activity
20162024
most citedProfessor Forcing: A New Algorithm for Training Recurrent Networks

328 citations · 329 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20241 cited

SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning

Haiwen Diao, Bo Wan, Xu Jia +4

Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable param…

cs.MM2024

Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion

Yu Zhao, Ying Zhang, Baohang Zhou +3

A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the…

cs.CL2024

Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection

Yuxi Li, Yi Liu, Gelei Deng +7

With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent ou…

cs.CL2023

From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment

Yu Zhao, Yike Wu, Xiangrui Cai +3

Entity Alignment (EA) aims to find the equivalent entities between two Knowledge Graphs (KGs). Existing methods usually encode the triples of entities as embeddings and learn to al…

cs.CL2021

Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition

Ying Zhang, Fandong Meng, Yufeng Chen +2

Zero-resource named entity recognition (NER) severely suffers from data scarcity in a specific domain or language. Most studies on zero-resource NER transfer knowledge from various…

stat.ML2016328 cited

Professor Forcing: A New Algorithm for Training Recurrent Networks

Alex Lamb, Anirudh Goyal, Ying Zhang +3

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do…