Search Results for author:
Found 6 papers, 1 papers with code
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems.
Enhancing Unsupervised Anomaly Detection with Score-Guided Network
Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems.
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder
Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model.
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets.
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
Time-series is ubiquitous across applications, such as transportation, finance and healthcare.
Decoding Spiking Mechanism with Dynamic Learning on Neuron Population
Our model is aimed to reconstruct neuron information while inferring representations of neuron spiking states.
