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Found 9 papers, 4 papers with code
Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Particle filtering is a standard Monte-Carlo approach for a wide range of sequential inference tasks.
Continuous Latent Process Flows
Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines.
Adaptive Appearance Rendering
Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network.
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.
Variational Hyper RNN for Sequence Modeling
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence.
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
In this paper, we aim to characterize adversarial examples based on spatial context information in semantic segmentation.
Sparsely Aggregated Convolutional Networks
We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers.
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed.
