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Found 31 papers, 4 papers with code
The MITLL-AFRL IWSLT 2016 Systems
This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign.
One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing
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.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
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.
MACS: Mass Conditioned 3D Hand and Object Motion Synthesis
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.
Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images
We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images.
Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers
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.
Exact Selective Inference with Randomization
We introduce a pivot for exact selective inference with randomization.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
Black-box Selective Inference via Bootstrapping
Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso.
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting.
Signal Enhancement for Magnetic Navigation Challenge Problem
It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor.
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
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
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.
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
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.
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
A TerpreT model is composed of a specification of a program representation and an interpreter that describes how programs map inputs to outputs.
TerpreT: A Probabilistic Programming Language for Program Induction
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).
High-dimensional regression adjustments in randomized experiments
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.
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization
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
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.
Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose
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.
3D Scanning Deformable Objects With a Single RGBD Sensor
We present a 3D scanning system for deformable objects that uses only a single Kinect sensor.
Large-Scale and Drift-Free Surface Reconstruction Using Online Subvolume Registration
Our method supports online model correction, without needing to reprocess or store any input depth data.
Learning an Efficient Model of Hand Shape Variation From Depth Images
We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.
User-Specific Hand Modeling from Monocular Depth Sequences
We focus on modeling the human hand, and assume that a single rough template model is available.
Exact Post-Selection Inference for Sequential Regression Procedures
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
We derive an exact p-value for testing a global null hypothesis in a general adaptive regression problem.
Methodology
A significance test for the lasso
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
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
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).
