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07/14/2023
Variational Prediction
Bayesian inference offers benefits over maximum likelihood, but it also ...
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06/01/2023
Diffusion Self-Guidance for Controllable Image Generation
Large-scale generative models are capable of producing high-quality imag...
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04/27/2023
Learning a Diffusion Prior for NeRFs
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D repr...
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03/23/2023
DreamBooth3D: Subject-Driven Text-to-3D Generation
We present DreamBooth3D, an approach to personalize text-to-3D generativ...
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11/17/2022
VeLO: Training Versatile Learned Optimizers by Scaling Up
While deep learning models have replaced hand-designed features across m...
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10/05/2022
Imagen Video: High Definition Video Generation with Diffusion Models
We present Imagen Video, a text-conditional video generation system base...
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09/29/2022
DreamFusion: Text-to-3D using 2D Diffusion
Recent breakthroughs in text-to-image synthesis have been driven by diff...
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12/02/2021
Zero-Shot Text-Guided Object Generation with Dream Fields
We combine neural rendering with multi-modal image and text representati...
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10/05/2021
Autoregressive Diffusion Models
We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...
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07/01/2021
Variational Diffusion Models
Diffusion-based generative models have demonstrated a capacity for perce...
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11/17/2020
VIB is Half Bayes
In discriminative settings such as regression and classification there a...
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10/15/2020
Non-saturating GAN training as divergence minimization
Non-saturating generative adversarial network (GAN) training is widely u...
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09/23/2020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Much as replacing hand-designed features with learned functions has revo...
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05/20/2020
What makes for good views for contrastive learning
Contrastive learning between multiple views of the data has recently ach...
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02/27/2020
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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02/20/2020
Regularized Autoencoders via Relaxed Injective Probability Flow
Invertible flow-based generative models are an effective method for lear...
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02/07/2020
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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01/31/2020
On Implicit Regularization in β-VAEs
While the impact of variational inference (VI) on posterior inference in...
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10/22/2019
Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of vari...
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10/21/2019
On Predictive Information Sub-optimality of RNNs
Certain biological neurons demonstrate a remarkable capability to optima...
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06/06/2019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Deploying machine learning systems in the real world requires both high ...
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05/24/2019
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
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05/16/2019
On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many proble...
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01/10/2019
Preventing Posterior Collapse with delta-VAEs
Due to the phenomenon of "posterior collapse," current latent variable g...
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11/01/2017
An Information-Theoretic Analysis of Deep Latent-Variable Models
We present an information-theoretic framework for understanding trade-of...
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03/13/2017
Continual Learning Through Synaptic Intelligence
While deep learning has led to remarkable advances across diverse applic...
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11/24/2016
Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
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11/07/2016
Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs...
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11/03/2016
Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete str...
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06/16/2016
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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06/16/2016
On the Expressive Power of Deep Neural Networks
We propose a new approach to the problem of neural network expressivity,...
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06/02/2016
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
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11/10/2015
The Fast Bilateral Solver
We present the bilateral solver, a novel algorithm for edge-aware smooth...
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06/06/2014
