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Found 16 papers, 7 papers with code
On the Value of Infinite Gradients in Variational Autoencoder Models
A number of recent studies of continuous variational autoencoder (VAE) models have noted, either directly or indirectly, the tendency of various parameter gradients to drift towards infinity during training.
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task
In this paper, we study how SSL can enhance the performance of the out-of-distribution (OOD) detection task.
Trajectory Prediction for Autonomous Driving with Topometric Map
The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map.
Attentional Graph Neural Network for Parking-slot Detection
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection.
Further Analysis of Outlier Detection with Deep Generative Models
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.
Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter
The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise.
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions.
Lidar-based Object Classification with Explicit Occlusion Modeling
Object detection and classification based on lidar point cloud is a key technology for UGV.
Diagnosing and Enhancing VAE Models
Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood.
Compressing Neural Networks using the Variational Information Bottelneck
Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.
Compressing Neural Networks using the Variational Information Bottleneck
Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.
Understanding and Predicting The Attractiveness of Human Action Shot
Selecting attractive photos from a human action shot sequence is quite challenging, because of the subjective nature of the "attractiveness", which is mainly a combined factor of human pose in action and the background.
Hidden Talents of the Variational Autoencoder
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.
Semi-supervised Gender Classification with Joint Textual and Social Modeling
In gender classification, labeled data is often limited while unlabeled data is ample.
Multivariate Bernoulli distribution
In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes.
