand a da...

0 Akash Srivastava, et al. ∙

research

∙ 03/20/2016

Composing graphical models with neural networks for structured representations and fast inference

We propose a general modeling and inference framework that composes prob...

0 Matthew J. Johnson, et al. ∙

research

∙ 02/16/2016

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a dema...

0 Elaine Angelino, et al. ∙

research

∙ 11/30/2015

A General Framework for Constrained Bayesian Optimization using Information-based Search

We present an information-theoretic framework for solving global black-b...

0 José Miguel Hernández-Lobato, et al. ∙

research

∙ 11/17/2015

Predictive Entropy Search for Multi-objective Bayesian Optimization

We present PESMO, a Bayesian method for identifying the Pareto set of mu...

0 Daniel Hernández-Lobato, et al. ∙

research

∙ 11/08/2015

Sandwiching the marginal likelihood using bidirectional Monte Carlo

Computing the marginal likelihood (ML) of a model requires marginalizing...

0 Roger B. Grosse, et al. ∙

research

∙ 09/30/2015

Convolutional Networks on Graphs for Learning Molecular Fingerprints

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

0 David Duvenaud, et al. ∙

research

∙ 06/18/2015

Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation

Many practical modeling problems involve discrete data that are best rep...

0 Scott W. Linderman, et al. ∙

research

∙ 06/11/2015

Spectral Representations for Convolutional Neural Networks

Discrete Fourier transforms provide a significant speedup in the computa...

0 Oren Rippel, et al. ∙

research

∙ 04/06/2015

Early Stopping is Nonparametric Variational Inference

We show that unconverged stochastic gradient descent can be interpreted ...

0 Dougal Maclaurin, et al. ∙

research

∙ 02/19/2015

Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian optimization is an effective methodology for the global optimiz...

0 Jasper Snoek, et al. ∙

research

∙ 02/18/2015

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Large multilayer neural networks trained with backpropagation have recen...

0 José Miguel Hernández-Lobato, et al. ∙

research

∙ 02/18/2015

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

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

0 José Miguel Hernández-Lobato, et al. ∙

research

∙ 02/11/2015

Gradient-based Hyperparameter Optimization through Reversible Learning

Tuning hyperparameters of learning algorithms is hard because gradients ...

0 Dougal Maclaurin, et al. ∙

research

∙ 11/14/2014

A framework for studying synaptic plasticity with neural spike train data

Learning and memory in the brain are implemented by complex, time-varyin...

0 Scott W. Linderman, et al. ∙

research

∙ 03/28/2014

Accelerating MCMC via Parallel Predictive Prefetching

We present a general framework for accelerating a large class of widely ...

0 Elaine Angelino, et al. ∙

research

∙ 03/22/2014

Firefly Monte Carlo: Exact MCMC with Subsets of Data

Markov chain Monte Carlo (MCMC) is a popular and successful general-purp...

0 Dougal Maclaurin, et al. ∙

research

∙ 03/22/2014

Bayesian Optimization with Unknown Constraints

Recent work on Bayesian optimization has shown its effectiveness in glob...

0 Michael A. Gelbart, et al. ∙

Ryan P. Adams

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