104 citations · 486 across the 19 of their papers we have counts for
39 papers
Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification
Ruohong Zhang, Yau-Shian Wang, Yiming Yang +2
Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories. Recently, large pre-t…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
On the Sentence Embeddings from Pre-trained Language Models
Bohan Li, Hao Zhou, Junxian He +3
Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models w…
Rethinking Transformer-based Set Prediction for Object Detection
Zhiqing Sun, Shengcao Cao, Yiming Yang +1
DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long train…
EIGEN: Event Influence GENeration using Pre-trained Language Models
Aman Madaan, Dheeraj Rajagopal, Yiming Yang +3
Reasoning about events and tracking their influences is fundamental to understanding processes. In this paper, we present EIGEN - a method to leverage pre-trained language models t…
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
Donghan Yu, Chenguang Zhu, Yiming Yang +1
Knowledge graphs (KGs) contain rich information about world knowledge, entities and relations. Thus, they can be great supplements to existing pre-trained language models. However,…