Search Results for author:
Found 13 papers, 3 papers with code
DocTime: A Document-level Temporal Dependency Graph Parser
We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph.
IGA: An Intent-Guided Authoring Assistant
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns
We present a comprehensive study of sparse attention patterns in Transformer models.
On Mechanistic Knowledge Localization in Text-to-Image Generative Models
To address this issue, we introduce the concept of Mechanistic Localization in text-to-image models, where knowledge about various visual attributes (e. g., ``style", ``objects", ``facts") can be mechanistically localized to a small fraction of layers in the UNet, thus facilitating efficient model editing.
Localizing and Editing Knowledge in Text-to-Image Generative Models
We adapt Causal Mediation Analysis for text-to-image models and trace knowledge about distinct visual attributes to various (causal) components in the (i) UNet and (ii) text-encoder of the diffusion model.
End-to-end Document Recognition and Understanding with Dessurt
Dessurt is a more flexible model than prior methods and is able to handle a variety of document domains and tasks.
Ranked #31 on
Visual Question Answering (VQA)
on DocVQA test
UniDoc: Unified Pretraining Framework for Document Understanding
Document intelligence automates the extraction of information from documents and supports many business applications.
Syntopical Graphs for Computational Argumentation Tasks
Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.
TIMERS: Document-level Temporal Relation Extraction
We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.
Ranked #3 on
Temporal Relation Classification
on TB-Dense
IGA : An Intent-Guided Authoring Assistant
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.
Self-Supervised Relationship Probing
Structured representations of images that model visual relationships are beneficial for many vision and vision-language applications.
A Joint Model for Document Segmentation and Segment Labeling
Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections.
Selective Encoding for Recognizing Unreliably Localized Faces
Most existing face verification systems rely on precise face detection and registration.
