Spain

ECAI 2020

SI-AGAN: Spatial Interpolation with Attentional Generative Adversarial Networks for Environment Monitoring

spatial interpolation

environmental

generative adversarial network

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!

ECAI 2020 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.

technical paper

You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

Maximal Correlation: An Alternative Criterion for Training Generative Networks

Automated Verification of Social Law Robustness for Reactive Agents

Common and Discriminative Semantic Pursuit for Multi-modal Multi-label Learning

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

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

https://d3smihljt9218e.cloudfront.net/transcript/937/subtitle/decd3d98f4bf699580c7a31d914e0ef9.vtt

English (automatic)

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You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

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Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks

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Next from ECAI 2020

You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

Similar lecture

Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks

Downloads