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05/23/2023
Mitigating Test-Time Bias for Fair Image Retrieval
We address the challenge of generating fair and unbiased image retrieval...
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03/09/2023
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with ...
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10/23/2022
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Weight pruning is among the most popular approaches for compressing deep...
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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...
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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 ...
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02/03/2022
Flexible Triggering Kernels for Hawkes Process Modeling
Recently proposed encoder-decoder structures for modeling Hawkes process...
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07/05/2021
Imputation-Free Learning from Incomplete Observations
Although recent works have developed methods that can generate estimatio...
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06/04/2021
Efficient Classification of Very Large Images with Tiny Objects
An increasing number of applications in the computer vision domain, spec...
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04/02/2021
Malignancy Prediction and Lesion Identification from Clinical Dermatological Images
We consider machine-learning-based malignancy prediction and lesion iden...
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12/15/2020
Wasserstein Contrastive Representation Distillation
The primary goal of knowledge distillation (KD) is to encapsulate the in...
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12/06/2020
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Deep neural networks have shown significant promise in comprehending com...
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11/25/2020
Supercharging Imbalanced Data Learning With Causal Representation Transfer
Dealing with severe class imbalance poses a major challenge for real-wor...
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10/23/2020
Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance i...
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09/17/2020
Variational Disentanglement for Rare Event Modeling
Combining the increasing availability and abundance of healthcare data a...
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08/14/2020
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key t...
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06/14/2020
Survival Analysis meets Counterfactual Inference
There is growing interest in applying machine learning methods for count...
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04/03/2020
Neural Conditional Event Time Models
Event time models predict occurrence times of an event of interest based...
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03/09/2020
Variational Learning of Individual Survival Distributions
The abundance of modern health data provides many opportunities for the ...
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02/29/2020
Survival Cluster Analysis
Conventional survival analysis approaches estimate risk scores or indivi...
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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...
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02/07/2020
Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of dat...
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10/09/2019
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
We investigate time-dependent data analysis from the perspective of recu...
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10/05/2019
Straight-Through Estimator as Projected Wasserstein Gradient Flow
The Straight-Through (ST) estimator is a widely used technique for back-...
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09/30/2019
Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges o...
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05/30/2019
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Unsupervised domain adaptation seeks to learn an invariant and discrimin...
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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...
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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...
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04/07/2019
An unsupervised transfer learning algorithm for sleep monitoring
Objective: To develop multisensor-wearable-device sleep monitoring algor...
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03/29/2019
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
We consider preoperative prediction of thyroid cancer based on ultra-hig...
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08/29/2018
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Network embeddings, which learn low-dimensional representations for each...
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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...
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06/08/2018
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
A new generative adversarial network is developed for joint distribution...
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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...
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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...
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05/10/2018
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing...
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04/09/2018
Adversarial Time-to-Event Modeling
Modern health data science applications leverage abundant molecular and ...
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01/15/2018
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Predicting diagnoses from Electronic Health Records (EHRs) is an importa...
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09/21/2017
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sente...
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09/05/2017
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the non-identifiability issues associated with bidirectio...
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08/16/2017
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important...
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06/12/2017
Adversarial Feature Matching for Text Generation
The Generative Adversarial Network (GAN) has achieved great success in g...
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06/05/2017
Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
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11/23/2016
Learning Generic Sentence Representations Using Convolutional Neural Networks
We propose a new encoder-decoder approach to learn distributed sentence ...
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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...
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02/25/2016
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
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12/16/2015
Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen- tial observations can...
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09/23/2015
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential depende...
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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 ...
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04/20/2012
Efficient hierarchical clustering for continuous data
We present an new sequential Monte Carlo sampler for coalescent based Ba...
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02/22/2011
