b'Ricardo Henao'

research

∙ 10/23/2022

Pushing the Efficiency Limit Using Structured Sparse Convolutions

Weight pruning is among the most popular approaches for compressing deep...

Vinay Kumar Verma, et al. ∙

research

∙ 10/11/2022

Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks

Most existing works on continual learning (CL) focus on overcoming the c...

Sijia Wang, et al. ∙

research

∙ 02/25/2022

Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

End-to-end learning of dynamical systems with black-box models, such as ...

Paidamoyo Chapfuwa, et al. ∙

research

∙ 02/03/2022

Flexible Triggering Kernels for Hawkes Process Modeling

Recently proposed encoder-decoder structures for modeling Hawkes process...

Yamac Alican Isik, et al. ∙

research

∙ 07/05/2021

Imputation-Free Learning from Incomplete Observations

Although recent works have developed methods that can generate estimatio...

Qitong Gao, et al. ∙

research

∙ 06/04/2021

Efficient Classification of Very Large Images with Tiny Objects

An increasing number of applications in the computer vision domain, spec...

Fanjie Kong, et al. ∙

research

∙ 04/02/2021

Malignancy Prediction and Lesion Identification from Clinical Dermatological Images

We consider machine-learning-based malignancy prediction and lesion iden...

Meng Xia, et al. ∙

research

∙ 12/15/2020

Wasserstein Contrastive Representation Distillation

The primary goal of knowledge distillation (KD) is to encapsulate the in...

Liqun Chen, et al. ∙

research

∙ 12/06/2020

Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models

Deep neural networks have shown significant promise in comprehending com...

Dong Wang, et al. ∙

research

∙ 11/25/2020

Supercharging Imbalanced Data Learning With Causal Representation Transfer

Dealing with severe class imbalance poses a major challenge for real-wor...

Junya Chen, et al. ∙

research

∙ 10/23/2020

Counterfactual Representation Learning with Balancing Weights

A key to causal inference with observational data is achieving balance i...

Serge Assaad, et al. ∙

research

∙ 09/17/2020

Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data a...

Zidi Xiu, et al. ∙

research

∙ 08/14/2020

Weakly supervised cross-domain alignment with optimal transport

Cross-domain alignment between image objects and text sequences is key t...

Siyang Yuan, et al. ∙

research

∙ 06/14/2020

Survival Analysis meets Counterfactual Inference

There is growing interest in applying machine learning methods for count...

Paidamoyo Chapfuwa, et al. ∙

research

∙ 04/03/2020

Neural Conditional Event Time Models

Event time models predict occurrence times of an event of interest based...

Matthew Engelhard, et al. ∙

research

∙ 03/09/2020

Variational Learning of Individual Survival Distributions

The abundance of modern health data provides many opportunities for the ...

Zidi Xiu, et al. ∙

research

∙ 02/29/2020

Survival Cluster Analysis

Conventional survival analysis approaches estimate risk scores or indivi...

Paidamoyo Chapfuwa, et al. ∙

research

∙ 02/12/2020

Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes

Developing machine learning models for radiology requires large-scale im...

Rachel Lea Ballantyne Draelos, et al. ∙

research

∙ 02/07/2020

Learning Autoencoders with Relational Regularization

A new algorithmic framework is proposed for learning autoencoders of dat...

Hongteng Xu, et al. ∙

research

∙ 10/09/2019

Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

We investigate time-dependent data analysis from the perspective of recu...

Kevin J Liang, et al. ∙

research

∙ 10/05/2019

Straight-Through Estimator as Projected Wasserstein Gradient Flow

The Straight-Through (ST) estimator is a widely used technique for back-...

Pengyu Cheng, et al. ∙

research

∙ 09/30/2019

Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges o...

Wenlin Wang, et al. ∙

research

∙ 05/30/2019

Discriminative Clustering for Robust Unsupervised Domain Adaptation

Unsupervised domain adaptation seeks to learn an invariant and discrimin...

Rui Wang, et al. ∙

research

∙ 05/21/2019

Survival Function Matching for Calibrated Time-to-Event Predictions

Models for predicting the time of a future event are crucial for risk as...

Paidamoyo Chapfuwa, et al. ∙

research

∙ 04/26/2019

A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images

We consider thyroid-malignancy prediction from ultra-high-resolution who...

David Dov, et al. ∙

research

∙ 04/07/2019

An unsupervised transfer learning algorithm for sleep monitoring

Objective: To develop multisensor-wearable-device sleep monitoring algor...

Xichen She, et al. ∙

research

∙ 03/29/2019

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

We consider preoperative prediction of thyroid cancer based on ultra-hig...

David Dov, et al. ∙

research

∙ 08/29/2018

Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment

Network embeddings, which learn low-dimensional representations for each...

Dinghan Shen, et al. ∙

research

∙ 07/24/2018

Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients

We develop a hierarchical infinite latent factor model (HIFM) to appropr...

Elizabeth Lorenzi, et al. ∙

research

∙ 06/08/2018

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

A new generative adversarial network is developed for joint distribution...

Yunchen Pu, et al. ∙

research

∙ 05/24/2018

Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Many deep learning architectures have been proposed to model the composi...

Dinghan Shen, et al. ∙

research

∙ 05/14/2018

NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Semantic hashing has become a powerful paradigm for fast similarity sear...

Dinghan Shen, et al. ∙

research

∙ 05/10/2018

Joint Embedding of Words and Labels for Text Classification

Word embeddings are effective intermediate representations for capturing...

Guoyin Wang, et al. ∙

research

∙ 04/09/2018

Adversarial Time-to-Event Modeling

Modern health data science applications leverage abundant molecular and ...

Paidamoyo Chapfuwa, et al. ∙

research

∙ 01/15/2018

Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

Predicting diagnoses from Electronic Health Records (EHRs) is an importa...

Xinyuan Zhang, et al. ∙

research

∙ 09/21/2017

Deconvolutional Latent-Variable Model for Text Sequence Matching

A latent-variable model is introduced for text matching, inferring sente...

Dinghan Shen, et al. ∙

research

∙ 09/05/2017

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

We investigate the non-identifiability issues associated with bidirectio...

Chunyuan Li, et al. ∙

research

∙ 08/16/2017

Deconvolutional Paragraph Representation Learning

Learning latent representations from long text sequences is an important...

Yizhe Zhang, et al. ∙

research

∙ 06/12/2017

Adversarial Feature Matching for Text Generation

The Generative Adversarial Network (GAN) has achieved great success in g...

Yizhe Zhang, et al. ∙

research

∙ 06/05/2017

Stochastic Gradient Monomial Gamma Sampler

Recent advances in stochastic gradient techniques have made it possible ...

Yizhe Zhang, et al. ∙

research

∙ 11/23/2016

Learning Generic Sentence Representations Using Convolutional Neural Networks

We propose a new encoder-decoder approach to learn distributed sentence ...

Zhe Gan, et al. ∙

research

∙ 09/28/2016

Variational Autoencoder for Deep Learning of Images, Labels and Captions

A novel variational autoencoder is developed to model images, as well as...

Yunchen Pu, et al. ∙

research

∙ 02/25/2016

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...

Yizhe Zhang, et al. ∙

research

∙ 12/16/2015

Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can...

Yizhe Zhang, et al. ∙

research

∙ 09/23/2015

Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Deep dynamic generative models are developed to learn sequential depende...

Zhe Gan, et al. ∙

research

∙ 04/28/2015

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood

We consider the problem of discriminative factor analysis for data that ...

Xin Yuan, et al. ∙

research

∙ 04/20/2012

Efficient hierarchical clustering for continuous data

We present an new sequential Monte Carlo sampler for coalescent based Ba...

Ricardo Henao, et al. ∙

research

∙ 02/22/2011

Predictive Active Set Selection Methods for Gaussian Processes

We propose an active set selection framework for Gaussian process classi...

Ricardo Henao, et al. ∙

Ricardo Henao

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