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Found 7 papers, 5 papers with code
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees
In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain.
Natural Counterfactuals With Necessary Backtracking
Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions.
Taxonomy-Structured Domain Adaptation
Domain adaptation aims to mitigate distribution shifts among different domains.
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision.
MIXGAN: Learning Concepts from Different Domains for Mixture Generation
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e. g., content and style) from different domains and thus generating a new domain with learned concepts.
