Spain

ECAI 2020

Multi-Stage Transfer Learning with an Application to Selection Process

selection-process

multi-stage

transfer-learning

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.

Improving Supervised Superpixel-Based Codebook Representations by Local Convolutional Features

Incorporating Semantic Dependencies Extracted from Knowledge Graphs into Joint Inference template-based Information Extraction

On Measuring Inconsistency in Relational Databases with Denial Constraints

Classifying the classifier: dissecting the weight space of neural networks

Is hardness inherent in computational problems? Performance of human and electronic computers on random instances of the 0-1 knapsack problem.

A Multilayered Informative Random Walk for Attributed Social Network Embedding

Semantics of negative sequential patterns

Maximum Entropy Reinforced Single Object Visual Tracking

Preprocessing in Incomplete MaxSAT Solving

Resilient Distributed Constraints Optimization in Physical Multi-Agent Systems

https://d3smihljt9218e.cloudfront.net/transcript/1188/subtitle/009bb3c25a9807eedde5c3276aa3543f.vtt

English (automatic)

Multi-Stage Transfer Learning with an Application to Selection Process

Downloads

Next from ECAI 2020

Improving Supervised Superpixel-Based Codebook Representations by Local Convolutional Features

Similar lecture

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models

Multi-Stage Transfer Learning with an Application to Selection Process

.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

Improving Supervised Superpixel-Based Codebook Representations by Local Convolutional Features

Similar lecture

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models

Downloads