Spain

ECAI 2020

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

scene text recognition; fully-convolutional intensive feature flow neural network; intensive block

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

Multi-Stage Transfer Learning with an Application to Selection Process

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

https://d3smihljt9218e.cloudfront.net/transcript/1187/subtitle/cadb0e57ee0841c850ce4ad431f9ddaa.vtt

English (automatic)

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Multi-Stage Transfer Learning with an Application to Selection Process

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On the Interpretation of Convolutional Neural Networks for Text Classification

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Next from ECAI 2020

Multi-Stage Transfer Learning with an Application to Selection Process

Similar lecture

On the Interpretation of Convolutional Neural Networks for Text Classification

Downloads