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Found 11 papers, 4 papers with code
Self-Supervised Adversarial Imitation Learning
We address this limitation by incorporating a discriminator into the original framework, offering two key advantages and directly solving a learning problem previous work had.
Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey
In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research.
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms.
Imitating Unknown Policies via Exploration
Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations.
HAPRec: Hybrid Activity and Plan Recognizer
Computer-based assistants have recently attracted much interest due to its applicability to ambient assisted living.
Component Analysis for Visual Question Answering Architectures
Our major contribution is to identify core components for training VQA models so as to maximize their predictive performance.
Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification
While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged.
Classifying Norm Conflicts using Learned Semantic Representations
While most social norms are informal, they are often formalized by companies in contracts to regulate trades of goods and services.
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks.
Bidirectional Retrieval Made Simple
This paper provides a very simple yet effective character-level architecture for learning bidirectional retrieval models.
Order embeddings and character-level convolutions for multimodal alignment
With the novel and fast advances in the area of deep neural networks, several challenging image-based tasks have been recently approached by researchers in pattern recognition and computer vision.
