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Found 9 papers, 1 papers with code
Insurance Question Answering via Single-turn Dialogue Modeling
With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention.
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data.
Key-point Guided Deformable Image Manipulation Using Diffusion Model
In this paper, we introduce a Key-point-guided Diffusion probabilistic Model (KDM) that gains precise control over images by manipulating the object's key-point.
Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning
To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session.
class-incremental learning
Few-Shot Class-Incremental Learning
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Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions.
MMGAN: Manifold Matching Generative Adversarial Network
MMGAN finds two manifolds representing the vector representations of real and fake images.
Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering
Evolutionary clustering aims at capturing the temporal evolution of clusters.
Investigating the Image of Entities in Social Media: Dataset Design and First Results
The objective of this paper is to describe the design of a dataset that deals with the image (i. e., representation, web reputation) of various entities populating the Internet: politicians, celebrities, companies, brands etc.
Annotated Bibliographical Reference Corpora in Digital Humanities
In this paper, we present new bibliographical reference corpora in digital humanities (DH) that have been developed under a research project, Robust and Language Independent Machine Learning Approaches for Automatic Annotation of Bibliographical References in DH Books supported by Google Digital Humanities Research Awards.
