Spain

ECAI 2020

Robust Submodular Minimization with Applications to Cooperative Modeling

assignment

image correspondence

cooperative modeling

graph algorithms

combinatorial constraints

submodular minimization

robust optimization

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!

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.

Minimizing and recovering from the effect of concept drift via feature selection

A Two-Stream Network with Image-to-Class Deep Metric for Few-Shot Classification

Dual Rejection Sampling for Wasserstein Auto-Encoders

Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion

Feature importance estimation with self-attention networks

Online Learning to Accelerate Neural Network Inference with Traveling Classifiers

Topological Bayesian Optimization with Persistence Diagrams

Convolutional Dictionary Pair Learning Network for Image Representation Learning

A Conditional Perspective on Iterated Contraction

SAI: A Sensible Artificial Intelligence that plays Go

https://d3smihljt9218e.cloudfront.net/transcript/1156/subtitle/40d8d494fb07299fde966ec11db831e8.vtt

English (automatic)

Robust Submodular Minimization with Applications to Cooperative Modeling

Downloads

Next from ECAI 2020

Minimizing and recovering from the effect of concept drift via feature selection

Similar lecture

Improving Fairness Generalization Through a Sample-Robust Optimization Method

Robust Submodular Minimization with Applications to Cooperative Modeling

.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

Minimizing and recovering from the effect of concept drift via feature selection

Similar lecture

Improving Fairness Generalization Through a Sample-Robust Optimization Method

Downloads