Search Results for author: Joseph Marino

Found 17 papers, 6 papers with code

Bridging the Gap Between Target Networks and Functional Regularization

no code implementations21 Oct 2022 Alexandre Piche, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan

However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.

DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides

1 code implementation CVPR 2022 Parmida Ghahremani, Joseph Marino, Ricardo Dodds, Saad Nadeem

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.

Insights from Generative Modeling for Neural Video Compression

no code implementations28 Jul 2021 Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

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.

Video Compression

Beyond Target Networks: Improving Deep $Q$-learning with Functional Regularization

no code implementations4 Jun 2021 Alexandre Piché, Valentin Thomas, Joseph Marino, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan

However, training is often unstable due to fast-changing target Q-values, and target networks are employed to regularize the Q-value estimation and stabilize training by using an additional set of lagging parameters.

Q-Learning

SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS

no code implementations ICLR Workshop Neural_Compression 2021 Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

There has been a recent surge of interest in neural video compression models that combines data-driven dimensionality reduction with learned entropy coding.

Dimensionality Reduction Video Compression

Generative Video Compression as Hierarchical Variational Inference

no code implementations pproximateinference AABI Symposium 2021 Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.

Density Estimation Variational Inference +1

Predictive Coding, Variational Autoencoders, and Biological Connections

no code implementations NeurIPS Workshop Neuro_AI 2019 Joseph Marino

This paper reviews predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas.

BIG-bench Machine Learning

Iterative Amortized Policy Optimization

1 code implementation NeurIPS 2021 Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue

Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.

Continuous Control reinforcement-learning +1

Hierarchical Autoregressive Modeling for Neural Video Compression

1 code implementation ICLR 2021 Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.

Density Estimation Video Compression

Improving Sequential Latent Variable Models with Autoregressive Flows

no code implementations7 Oct 2020 Joseph Marino, Lei Chen, JiaWei He, Stephan Mandt

We propose an approach for improving sequence modeling based on autoregressive normalizing flows.

Variational Autoencoders with Jointly Optimized Latent Dependency Structure

no code implementations ICLR 2019 Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann

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.

A General Method for Amortizing Variational Filtering

1 code implementation NeurIPS 2018 Joseph Marino, Milan Cvitkovic, Yisong Yue

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.

Inference Optimization Variational Inference

Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling

no code implementations20 Oct 2018 Saad Nadeem, Joseph Marino, Xianfeng GU, Arie Kaufman

The use of Fiedler vectors and the segmented folds presents a precise way of visualizing corresponding regions across datasets and visual modalities.

Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos

no code implementations17 Sep 2018 Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, Arie Kaufman

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.

Navigate

Iterative Amortized Inference

1 code implementation ICML 2018 Joseph Marino, Yisong Yue, Stephan Mandt

The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.

Inference Optimization Variational Inference

Learning to Infer

no code implementations ICLR 2018 Joseph Marino, Yisong Yue, Stephan Mandt

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).

Inference Optimization

Cannot find the paper you are looking for? You can Submit a new open access paper.