Search Results for author:
Found 6 papers, 5 papers with code
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions.
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning.
Ranked #2 on
Video Captioning
on YouCook2
Relation-Aware Language-Graph Transformer for Question Answering
To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner.
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
To this end, we propose TokenMixup, an efficient attention-guided token-level data augmentation method that aims to maximize the saliency of a mixed set of tokens.
Ranked #65 on
Image Classification
on CIFAR-10
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths.
Ranked #1 on
Human-Object Interaction Detection
on V-COCO
(MAP metric)
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
