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Found 25 papers, 14 papers with code
Higher Order Mutual Information Approximation for Feature Selection
Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved.
Translation Synchronization via Truncated Least Squares
In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.
Dynamic Filtering with Large Sampling Field for ConvNets
We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g., 2D images or 3D shapes).
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization
Fusing a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) of the same scene leads to a super-resolution image (SRI), which is information rich spatially and spectrally.
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference.
Can 3D Adversarial Logos Cloak Humans?
Contrary to the traditional adversarial patch, this new form of attack is mapped into the 3D object world and back-propagates to the 2D image domain through differentiable rendering.
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing
Camera Image Signal Processing (ISP) pipelines can get appealing results in different image signal processing tasks.
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors
This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.
Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning
Variational Autoencoders (VAEs) have been shown to be remarkably effective in recovering model latent spaces for several computer vision tasks.
Deep Predictive Learning of Carotid Stenosis Severity
Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function.
Reinforcement Learning of Self Enhancing Camera Image and Signal Processing
Our \textit{Recursive Self Enhancement Reinforcement Learning}(RSE-RL) model views the identification and correction of artifacts as a recursive self-learning and self-improvement exercise and consists of two major sub-modules: (i) The latent feature sub-space clustering/grouping obtained through variational auto-encoders enabling rapid identification of the correspondence and discrepancy between noisy and clean image patches.
Learning Optimal Control with Stochastic Models of Hamiltonian Dynamics
Optimal control problems can be solved by applying the Pontryagin maximum principle and then solving for a Hamiltonian dynamical system.
E-CIR: Event-Enhanced Continuous Intensity Recovery
A camera begins to sense light the moment we press the shutter button.
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation
We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data.
Sample Efficient Learning of Factored Embeddings of Tensor Fields
We learn approximate full-rank and compact tensor sketches with decompositive representations providing compact space, time and spectral embeddings of tensor fields.
A Particle-based Sparse Gaussian Process Optimizer
Task learning in neural networks typically requires finding a globally optimal minimizer to a loss function objective.
Solving the Side-Chain Packing Arrangement of Proteins from Reinforcement Learned Stochastic Decision Making
In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem.
DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
We additionally show that our approach easily extends to video super-resolution when combined with recent advances in implicit neural representation.
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
GenCorres addresses this issue by learning an implicit generator from the input shapes, which provides intermediate shapes between two arbitrary shapes.
Motion Code: Robust Time series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
Instead of treating time series as a static vector or a data sequence as often seen in previous methods, we introduce a novel framework that considers each time series, not necessarily of fixed length, as a sample realization of a continuous-time stochastic process.
DPO: Differential reinforcement learning with application to optimal configuration search
Reinforcement learning (RL) with continuous state and action spaces remains one of the most challenging problems within the field.
