update

content/publication/2019neurips-aligning/cite.bib

@@ -1,14 +1,14 @@
 @incollection{liu2019aligning,
- author = {Fenglin Liu and
-Yuanxin Liu and
-Xuancheng Ren and
-Xiaodong He and
-Xu Sun},
- title = {Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations},
- booktitle = {Advances in Neural Information Processing Systems 32},
- publisher = {Curran Associates, Inc.},
- pages = {6847--6857},
- year = {2019},
- url = {http://papers.nips.cc/paper/8909-aligning-visual-regions-and-textual-concepts-for-semantic-grounded-image-representations}
+  author    = {Fenglin Liu and
+               Yuanxin Liu and
+               Xuancheng Ren and
+               Xiaodong He and
+               Xu Sun},
+  title     = {Aligning Visual Regions and Textual Concepts for Semantic-Grounded
+               Image Representations},
+  booktitle = {Advances in Neural Information Processing Systems 32},
+  publisher = {Curran Associates, Inc.},
+  pages     = {6847--6857},
+  year      = {2019},
+  url       = {http://papers.nips.cc/paper/8909-aligning-visual-regions-and-textual-concepts-for-semantic-grounded-image-representations}
 }
-

content/publication/2020neurips-prophet/cite.bib

@@ -0,0 +1,16 @@
+@incollection{liu2020prophet,
+  author    = {Liu, Fenglin and
+               Ren, Xuancheng and
+               Wu, Xian and
+               Ge, Shen and
+               Fan, Wei and
+               Zou, Yuexian and
+               Sun, Xu},
+  booktitle = {Advances in Neural Information Processing Systems},
+  pages     = {1865--1876},
+  publisher = {Curran Associates, Inc.},
+  title     = {Prophet Attention: Predicting Attention with Future Attention},
+  url       = {https://proceedings.neurips.cc/paper/2020/file/13fe9d84310e77f13a6d184dbf1232f3-Paper.pdf},
+  volume    = {33},
+  year      = {2020}
+}

content/publication/2020neurips-prophet/index.md

@@ -8,5 +8,8 @@ abstract: "Recently, attention based models have been used extensively in image
 featured: false
 publication: "*Advances in Neural Information Processing Systems 33, **NeurIPS 2020** (to appear)*"
 publication_short: "**NeurIPS 2020** (to appear)"
+
+url_pdf: "https://proceedings.neurips.cc/paper/2020/hash/13fe9d84310e77f13a6d184dbf1232f3-Abstract.html"
+
 ---
 

content/publication/2021aaai-collaborative/index.md

@@ -37,7 +37,7 @@ featured: false
 #   icon: twitter
 
 url_pdf:
-url_arxiv:
+url_arxiv: "https://arxiv.org/abs/2009.07712"
 url_code:
 url_dataset:
 url_poster:

content/publication/2021aaai-exploring/index.md

@@ -37,9 +37,9 @@ featured: false
 #   icon_pack: fab
 #   icon: twitter
 
-url_pdf:
-url_arxiv:
-url_code:
+url_pdf: "https://www.aaai.org/AAAI21Papers/AAAI-2538.SunX.pdf"
+url_arxiv: "https://arxiv.org/abs/2006.05620"
+url_code: 
 url_dataset:
 url_poster:
 url_project:

content/publication/2021aaai-multiview/index.md

@@ -37,7 +37,7 @@ featured: false
 #   icon: twitter
 
 url_pdf:
-url_arxiv:
+url_arxiv: "https://arxiv.org/abs/2102.10780"
 url_code:
 url_dataset:
 url_poster:

content/publication/2021aaai-semantics/index.md

@@ -36,9 +36,9 @@ featured: false
 #   icon_pack: fab
 #   icon: twitter
 
-url_pdf:
+url_pdf: "https://www.aaai.org/AAAI21Papers/AAAI-9726.RenX.pdf"
 url_arxiv:
-url_code:
+url_code: "https://github.com/lancopku/sempre"
 url_dataset:
 url_poster:
 url_project:

content/publication/2021naacl-careful/index.md

@@ -0,0 +1,70 @@
+---
+# Documentation: https://sourcethemes.com/academic/docs/managing-content/
+
+title: "Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models"
+authors: ["Wenkai Yang", "Lei Li", "Zhiyuan Zhang", "admin", "Xu Sun", "Qun Liu"]
+date: 2021-06-06
+doi: ""
+
+# Schedule page publish date (NOT publication's date).
+publishDate: 2020-12-01T12:53:44+08:00
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: "*Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, **NAACL 2021** (to appear)*"
+publication_short: "**NAACL 2021** (to appear)"
+
+abstract: "Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods usually assume that attackers have a certain degree of data knowledge, either the dataset which users would use or proxy datasets for a similar task, for implementing the data poisoning procedure. However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples. Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier. We hope this work can raise the awareness of such a critical security risk hidden in the embedding layers of NLP models."
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+categories: []
+featured: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+url_pdf:
+url_arxiv: "https://arxiv.org/abs/2103.15543"
+url_code: "https://github.com/lancopku/Embedding-Poisoning"
+url_dataset:
+url_poster:
+url_project:
+url_slides:
+url_source:
+url_video:
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides: ""
+---

content/publication/2021naacl-global/index.md

@@ -0,0 +1,71 @@
+---
+# Documentation: https://sourcethemes.com/academic/docs/managing-content/
+
+title: "A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models"
+authors: ["Kaiyuan Liao", "Yi Zhang", "admin", "Qi Su", "Xu Sun", "Bin He"]
+author_notes: ["Equal contribution", "Equal contribution"]
+date: 2021-06-06
+doi: ""
+
+# Schedule page publish date (NOT publication's date).
+publishDate: 2020-12-01T12:57:44+08:00
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: "*Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, **NAACL 2021** (to appear)*"
+publication_short: "**NAACL 2021** (to appear)"
+
+abstract: "Early exit mechanism aims to accelerate inference speed for large-scale pre-trained language models. The essential idea is exiting early without passing through all the inference layers at the inference stage. To make accurate predictions for downstream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused passed layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the hierarchical linguistic information embedded in the past layers and then take a further step to engage the future states which are originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding more effective and more robust results."
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+categories: []
+featured: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+url_pdf: 
+url_arxiv:
+url_code: "https://github.com/lancopku/Early-Exit"
+url_dataset:
+url_poster:
+url_project:
+url_slides:
+url_source:
+url_video:
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides: ""
+---

content/publication/2021naacl-neural/index.md

@@ -0,0 +1,70 @@
+---
+# Documentation: https://sourcethemes.com/academic/docs/managing-content/
+
+title: "Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects"
+authors: ["Zhiyuan Zhang", "admin", "Qi Su", "Xu Sun", "Bin He"]
+date: 2021-06-06
+doi: ""
+
+# Schedule page publish date (NOT publication's date).
+publishDate: 2020-12-01T12:57:45+08:00
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: "*Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, **NAACL 2021** (to appear)*"
+publication_short: "**NAACL 2021** (to appear)"
+
+abstract: "Side effects during neural network tuning are typically measured by overall accuracy changes. However, we find that even with similar overall accuracy, existing tuning methods result in non-negligible instance-wise side effects. Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus propose to conduct neural network surgery by only modifying a limited number of parameters. Neural network surgery can be realized using diverse techniques, and we investigate three lines of methods. Experimental results on representative tuning problems validate the effectiveness of the surgery approach. The dynamic selecting method achieves the best overall performance that not only satisfies the tuning goal but also induces fewer instance-wise side effects by changing only 10^-5 of the parameters."
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+categories: []
+featured: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+url_pdf:
+url_arxiv:
+url_code:
+url_dataset:
+url_poster:
+url_project:
+url_slides:
+url_source:
+url_video:
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides: ""
+---