We propose that humans use compositionality: complex structure is decomposed into simpler building blocks. David Duvenaud duvenaud. Check out the tutorial and the Empirical Inference. Chris Cremer, Aristotle on Meaning and Essence. Assistant Professor, University of Toronto. Research on machine learning, inference, and automatic modeling. We present code that computes stochastic gradients of the evidence lower bound for any differentiable posterior. Our method trains a neural net to output approximately optimal weights as a function of hyperparameters. Related: Richard Mann wrote a gripping blog post We propose a new family of efficient and expressive deep generative models of graphs. For example, we do stochastic variational inference in a deep Bayesian neural network. Variational autoencoders can be regularized to produce disentangled representations, in which each latent dimension has a distinct meaning. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. I'm an assistant professor at the University of Toronto. International Conference on Machine Learning, 2018 These data-driven features are more interpretable, and have better predictive performance on a variety of tasks. We also learn a distilled dataset where each feature in each datapoint is a hyperparameter, and tune millions of regularization hyperparameters. likelihoods. This allows end-to-end training of ODEs within larger models. However, existing regularization schemes also hurt the model's ability to model the data. We prove that our model-based procedure converges in the noisy quadratic setting. We give a simple recipe for reducing the variance of the gradient of the variational evidence lower bound. Verified email at cs.toronto.edu - Homepage. Neural ODEs become expensive to solve numerically as training progresses. Every teacher and class are different, and knowing what to expect can help students best prepare themselves to succeed. We adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. This adds overhead, but scales to large state spaces and dynamics models. Autograd automatically differentiates native Python and Numpy code. We introduce a new family of deep neural network models. These models can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes. Our approach only requires adding a simple normalization step during training. David Duvenaud Includes stochastic variational inference for fitting latent SDE time series models. We track the loss of entropy during optimization to get a scalable estimate of the marginal likelihood. About Us. We support ministers in leading the nation’s health and social care to help people live more independent, healthier lives for longer. Alumni. We meta-learn information helpful for training on a particular task or dataset, leveraging recent work on implicit differentiation. Instead of the usual Monte-Carlo based methods for computing integrals of likelihood functions, we instead construct a surrogate model of the likelihood function, and infer its integral conditioned on a set of evaluations. The standard interpretation of importance-weighted autoencoders is that they maximize a tighter, multi-sample lower bound than the standard evidence lower bound. Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Amortized inference allows latent-variable models to scale to large datasets. The low-dimensional latent mixture model summarizes the properties of the high-dimensional density manifolds describing the data. Previously, I was a postdoc in the Harvard Intelligent Probabilistic Systems group, worki Models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. Sort. David Duvenaud. We show that you can reinterpret standard classification architectures as energy-based generative models and train them as such. Invertible residual networks provide transformations where only Lipschitz conditions rather than architectural constraints are needed for enforcing invertibility. | bibtex Doing this allows us to achieve state-of-the-art performance at both generative and discriminative modeling in a single model. This work formed my M.Sc. David Duvenaud. We give an alternate interpretation: it optimizes the standard lower bound, but using a more complex distribution, which we show how to visualize. Adding this energy-based training also improves calibration, out-of-distribution detection, and adversarial robustness. examples directory. We also give a principled, classifier-free measure of disentanglement called the mutual information gap. David Duvenaud. To compute likelihoods, we introduce a tractable approximation to the Jacobian log-determinant of a residual block. We introduce a family of restricted neural network architectures that allow efficient computation of a family of differential operators involving dimension-wise derivatives, such as the divergence. 4 months ago [D] Self Tuning Networks. My research focuses on constructing deep probabilistic models to help predict, explain and design things. To address this problem, we define a new kernel for conditional parameter spaces that explicitly includes information about which parameters are relevant in a given structure. Uses virtual Brownian trees for constant memory cost. I took his summer class which was a pain. Do your part and I promise you that you will get an A, not an easy A, but an A. I hope to bring all these lists closer to 0 when I get time. If you fit a mixture of Gaussians to a single cluster that is curved or heavy-tailed, your model will report that the data contains many clusters! This Bayesian interpretation of SGD gives a theoretical foundation for popular tricks such as early stopping and ensembling. The output of the network is computed using a black-box differential equation solver. Skip slideshow. Thousands of schools from the USA, Canada and the UK are included on … David spent two summers in the machine vision team at Google Research, and also co-founded Invenia, an energy forecasting and trading company. We emphasize how easy it is to construct scalable inference methods using only automatic differentiation. Bayesian neural nets combine the flexibility of deep learning with uncertainty estimation, but are usually approximated using a fully-factorized Guassian. The best professor I ever had opened the first class with his Rate My Professor reviews. For example: We explore the use of exact per-sample Hessian-vector products and gradients to construct optimizers that are self-tuning and hyperparameter-free. Searching UMUC professor ratings has never been easier. We use our method to fit stochastic dynamics defined by neural networks, achieving competitive performance on a 50-dimensional motion capture dataset. | slides. I'm an assistant professor at the University of Toronto. Oh yes, and classical music and jazz. A prototype for the automatic statistician project. We compute exact gradients of the validation loss with respect to all hyperparameters by differentiating through the entire training procedure. How could an AI do statistics? In addition, we combine our method with gradient-based stochastic variational inference for latent stochastic differential equations. Harvard Intelligent Probabilistic Systems, Max Planck Institute for Intelligent Systems, CSC412: Probabilistic Learning and Reasoning, STA414: Statistical Methods for Machine Learning, STA4273: Learning Discrete Latent Structure, CSC2541: Differentiable Inference and Generative Models, stochastic variational inference in a deep Bayesian neural network, images labeled only by what objects they contain. Follow. Every teacher and class are different, and knowing what to expect can help students best prepare themselves to succeed. Reddit gives you the best of the internet in one place. We show that natural gradient ascent with adaptive weight noise implicitly fits a variational Gassuain posterior. We formalize this idea using a grammar over Gaussian process kernels. David Charles Howard H. Newman Professor of Philosophy and Classics (Leave of absence Spring 2021) Address: 344 College St, New Haven, CT 06511-6629. Paper due every week along with readings that turn into quizzes. All the latest breaking UK and world news with in-depth comment and analysis, pictures and videos from MailOnline and the Daily Mail. This allows gradient-based optimization through the space of chemical compounds. We apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. Please consider supporting us … This list is based on what was entered into the 'organiser' field in a talk. 350 Withers Hall, Campus Box 8108, Raleigh, NC 27695-8108. Definition in Greek Philosophy. Our approach contrasts with ad-hoc in-filling approaches, such as blurring or injecting noise, which generate inputs far from the data distribution, and ignore informative relationships between different parts of the image. We develop a molecular autoencoder, which converts discrete representations of molecules to and from a continuous representation. The u/DavidDuvenaud community on Reddit. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. When can we trust our experiments? Possible Matching Profiles. We give a tractable unbiased estimate of the log density, and improve these models in other ways. Find & rate your professors … My research focuses on constructing deep probabilistic models to help predict, explain and design things. Title. He did his Ph.D. at the University of Cambridge, studying Bayesian nonparametrics with Zoubin Ghahramani and Carl Rasmussen. David Kristjanson Duvenaud. We show that people prefer compositional extrapolations, and argue that this is consistent with broad principles of human cognition. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. Frank R. Schmidt Bosch Center … About Us. This is just contents of my never ending lists of tasks I tagged in 2Do with read, watch and check tags.. All lists are sorted by priority. David Duvenaud is an assistant professor in computer science and statistics at the University of Toronto. 919.515.2483 Reddit gives you the best of the internet in one place. The MachineLearning at Columbia mailing list is a good source of informationabout talks and other events on campus. Though we do not see similar gains in deep learning tasks, we match the performance of well-tuned optimizers. David Duvenaud is an assistant professor in computer science and statistics at the University of Toronto. We show that some standard differential equation solvers are equivalent to Gaussian process predictive means, giving them a natural way to handle uncertainty. To optimize the overall architecture of a neural network along with its hyperparameters, we must be able to relate the performance of nets having differing numbers of hyperparameters. We use the implicit function theorem to scalably approximate gradients of the validation loss with respect to hyperparameters. Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks. backpropagation), which means it's efficient for This architecture generalizes standard molecular fingerprints. Block user. Machine Learning Bayesian Statistics Approximate Inference. To search through an open-ended class of structured, nonparametric regression models, we introduce a simple grammar which specifies composite kernels. We use this framework to automatically segment and categorize mouse behavior from raw depth video. We propose a general modeling and inference framework that combines the complementary 203 432-1698. david.charles@yale.edu. Our proposed architecture has a Jacobian matrix composed of diagonal and hollow (zero-diagonal) components. Learn more about blocking users. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Publications. To suggest better neural network architectures, we analyze the properties of different priors on compositions of functions. Talks organised by Prof David Duvenaud. Check out professor ratings from University of Maryland-University College students, as well as comments from past students. He’s done groundbreaking work on neural ODEs, and has been at the cutting edge of the field for most of the last decade. In an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. Our initial experiments indicate that when training deep nets our optimizer works too well, in a sense - it descends into regions of high variance and high curvature early on in the optimization, and gets stuck there. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. It can handle loops, ifs, recursion and closures, and it can even take derivatives of its own derivatives. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost. This work is part of the larger probabilistic numerics research agenda, which interprets numerical algorithms as inference procedures so they can be better understood and extended. By combining information across different scales, we use image-level labels (such as this image contains a cat) to infer what different classes of objects look like at the pixel-level, and where they occur in images. We show how to efficiently integrate over exponentially-many ways of modeling a function as a sum of low-dimensional functions. This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, allowing us to scale to modern-size convnets. As part of the deal – which will see ServiceNow keep Element AI’s research scientists and patents and effectively abandon its business – the buyer has agreed to pay US$10-million to key employees and consultants including Mr. Gagne and Dr. Bengio as part of a retention plan. Biography. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer.
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