Search Results for author:
Found 21 papers, 8 papers with code
Deep Entity Matching with Pre-Trained Language Models
Our experiments show that a straightforward application of language models such as BERT, DistilBERT, or RoBERTa pre-trained on large text corpora already significantly improves the matching quality and outperforms previous state-of-the-art (SOTA), by up to 29% of F1 score on benchmark datasets.
Ranked #2 on
Entity Resolution
on WDC Watches-xlarge
Snippext: Semi-supervised Opinion Mining with Augmented Data
A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data.
Annotating Columns with Pre-trained Language Models
Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information.
Ranked #1 on
Column Type Annotation
on VizNet-Sato-MultiColumn
TimelineQA: A Benchmark for Question Answering over Timelines
We describe a set of experiments on TimelineQA with several state-of-the-art QA models.
Teddy: A System for Interactive Review Analysis
They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services.
SpinArt: A Spin-based Verifier for Artifact Systems
Data-driven workflows, of which IBM's Business Artifacts are a prime exponent, have been successfully deployed in practice, adopted in industrial standards, and have spawned a rich body of research in academia, focused primarily on static analysis.
Databases Formal Languages and Automata Theory
VERIFAS: A Practical Verifier for Artifact Systems
Data-driven workflows, of which IBM's Business Artifacts are a prime exponent, have been successfully deployed in practice, adopted in industrial standards, and have spawned a rich body of research in academia, focused primarily on static analysis.
Databases Logic in Computer Science
Verification of Hierarchical Artifact Systems
Data-driven workflows, of which IBM's Business Artifacts are a prime exponent, have been successfully deployed in practice, adopted in industrial standards, and have spawned a rich body of research in academia, focused primarily on static analysis.
Databases
Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network
Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields.
Subjective Databases
In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes.
Databases
Voyageur: An Experiential Travel Search Engine
We describe Voyageur, which is an application of experiential search to the domain of travel.
Fast LLMMSE filter for low-dose CT imaging
Low-dose X-ray CT technology is one of important directions of current research and development of medical imaging equipment.
Towards Productionizing Subjective Search Systems
Also, we found that the existing search algorithms do not meet the search quality standard required by production systems.
Enhancing Review Comprehension with Domain-Specific Commonsense
In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs).
Constructing Explainable Opinion Graphs from Review
The Web is a major resource of both factual and subjective information.
Deep or Simple Models for Semantic Tagging? It Depends on your Data [Experiments]
We embark on a systematic study to investigate the following question: Are deep models the best performing model for all semantic tagging tasks?
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting
Ghost imaging radar is a new system of gaze imaging radar with high detection sensitivity, super-resolution and better anti-interference performance, but the relative motion between the radar system and the target will make the target imaging deteriorate.
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition
A non-convex weighted low-rank decomposition (NC-WLRD) model for LRMA is also proposed to reconstruct all degraded patch matrices grouped by the proposed RwM algorithm.
Reimagining Retrieval Augmented Language Models for Answering Queries
We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison.
Human-Centered Planning
The vision of creating AI-powered personal assistants also involves creating structured outputs, such as a plan for one's day, or for an overseas trip.
LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing
Based on these findings, we propose design implications to inform the future development of agent-assisted content editing.
