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Found 19 papers, 9 papers with code
Learning to Infer
Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).
Probabilistic Video Generation using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data.
Iterative Amortized Inference
The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.
Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos
Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer.
Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling
The use of Fiedler vectors and the segmented folds presents a precise way of visualizing corresponding regions across datasets and visual modalities.
A General Method for Amortizing Variational Filtering
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i. e. filtering.
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure.
Improving Sequential Latent Variable Models with Autoregressive Flows
We propose an approach for improving sequence modeling based on autoregressive normalizing flows.
Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.
Iterative Amortized Policy Optimization
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.
Predictive Coding, Variational Autoencoders, and Biological Connections
This paper reviews predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas.
Generative Video Compression as Hierarchical Variational Inference
Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.
SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS
There has been a recent surge of interest in neural video compression models that combines data-driven dimensionality reduction with learned entropy coding.
Bridging the Gap Between Target Networks and Functional Regularization
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
Insights from Generative Modeling for Neural Video Compression
While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images.
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression.
Bridging the Gap Between Target Networks and Functional Regularization
However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.
An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment
We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
