6 citations · 15 across the 8 of their papers we have counts for
8 papers
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
Editing Common Sense in Transformers
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri +4
Editing model parameters directly in Transformers makes updating open-source transformer-based models possible without re-training (Meng et al., 2023). However, these editing metho…
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
Ruofan Wu, Boqun Ma, Hong Jin +3
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling…
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution
Ankita Gupta, Marzena Karpinska, Wenlong Zhao +5
Large-scale, high-quality corpora are critical for advancing research in coreference resolution. However, existing datasets vary in their definition of coreferences and have been c…
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
Yuezhou Sun, Wenlong Zhao, Lijun Zhang +3
This paper investigates deep neural network (DNN) compression from the perspective of compactly representing and storing trained parameters. We explore the previously overlooked op…
IGA : An Intent-Guided Authoring Assistant
Simeng Sun, Wenlong Zhao, Varun Manjunatha +5
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants h…
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings
Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao +3
The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to t…
Rethinking Exposure Bias In Language Modeling
Yifan Xu, Kening Zhang, Haoyu Dong +3
Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned…