1 code implementation • EMNLP (sdp) 2020 • Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, Christopher Griffin
We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged.
no code implementations • EMNLP (sdp) 2020 • Kunho Kim, Athar Sefid, C. Lee Giles
Author name disambiguation (AND) algorithms identify a unique author entity record from all similar or same publication records in scholarly or similar databases.
no code implementations • 31 Jan 2025 • Ting-Yao E. Hsu, Yi-Li Hsu, Shaurya Rohatgi, Chieh-Yang Huang, Ho Yin Sam Ng, Ryan Rossi, Sungchul Kim, Tong Yu, Lun-Wei Ku, C. Lee Giles, Ting-Hao K. Huang
This paper presents an overview of the first SCICAP Challenge and details the performance of various models on its data, capturing a snapshot of the fields state.
no code implementations • 19 Nov 2024 • Levent Toksoz, Mukund Srinath, Gang Tan, C. Lee Giles
A novel pseudocode search engine is designed to facilitate efficient retrieval and search of academic papers containing pseudocode.
no code implementations • 17 Aug 2024 • Siyu Wu, Alessandro Oltramari, Jonathan Francis, C. Lee Giles, Frank E. Ritter
Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems.
no code implementations • 7 Jun 2024 • Levent Toksoz, Gang Tan, C. Lee Giles
Pseudocode in a scholarly paper provides a concise way to express the algorithms implemented therein.
no code implementations • 21 May 2024 • Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali
However, most research has predominantly focused on language-based reasoning and word problems, often overlooking the potential of LLMs in handling symbol-based calculations and reasoning.
no code implementations • 26 Mar 2024 • Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, C. Lee Giles, Ting-Hao K. Huang
Crafting effective captions for figures is important.
no code implementations • 16 Feb 2024 • Mukund Srinath, Pranav Venkit, Maria Badillo, Florian Schaub, C. Lee Giles, Shomir Wilson
Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them.
no code implementations • 4 Feb 2024 • Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali
We sampled the datasets from $7$ Tomita and $4$ Dyck grammars and trained them on $4$ RNN cells: LSTM, GRU, O2RNN, and MIRNN.
no code implementations • 23 Oct 2023 • Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi, Sungchul Kim, C. Lee Giles, Ting-Hao K. Huang
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
no code implementations • 1 Mar 2023 • Tatiana Chakravorti, Robert Fraleigh, Timothy Fritton, Michael McLaughlin, Vaibhav Singh, Christopher Griffin, Anthony Kwasnica, David Pennock, C. Lee Giles, Sarah Rajtmajer
We present a prototype hybrid prediction market and demonstrate the avenue it represents for meaningful human-AI collaboration.
no code implementations • 28 Jan 2023 • Zeba Karishma, Shaurya Rohatgi, Kavya Shrinivas Puranik, Jian Wu, C. Lee Giles
However, there are no large-scale retrieval services for scientific figures and tables.
no code implementations • 23 Dec 2021 • Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmaan Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael McLaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, C. Lee Giles
Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress.
1 code implementation • Findings (EMNLP) 2021 • Ting-Yao Hsu, C. Lee Giles, Ting-Hao 'Kenneth' Huang
Researchers use figures to communicate rich, complex information in scientific papers.
Ranked #1 on
Image Captioning
on SCICAP
no code implementations • 20 May 2021 • Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, C. Lee Giles
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
no code implementations • 8 Apr 2021 • Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka, Arjun Manoj Menon, Sai Ajay Modukuri, Nishanth Nakshatri, Xin Wei, Zhuoer Wang, James Caverlee, Sarah M. Rajtmajer, C. Lee Giles
In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.
no code implementations • 7 Apr 2021 • Ankur Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles
Two particular tasks that test this type of reasoning are (1) mathematical equation verification, which requires determining whether trigonometric and linear algebraic statements are valid identities or not, and (2) equation completion, which entails filling in a blank within an expression to make it true.
no code implementations • 5 Jan 2021 • Nishanth Nakshatri, Arjun Menon, C. Lee Giles, Sarah Rajtmajer, Christopher Griffin
We show that under certain assumptions on the underlying geometry, the resulting synthetic prediction market can be used to arbitrarily closely approximate a binary function defined on a set of input data.
no code implementations • 9 Dec 2020 • Xiaoxiao Li, Rabah Al-Zaidy, Amy Zhang, Stefan Baral, Le Bao, C. Lee Giles
Conclusions: In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews, as well as facilitating live reviews, where reviews are updated regularly.
no code implementations • 7 Dec 2020 • Yasith Jayawardana, Alexander C. Nwala, Gavindya Jayawardena, Jian Wu, Sampath Jayarathna, Michael L. Nelson, C. Lee Giles
The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources.
no code implementations • 27 Jul 2020 • Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles
The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism.
no code implementations • 5 Jun 2020 • John Stogin, Ankur Mali, C. Lee Giles
We introduce a neural stack architecture, including a differentiable parametrized stack operator that approximates stack push and pop operations for suitable choices of parameters that explicitly represents a stack.
1 code implementation • ACL 2020 • Ting-Hao 'Kenneth' Huang, Chieh-Yang Huang, Chien-Kuang Cornelia Ding, Yen-Chia Hsu, C. Lee Giles
This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10, 966 English abstracts in the COVID-19 Open Research Dataset.
no code implementations • ACL 2021 • Mukund Srinath, Shomir Wilson, C. Lee Giles
Organisations disclose their privacy practices by posting privacy policies on their website.
no code implementations • 10 Feb 2020 • Alexander Ororbia, Ankur Mali, Daniel Kifer, C. Lee Giles
Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals.
no code implementations • 9 Dec 2019 • Shaurya Rohatgi, Wei Zhong, Richard Zanibbi, Jian Wu, C. Lee Giles
Query Auto Completion (QAC) is among the most appealing features of a web search engine.
1 code implementation • 27 Nov 2019 • Ke Yuan, Dafang He, Zhuoren Jiang, Liangcai Gao, Zhi Tang, C. Lee Giles
Compared to conventional summarization tasks, this task has two extra and essential constraints: 1) Detailed math questions consist of text and math equations which require a unified framework to jointly model textual and mathematical information; 2) Unlike text, math equations contain semantic and structural features, and both of them should be captured together.
1 code implementation • 12 Nov 2019 • Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles
We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity.
no code implementations • 15 Oct 2019 • Kaixuan Zhang, Qinglong Wang, Xue Liu, C. Lee Giles
This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models.
no code implementations • 7 Sep 2019 • Ankur Mali, Alexander Ororbia, C. Lee Giles
The NSPDA is also compared to a classical analog stack neural network pushdown automaton (NNPDA) as well as a wide array of first and second-order RNNs with and without external memory, trained using different learning algorithms.
1 code implementation • 20 Jun 2019 • Athar Sefid, Jian Wu, Allen C. Ge, Jing Zhao, Lu Liu, Cornelia Caragea, Prasenjit Mitra, C. Lee Giles
We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset.
no code implementations • 25 May 2019 • Alexander Ororbia, Ankur Mali, Daniel Kifer, C. Lee Giles
In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered.
no code implementations • 14 Nov 2018 • Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles
The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured.
2 code implementations • 17 Oct 2018 • Alexander Ororbia, Ankur Mali, C. Lee Giles, Daniel Kifer
We compare our model and learning procedure to other back-propagation through time alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization.
no code implementations • 9 Sep 2018 • Dafang He, Xiao Yang, Daniel Kifer, C. Lee Giles
We propose a novel and effective framework for this and experimentally demonstrate that: (1) A CNN that can be effectively used to extract instance-level text contour from natural images.
1 code implementation • CVPR 2019 • Anand Gopalakrishnan, Ankur Mali, Dan Kifer, C. Lee Giles, Alexander G. Ororbia
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation.
1 code implementation • WS 2018 • Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, C. Lee Giles
We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.
no code implementations • 23 Apr 2018 • Dafang He, Yeqing Li, Alexander Gorban, Derrall Heath, Julian Ibarz, Qian Yu, Daniel Kifer, C. Lee Giles
In this work, we propose a new framework that learns this task in an end-to-end way.
no code implementations • 22 Apr 2018 • Xiao Yang, Miaosen Wang, Wei Wang, Madian Khabsa, Ahmed Awadallah, Daniel Kifer, C. Lee Giles
We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue.
no code implementations • 15 Mar 2018 • Alexander G. Ororbia, Ankur Mali, Jian Wu, Scott O'Connell, David Miller, C. Lee Giles
For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques.
no code implementations • 5 Mar 2018 • Alexander G. Ororbia, Ankur Mali, Daniel Kifer, C. Lee Giles
Using back-propagation and its variants to train deep networks is often problematic for new users.
no code implementations • 19 Jan 2018 • Chen Liang, Jianbo Ye, Han Zhao, Bart Pursel, C. Lee Giles
Strict partial order is a mathematical structure commonly seen in relational data.
no code implementations • 16 Jan 2018 • Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles
Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars.
no code implementations • 4 Jan 2018 • Agnese Chiatti, Mu Jung Cho, Anupriya Gagneja, Xiao Yang, Miriam Brinberg, Katie Roehrick, Sagnik Ray Choudhury, Nilam Ram, Byron Reeves, C. Lee Giles
Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data.
no code implementations • 30 Nov 2017 • Alexander G. Ororbia II, Patrick Haffner, David Reitter, C. Lee Giles
We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences.
no code implementations • 29 Sep 2017 • Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles
Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis.
no code implementations • CVPR 2017 • Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
no code implementations • CVPR 2017 • Dafang He, Xiao Yang, Chen Liang, Zihan Zhou, Alexander G. Ororbi II, Daniel Kifer, C. Lee Giles
Scene text detection has attracted great attention these years.
no code implementations • CVPR 2017 • Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
1 code implementation • 17 May 2017 • Shikun Liu, C. Lee Giles, Alexander G. Ororbia II
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #6 on
3D Object Recognition
on ModelNet40
no code implementations • 5 Dec 2016 • Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles
Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design.
no code implementations • 22 Nov 2016 • Xiao Yang, Dafang He, Wenyi Huang, Zihan Zhou, Alex Ororbia, Dan Kifer, C. Lee Giles
Physical library collections are valuable and long standing resources for knowledge and learning.
no code implementations • 6 Oct 2016 • Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.
no code implementations • 5 Oct 2016 • Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, Xue Liu
However, after a thorough analysis of the fundamental flaw in DNNs, we discover that the effectiveness of current defenses is limited and, more importantly, cannot provide theoretical guarantees as to their robustness against adversarial sampled-based attacks.
no code implementations • 26 Jan 2016 • Alexander G. Ororbia II, C. Lee Giles, Daniel Kifer
Many previous proposals for adversarial training of deep neural nets have included di- rectly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing constrained adversarial ob- jective functions.
no code implementations • 22 Nov 2015 • Alexander G. Ororbia II, C. Lee Giles, David Reitter
Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems.
no code implementations • AAAI 2015 • Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles
It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context.