Search Results for author:
Found 9 papers, 6 papers with code
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images
In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data.
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
Correcting Diffusion Generation through Resampling
Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth images, which has resulted in several notable problems in image generation, including missing object errors in text-to-image generation and low image quality.
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the data, and model (epistemic) uncertainty, resulting from the lack of knowledge in the model.
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets.
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis
We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object.
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
Based on this finding, we further propose a simple, light-weight image editing algorithm where the mixing weights of the two text embeddings are optimized for style matching and content preservation.
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection
Ideology is at the core of political science research.
Audrey: A Personalized Open-Domain Conversational Bot
Conversational Intelligence requires that a person engage on informational, personal and relational levels.
