
Vitruvion: A Generative Model of Parametric CAD Sketches
Parametric computeraided design (CAD) tools are the predominant way tha...
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Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Generalization is a central challenge for the deployment of reinforcemen...
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Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
In design, fabrication, and control problems, we are often faced with th...
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Active multifidelity Bayesian online changepoint detection
Online algorithms for detecting changepoints, or abrupt shifts in the be...
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Randomized Automatic Differentiation
The successes of deep learning, variational inference, and many other fi...
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SketchGraphs: A LargeScale Dataset for Modeling Relational Geometry in ComputerAided Design
Parametric computeraided design (CAD) is the dominant paradigm in mecha...
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Learning Composable Energy Surrogates for PDE Order Reduction
Metamaterials are an important emerging class of engineered materials i...
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
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On the Difficulty of WarmStarting Neural Network Training
In many realworld deployments of machine learning systems, data arrive ...
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Discrete Object Generation with Reversible Inductive Construction
The success of generative modeling in continuous domains has led to a su...
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A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Sequential decision making in the presence of uncertainty and stochastic...
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SpArSe: Sparse Architecture Search for CNNs on ResourceConstrained Microcontrollers
The vast majority of processors in the world are actually microcontrolle...
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Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
We consider optimization problems in which the objective requires an inn...
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Predicting ElectronIonization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often ...
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Motivating the Rules of the Game for Adversarial Example Research
Advances in machine learning have led to broad deployment of systems wit...
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Compressibility and Generalization in LargeScale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
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Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
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Estimating the Spectral Density of Large Implicit Matrices
Many important problems are characterized by the eigenvalues of a large ...
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PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs)  such as logistic regression, Poisson...
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Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professionalgrade software applications are powerful but complicatedex...
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Variational Boosting: Iteratively Refining Posterior Approximations
We propose a blackbox variational inference method to approximate intra...
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Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
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Bayesian latent structure discovery from multineuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in t...
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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...
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Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes prob...
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Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a dema...
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A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
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Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
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Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
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Dependent Multinomial Models Made Easy: Stick Breaking with the PólyaGamma Augmentation
Many practical modeling problems involve discrete data that are best rep...
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Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
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Early Stopping is Nonparametric Variational Inference
We show that unconverged stochastic gradient descent can be interpreted ...
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Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
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Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive blackbox optimizat...
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Gradientbased Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients ...
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A framework for studying synaptic plasticity with neural spike train data
Learning and memory in the brain are implemented by complex, timevaryin...
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Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely ...
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Firefly Monte Carlo: Exact MCMC with Subsets of Data
Markov chain Monte Carlo (MCMC) is a popular and successful generalpurp...
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Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in glob...
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Avoiding pathologies in very deep networks
Choosing appropriate architectures and regularization strategies for dee...
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Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are wellsuited for modeling repuls...
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Input Warping for Bayesian Optimization of Nonstationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
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Learning Ordered Representations with Nested Dropout
In this paper, we study ordered representations of data in which differe...
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Discovering Latent Network Structure in Point Process Data
Networks play a central role in modern data analysis, enabling us to rea...
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ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesi...
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Parallel MCMC with Generalized Elliptical Slice Sampling
Probabilistic models are conceptually powerful tools for finding structu...
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Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model h...
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