Search Results for author: Jonathan Taylor

Found 31 papers, 4 papers with code

One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing

no code implementations15 Apr 2024 Yueyu Hu, Onur G. Guleryuz, Philip A. Chou, Danhang Tang, Jonathan Taylor, Rus Maxham, Yao Wang

In this paper, we propose a new approach to upgrade a 2D video codec to support stereo RGB-D video compression, by wrapping it with a neural pre- and post-processor pair.

Video Compression

Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers

1 code implementation8 Feb 2024 Onur G. Guleryuz, Philip A. Chou, Berivan Isik, Hugues Hoppe, Danhang Tang, Ruofei Du, Jonathan Taylor, Philip Davidson, Sean Fanello

Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures.

Video Compression

MACS: Mass Conditioned 3D Hand and Object Motion Synthesis

no code implementations22 Dec 2023 Soshi Shimada, Franziska Mueller, Jan Bednarik, Bardia Doosti, Bernd Bickel, Danhang Tang, Vladislav Golyanik, Jonathan Taylor, Christian Theobalt, Thabo Beeler

To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach.

Motion Synthesis Object

Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers

no code implementations20 Mar 2023 Berivan Isik, Onur G. Guleryuz, Danhang Tang, Jonathan Taylor, Philip A. Chou

We propose differentiable approximations to key video codec components and demonstrate that, in addition to providing meaningful compression improvements over the standard codec, the neural codes of the sandwich lead to significantly better rate-distortion performance in two important scenarios. When transporting high-resolution video via low-resolution HEVC, the sandwich system obtains 6. 5 dB improvements over standard HEVC.

Motion Compensation Video Compression

Exact Selective Inference with Randomization

no code implementations25 Dec 2022 Snigdha Panigrahi, Kevin Fry, Jonathan Taylor

We introduce a pivot for exact selective inference with randomization.


Black-box Selective Inference via Bootstrapping

no code implementations28 Mar 2022 Sifan Liu, Jelena Markovic-Voronov, Jonathan Taylor

Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso.

Model Selection

Signal Enhancement for Magnetic Navigation Challenge Problem

1 code implementation23 Jul 2020 Albert R. Gnadt, Joseph Belarge, Aaron Canciani, Glenn Carl, Lauren Conger, Joseph Curro, Alan Edelman, Peter Morales, Aaron P. Nielsen, Michael F. O'Keeffe, Christopher V. Rackauckas, Jonathan Taylor, Allan B. Wollaber

It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor.

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

no code implementations12 Nov 2018 Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello

We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.

Denoising Super-Resolution

Why Adaptively Collected Data Have Negative Bias and How to Correct for It

no code implementations7 Aug 2017 Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou

In this paper, we prove that when the data collection procedure satisfies natural conditions, then sample means of the data have systematic \emph{negative} biases.

TerpreT: A Probabilistic Programming Language for Program Induction

no code implementations15 Aug 2016 Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

TerpreT is similar to a probabilistic programming language: a model is composed of a specification of a program representation (declarations of random variables) and an interpreter describing how programs map inputs to outputs (a model connecting unknowns to observations).

BIG-bench Machine Learning Probabilistic Programming +2

High-dimensional regression adjustments in randomized experiments

no code implementations22 Jul 2016 Stefan Wager, Wenfei Du, Jonathan Taylor, Robert Tibshirani

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect.

regression valid +1

Fits Like a Glove: Rapid and Reliable Hand Shape Personalization

no code implementations CVPR 2016 David Joseph Tan, Thomas Cashman, Jonathan Taylor, Andrew Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton

We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.

Selective Sequential Model Selection

no code implementations8 Dec 2015 William Fithian, Jonathan Taylor, Robert Tibshirani, Ryan Tibshirani

Extending the selected-model tests of Fithian et al. (2014), we construct p-values for each step in the path which account for the adaptive selection of the model path using the data.

Model Selection regression

Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose

no code implementations ICCV 2015 Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, Jamie Shotton

In this paper, we show that we can significantly improving upon black box optimization by exploiting high-level knowledge of the structure of the parameters and using a local surrogate energy function.

Hand Pose Estimation Image Generation

Learning an Efficient Model of Hand Shape Variation From Depth Images

no code implementations CVPR 2015 Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, Andrew Fitzgibbon

We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.

Exact Post-Selection Inference for Sequential Regression Procedures

1 code implementation16 Jan 2014 Ryan J. Tibshirani, Jonathan Taylor, Richard Lockhart, Robert Tibshirani

We propose new inference tools for forward stepwise regression, least angle regression, and the lasso.

Methodology 62F03, 62G15

Tests in adaptive regression via the Kac-Rice formula

no code implementations14 Aug 2013 Jonathan Taylor, Joshua Loftus, Ryan Tibshirani

We derive an exact p-value for testing a global null hypothesis in a general adaptive regression problem.


A significance test for the lasso

no code implementations30 Jan 2013 Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani

We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an $\operatorname {Exp}(1)$ asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model).

Statistics Theory Methodology Statistics Theory

A lasso for hierarchical interactions

no code implementations22 May 2012 Jacob Bien, Jonathan Taylor, Robert Tibshirani

We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important.

Bounding Performance Loss in Approximate MDP Homomorphisms

no code implementations NeurIPS 2008 Jonathan Taylor, Doina Precup, Prakash Panagaden

We prove that the difference in the optimal value function of different states can be upper-bounded by the value of this metric, and that the bound is tighter than that provided by bisimulation metrics (Ferns et al. 2004, 2005).

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