Spain

ECAI 2020

ST-MFM: A Spatiotemporal Multi-modal Fusion Model for Urban Anomalies Prediction

anomaly prediction

multi-modal fusion

spatiotemporal features

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Semantic Point Completion Network for 3D Semantic Scene Completion

Learning Class-based Graph Representation for Object Detection

Generating Instantiated Argument Graphs from Probabilistic Information

Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Latent Factor Analysis

Causal query in observational data with hidden variables

Adversarial Learning for Overlapping Community Detection and Network Embedding

Gradual guaranteed coordination in repeated win-lose coordination games

From mixture of longitudinal and non-Gaussian advertising data to Click-Through-Rate prediction

Delving Deeper into the Decoder of Video Captioning

https://d3smihljt9218e.cloudfront.net/transcript/942/subtitle/3455c6a79d25949778f3120601de5c6e.vtt

English (automatic)

ST-MFM: A Spatiotemporal Multi-modal Fusion Model for Urban Anomalies Prediction

Downloads

Next from ECAI 2020

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Similar lecture

Metric Nearness Made Practical

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

ST-MFM: A Spatiotemporal Multi-modal Fusion Model for Urban Anomalies Prediction

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ECAI 2020

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Similar lecture

Metric Nearness Made Practical

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads