
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible
Machine learning is predicated on the concept of generalization: a model...
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Finite Versus Infinite Neural Networks: an Empirical Study
We perform a careful, thorough, and large scale empirical study of the c...
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A new method for parameter estimation in probabilistic models: Minimum probability flow
Fitting probabilistic models to data is often difficult, due to the gene...
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Exact posterior distributions of wide Bayesian neural networks
Recent work has shown that the prior over functions induced by a deep Ba...
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Infinite attention: NNGP and NTK for deep attention networks
There is a growing amount of literature on the relationship between wide...
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Two equalities expressing the determinant of a matrix in terms of expectations over matrixvector products
We introduce two equations expressing the inverse determinant of a full ...
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Your GAN is Secretly an Energybased Model and You Should use Discriminator Driven Latent Sampling
We show that the sum of the implicit generator logdensity log p_g of a ...
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The large learning rate phase of deep learning: the catapult mechanism
The choice of initial learning rate can have a profound effect on the pe...
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Using a thousand optimization tasks to learn hyperparameter search strategies
We present TaskSet, a dataset of tasks for use in training and evaluatin...
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On the infinite width limit of neural networks with a standard parameterization
There are currently two parameterizations used to derive fixed kernels c...
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Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a library designed to enable research into infinitew...
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Neural reparameterization improves structural optimization
Structural optimization is a popular method for designing objects such a...
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Using learned optimizers to make models robust to input noise
Stateofthe art vision models can achieve superhuman performance on ima...
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The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
We investigate how the final parameters found by stochastic gradient des...
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A RAD approach to deep mixture models
Flow based models such as Real NVP are an extremely powerful approach to...
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A Mean Field Theory of Batch Normalization
We develop a mean field theory for batch normalization in fullyconnecte...
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Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
A longstanding goal in deep learning research has been to precisely char...
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Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit
Recent work has noted that all bad local minima can be removed from neur...
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Measuring the Effects of Data Parallelism on Neural Network Training
Recent hardware developments have made unprecedented amounts of data par...
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Learned optimizers that outperform SGD on wallclock and test loss
Deep learning has shown that learned functions can dramatically outperfo...
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Learned optimizers that outperform SGD on wallclock and validation loss
Deep learning has shown that learned functions can dramatically outperfo...
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Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes
There is a previously identified equivalence between wide fully connecte...
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Adversarial Reprogramming of Neural Networks
Deep neural networks are susceptible to adversarial attacks. In computer...
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Guided evolutionary strategies: escaping the curse of dimensionality in random search
Many applications in machine learning require optimizing a function whos...
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Stochastic natural gradient descent draws posterior samples in function space
Natural gradient descent (NGD) minimises the cost function on a Riemanni...
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PCA of high dimensional random walks with comparison to neural network training
One technique to visualize the training of neural networks is to perform...
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Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000Layer Vanilla Convolutional Neural Networks
In recent years, stateoftheart methods in computer vision have utiliz...
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Learning Unsupervised Learning Rules
A major goal of unsupervised learning is to discover data representation...
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Sensitivity and Generalization in Neural Networks: an Empirical Study
In practice it is often found that large overparameterized neural netwo...
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Adversarial Examples that Fool both Human and Computer Vision
Machine learning models are vulnerable to adversarial examples: small ch...
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Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a generalpurpose method to train Markov chain Monte Carlo ke...
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Deep Neural Networks as Gaussian Processes
A deep fullyconnected neural network with an i.i.d. prior over its para...
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A Correspondence Between Random Neural Networks and Statistical Field Theory
A number of recent papers have provided evidence that practical design q...
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SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
We propose a new technique, Singular Vector Canonical Correlation Analys...
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REBAR: Lowvariance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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Learned Optimizers that Scale and Generalize
Learning to learn has emerged as an important direction for achieving ar...
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Capacity and Trainability in Recurrent Neural Networks
Two potential bottlenecks on the expressiveness of recurrent neural netw...
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Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
There exist many problem domains where the interpretability of neural ne...
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Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
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Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs...
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Deep Information Propagation
We study the behavior of untrained neural networks whose weights and bia...
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Exponential expressivity in deep neural networks through transient chaos
We combine Riemannian geometry with the mean field theory of high dimens...
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On the Expressive Power of Deep Neural Networks
We propose a new approach to the problem of neural network expressivity,...
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Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challengi...
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A universal tradeoff between power, precision and speed in physical communication
Maximizing the speed and precision of communication while minimizing pow...
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A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
In most sampling algorithms, including Hamiltonian Monte Carlo, transiti...
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Deep Knowledge Tracing
Knowledge tracingwhere a machine models the knowledge of a student as...
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
A central problem in machine learning involves modeling complex dataset...
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Analyzing noise in autoencoders and deep networks
Autoencoders have emerged as a useful framework for unsupervised learnin...
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Efficient Methods for Unsupervised Learning of Probabilistic Models
In this thesis I develop a variety of techniques to train, evaluate, and...
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Jascha SohlDickstein
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Staff Research Scientist in the Brain group at Google, Academic Resident at Khan Academy, Visiting scholar in Surya Ganguli's lab at Stanford University, PhD in 2012 in the Redwood Center for Theoretical Neuroscience at UC Berkeley, in Bruno Olshausen's lab.