Search Results for author:
Found 8 papers, 8 papers with code
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Text-to-SQL generation enables non-experts to interact with databases via natural language.
Ranked #5 on
Text-To-SQL
on spider
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks.
DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker
Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects.
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
However, we show that standard differentially private model training is insufficient for providing strong certified robustness guarantees.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
Ranked #18 on
Link Property Prediction
on ogbl-wikikg2
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Factual Error Correction for Abstractive Summarization Models
Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
