no code implementations • ACL 2022 • Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao
In this work, we study a more challenging but practical problem, i. e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.
class-incremental learning Few-Shot Class-Incremental Learning +4
no code implementations • EMNLP 2021 • Rui Wang, Ricardo Henao
Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data.
1 code implementation • 27 May 2024 • Mohd Ashhad, Ricardo Henao
Synthetic data generation holds considerable promise, offering avenues to enhance privacy, fairness, and data accessibility.
no code implementations • 27 May 2024 • Aaron T. Wang, Ricardo Henao, Lawrence Carin
Contextual outcomes in the $m$th set of contextual data, $\textsf{C}_m$, are modeled in terms of latent function $f_m(x)\in\textsf{F}$, where $\textsf{F}$ is a functional class with $(C-1)$-dimensional vector output.
1 code implementation • 11 Apr 2024 • Yuetan Chu, Gongning Luo, Longxi Zhou, Shaodong Cao, Guolin Ma, Xianglin Meng, Juexiao Zhou, Changchun Yang, Dexuan Xie, Ricardo Henao, Xigang Xiao, Lianming Wu, Zhaowen Qiu, Xin Gao
Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions.
no code implementations • 24 Oct 2023 • Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew M. Engelhard
Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals.
1 code implementation • 1 Jun 2023 • Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects.
no code implementations • 9 Mar 2023 • Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin
Open world classification is a task in natural language processing with key practical relevance and impact.
no code implementations • 25 Feb 2023 • Rui Wang, Pengyu Cheng, Ricardo Henao
To improve the fairness of PLMs in text generation, we propose to minimize the mutual information between the semantics in the generated text sentences and their demographic polarity, i. e., the demographic group to which the sentence is referring.
no code implementations • 2 Feb 2023 • Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao, Shreya Chakrabarti
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement.
no code implementations • 23 Oct 2022 • Vinay Kumar Verma, Nikhil Mehta, Shijing Si, Ricardo Henao, Lawrence Carin
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks.
no code implementations • 11 Oct 2022 • Sijia Wang, Yoojin Choi, Junya Chen, Mostafa El-Khamy, Ricardo Henao
This results in the eventual prohibitive expansion of the knowledge repository if we consider learning from a long sequence of tasks.
1 code implementation • 25 Feb 2022 • Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao
Understanding the effects of these system inputs on system outputs is crucial to have any meaningful model of a dynamical system.
1 code implementation • 3 Feb 2022 • Yamac Alican Isik, Connor Davis, Paidamoyo Chapfuwa, Ricardo Henao
Recently proposed encoder-decoder structures for modeling Hawkes processes use transformer-inspired architectures, which encode the history of events via embeddings and self-attention mechanisms.
1 code implementation • ICLR 2022 • Qitong Gao, Dong Wang, Joshua D. Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajic
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification.
1 code implementation • CVPR 2022 • Fanjie Kong, Ricardo Henao
Specifically, these classification tasks face two key challenges: $i$) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images; and $ii$) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (ROI) to image ratio.
no code implementations • NAACL 2021 • Vivek Subramanian, Matthew Engelhard, Sam Berchuck, Liqun Chen, Ricardo Henao, Lawrence Carin
In many natural language processing applications, identifying predictive text can be as important as the predictions themselves.
no code implementations • 2 Apr 2021 • Meng Xia, Meenal K. Kheterpal, Samantha C. Wong, Christine Park, William Ratliff, Lawrence Carin, Ricardo Henao
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture.
1 code implementation • 8 Jan 2021 • Fanjie Kong, Xiao-Yang Liu, Ricardo Henao
In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art.
no code implementations • CVPR 2021 • Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former.
Ranked #11 on Knowledge Distillation on CIFAR-100
no code implementations • 6 Dec 2020 • Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, Lawrence Carin
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts.
1 code implementation • NeurIPS 2021 • Zidi Xiu, Junya Chen, Ricardo Henao, Benjamin Goldstein, Lawrence Carin, Chenyang Tao
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Rui Wang, Shijing Si, Guoyin Wang, Lei Zhang, Lawrence Carin, Ricardo Henao
Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years.
no code implementations • 23 Oct 2020 • Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
1 code implementation • 17 Sep 2020 • Zidi Xiu, Chenyang Tao, Michael Gao, Connor Davis, Benjamin A. Goldstein, Ricardo Henao
Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems.
no code implementations • 20 Aug 2020 • Xichen She, Yaya Zhai, Ricardo Henao, Christopher W. Woods, Christopher Chiu, Geoffrey S. Ginsburg, Peter X. K. Song, Alfred O. Hero
$\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring.
no code implementations • 14 Aug 2020 • Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
1 code implementation • 22 Jun 2020 • Shijing Si, Rui Wang, Jedrek Wosik, Hao Zhang, David Dov, Guoyin Wang, Ricardo Henao, Lawrence Carin
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.
1 code implementation • 14 Jun 2020 • Paidamoyo Chapfuwa, Serge Assaad, Shuxi Zeng, Michael J. Pencina, Lawrence Carin, Ricardo Henao
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data.
no code implementations • 3 Apr 2020 • Matthew Engelhard, Samuel Berchuck, Joshua D'Arcy, Ricardo Henao
Consequently, no distinction is made between a) the probability of event occurrence, and b) the predicted time of occurrence.
1 code implementation • 9 Mar 2020 • Zidi Xiu, Chenyang Tao, Benjamin A. Goldstein, Ricardo Henao
The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making.
1 code implementation • 29 Feb 2020 • Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin, Ricardo Henao
As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions.
1 code implementation • 12 Feb 2020 • Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin
This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.
2 code implementations • ICML 2020 • Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin
A new algorithmic framework is proposed for learning autoencoders of data distributions.
1 code implementation • NeurIPS 2019 • Kevin J Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.
no code implementations • 5 Oct 2019 • Pengyu Cheng, Chang Liu, Chunyuan Li, Dinghan Shen, Ricardo Henao, Lawrence Carin
The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables.
1 code implementation • NeurIPS 2019 • Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
no code implementations • 30 May 2019 • Rui Wang, Guoyin Wang, Ricardo Henao
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset.
1 code implementation • 21 May 2019 • Paidamoyo Chapfuwa, Chenyang Tao, Lawrence Carin, Ricardo Henao
We present a survival function estimator for probabilistic predictions in time-to-event models, based on a neural network model for draws from the distribution of event times, without explicit assumptions on the form of the distribution.
no code implementations • 26 Apr 2019 • David Dov, Shahar Ziv Kovalsky, Serge Assaad, Avani A. Pendse Jonathan Cohen, Danielle Elliott Range, Ricardo Henao, Lawrence Carin
The lower bound further allows us to extend the proposed algorithm to simultaneously predict multiple bag and instance-level labels from a single output of a neural network.
no code implementations • 29 Mar 2019 • David Dov, Shahar Kovalsky, Jonathan Cohen, Danielle Range, Ricardo Henao, Lawrence Carin
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images.
no code implementations • EMNLP 2018 • Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years.
1 code implementation • 24 Jul 2018 • Elizabeth Lorenzi, Ricardo Henao, Katherine Heller
We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data.
Applications
1 code implementation • ICML 2018 • Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke
To assess the difference between real and synthetic data, Generative Adversarial Networks (GANs) are trained using a distribution discrepancy measure.
no code implementations • ICML 2018 • Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke
Recent advances on the scalability and flexibility of variational inference have made it successful at unravelling hidden patterns in complex data.
2 code implementations • ICML 2018 • Yunchen Pu, Shuyang Dai, Zhe Gan, Wei-Yao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
2 code implementations • ACL 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on Named Entity Recognition (NER) on CoNLL 2000
1 code implementation • ACL 2018 • Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Lawrence Carin, Ricardo Henao
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
2 code implementations • ACL 2018 • Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on Text Classification on DBpedia
4 code implementations • ICML 2018 • Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin, Ricardo Henao
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.
no code implementations • 15 Jan 2018 • Xinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li, Lawrence Carin
Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector.
no code implementations • ICLR 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
no code implementations • NeurIPS 2017 • Yunchen Pu, Wei-Yao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin
A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data.
no code implementations • 21 Sep 2017 • Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.
5 code implementations • NeurIPS 2017 • Chunyuan Li, Hao liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching.
4 code implementations • NeurIPS 2017 • Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin
Learning latent representations from long text sequences is an important first step in many natural language processing applications.
1 code implementation • ICML 2017 • Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin
We propose a framework for generating realistic text via adversarial training.
no code implementations • ICML 2017 • Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin
A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo.
no code implementations • NeurIPS 2017 • Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent.
no code implementations • 2 Dec 2016 • Elizabeth C Lorenzi, Zhifei Sun, Erich Huang, Ricardo Henao, Katherine A. Heller
We aim to create a framework for transfer learning using latent factor models to learn the dependence structure between a larger source dataset and a target dataset.
no code implementations • EMNLP 2017 • Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He, Lawrence Carin
We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes.
no code implementations • NeurIPS 2016 • Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin
A novel variational autoencoder is developed to model images, as well as associated labels or captions.
no code implementations • NeurIPS 2016 • Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
no code implementations • 16 Dec 2015 • Yizhe Zhang, Ricardo Henao, Lawrence Carin, Jianling Zhong, Alexander J. Hartemink
When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states.
no code implementations • NeurIPS 2015 • Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
We present a scalable Bayesian multi-label learning model based on learning low-dimensional label embeddings.
no code implementations • NeurIPS 2015 • Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin
We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PFA) modules.
1 code implementation • NeurIPS 2015 • Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson, Lawrence Carin
Deep dynamic generative models are developed to learn sequential dependencies in time-series data.
no code implementations • 28 Apr 2015 • Xin Yuan, Ricardo Henao, Ephraim L. Tsalik, Raymond J. Langley, Lawrence Carin
A Bayesian model based on the ranks of the data is proposed.
no code implementations • NeurIPS 2014 • Ricardo Henao, Xin Yuan, Lawrence Carin
A new Bayesian formulation is developed for nonlinear support vector machines (SVMs), based on a Gaussian process and with the SVM hinge loss expressed as a scaled mixture of normals.
no code implementations • NeurIPS 2009 • Ricardo Henao, Ole Winther
In this paper we present a novel approach to learn directed acyclic graphs (DAG) and factor models within the same framework while also allowing for model comparison between them.