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Found 7 papers, 1 papers with code
Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning
We present Generalized Contrastive Divergence (GCD), a novel objective function for training an energy-based model (EBM) and a sampler simultaneously.
A Statistical Manifold Framework for Point Cloud Data
The only requirement on the part of the user is the choice of a meaningful underlying probability distribution, which is more intuitive and natural to make than what is required in existing ad hoc formulations.
Regularized Autoencoders for Isometric Representation Learning
The recent success of autoencoders for representation learning can be traced in large part to the addition of a regularization term.
Adversarial Distributions Against Out-of-Distribution Detectors
As an outlier may deviate from the training distribution in unexpected ways, an ideal OOD detector should be able to detect all types of outliers.
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection
This phenomenon, which we refer to as outlier reconstruction, has a detrimental effect on the use of autoencoders for outlier detection, as an autoencoder will misclassify a clear outlier as being in-distribution.
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection
In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing.
A Linear-Time Variational Integrator for Multibody Systems
Our key contribution is to derive a recursive algorithm that evaluates DEL equations in $O(n)$, which scales up well for complex multibody systems such as humanoid robots.
Robotics
