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...
share
research
∙
07/23/2023
JAX FDM: A differentiable solver for inverse form-finding
We introduce JAX FDM, a differentiable solver to design mechanically eff...
research
∙
06/08/2023
Representing and Learning Functions Invariant Under Crystallographic Groups
Crystallographic groups describe the symmetries of crystals and other re...
research
∙
01/31/2023
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
Intelligent biological systems are characterized by their embodiment in ...
research
∙
11/03/2022
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
Partial differential equations (PDEs) are often computationally challeng...
research
∙
10/04/2022
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Markov chain Monte Carlo (MCMC) is an established approach for uncertain...
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...
research
∙
12/22/2021
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
Safety-critical applications require controllers/policies that can guara...
research
∙
09/29/2021
Vitruvion: A Generative Model of Parametric CAD Sketches
Parametric computer-aided design (CAD) tools are the predominant way tha...
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...
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...
research
∙
03/26/2021
Active multi-fidelity Bayesian online changepoint detection
Online algorithms for detecting changepoints, or abrupt shifts in the be...
research
∙
07/20/2020
Randomized Automatic Differentiation
The successes of deep learning, variational inference, and many other fi...
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...
research
∙
05/13/2020
Learning Composable Energy Surrogates for PDE Order Reduction
Meta-materials are an important emerging class of engineered materials i...
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...
research
∙
10/18/2019
On the Difficulty of Warm-Starting Neural Network Training
In many real-world deployments of machine learning systems, data arrive ...
research
∙
07/18/2019
Discrete Object Generation with Reversible Inductive Construction
The success of generative modeling in continuous domains has led to a su...
research
∙
06/24/2019
A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Sequential decision making in the presence of uncertainty and stochastic...
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...
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...
research
∙
11/21/2018
Predicting Electron-Ionization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often ...
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...
research
∙
04/16/2018
Compressibility and Generalization in Large-Scale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
research
∙
02/28/2018
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
research
∙
02/09/2018
Estimating the Spectral Density of Large Implicit Matrices
Many important problems are characterized by the eigenvalues of a large ...
research
∙
09/26/2017
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs) -- such as logistic regression, Poisson...
research
∙
05/22/2017
Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
research
∙
04/17/2017
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professional-grade software applications are powerful but complicated-ex...
research
∙
11/20/2016
Variational Boosting: Iteratively Refining Posterior Approximations
We propose a black-box variational inference method to approximate intra...
research
∙
10/26/2016
Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
research
∙
10/26/2016
Bayesian latent structure discovery from multi-neuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in t...
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...
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...
research
∙
02/16/2016
Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a dema...
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...
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...
research
∙
11/08/2015
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
research
∙
09/30/2015
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
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...
research
∙
06/11/2015
Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
research
∙
04/06/2015
Early Stopping is Nonparametric Variational Inference
We show that unconverged stochastic gradient descent can be interpreted ...
research
∙
02/19/2015
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
research
∙
02/18/2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
research
∙
02/18/2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive black-box optimizat...
research
∙
02/11/2015
Gradient-based Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients ...
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...
research
∙
03/28/2014
Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely ...
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...
research
∙
03/22/2014
