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01/05/2022
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledg...
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05/05/2020
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Predictive models that accurately emulate complex scientific processes c...
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05/03/2020
Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification
Automated diagnostic assistants in healthcare necessitate accurate AI mo...
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04/27/2020
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
The wide-spread adoption of representation learning technologies in clin...
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02/10/2020
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
The hypothesis that sub-network initializations (lottery) exist within t...
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12/17/2019
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Neural networks have become very popular in surrogate modeling because o...
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12/16/2019
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
In the past few years, generative models like Generative Adversarial Net...
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11/24/2019
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning
Exploiting known semantic relationships between fine-grained tasks is cr...
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10/30/2019
Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning
The role of uncertainty quantification (UQ) in deep learning has become ...
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10/30/2019
Learn-By-Calibrating: Using Calibration as a Training Objective
Calibration error is commonly adopted for evaluating the quality of unce...
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10/03/2019
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
There is significant interest in using modern neural networks for scient...
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10/03/2019
Improving Limited Angle CT Reconstruction with a Robust GAN Prior
Limited angle CT reconstruction is an under-determined linear inverse pr...
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09/25/2019
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
We present function preserving projections (FPP), a scalable linear proj...
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09/09/2019
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
With rapid adoption of deep learning in high-regret applications, the qu...
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07/26/2019
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation
Weakly supervised instance labeling using only image-level labels, in li...
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07/19/2019
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
With the rapid adoption of machine learning techniques for large-scale a...
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06/11/2019
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
Unsupervised domain adaptation aims to transfer and adapt knowledge lear...
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06/06/2019
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
This paper provides a general framework to study the effect of sampling ...
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04/08/2019
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets
Modern audio source separation techniques rely on optimizing sequence mo...
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11/20/2018
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense
Solving inverse problems continues to be a central challenge in computer...
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11/11/2018
Multiple Subspace Alignment Improves Domain Adaptation
We present a novel unsupervised domain adaptation (DA) method for cross-...
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11/01/2018
Designing an Effective Metric Learning Pipeline for Speaker Diarization
State-of-the-art speaker diarization systems utilize knowledge from exte...
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11/01/2018
Improving Robustness of Attention Models on Graphs
Machine learning models that can exploit the inherent structure in data ...
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10/31/2018
Unsupervised Dimension Selection using a Blue Noise Spectrum
Unsupervised dimension selection is an important problem that seeks to r...
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10/31/2018
Understanding Deep Neural Networks through Input Uncertainties
Techniques for understanding the functioning of complex machine learning...
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10/02/2018
Attention Models with Random Features for Multi-layered Graph Embeddings
Modern data analysis pipelines are becoming increasingly complex due to ...
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09/20/2018
Can Deep Clinical Models Handle Real-World Domain Shifts?
The hypothesis that computational models can be reliable enough to be ad...
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09/05/2018
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization
A common challenge in machine learning and related fields is the need to...
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08/04/2018
Triplet Network with Attention for Speaker Diarization
In automatic speech processing systems, speaker diarization is a crucial...
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05/18/2018
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Solving inverse problems continues to be a challenge in a wide array of ...
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02/18/2018
A Generative Modeling Approach to Limited Channel ECG Classification
Processing temporal sequences is central to a variety of applications in...
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12/19/2017
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Two-dimensional embeddings remain the dominant approach to visualize hig...
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12/16/2017
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
This paper proposes a new approach to construct high quality space-filli...
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11/28/2017
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Computed Tomography (CT) reconstruction is a fundamental component to a ...
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11/15/2017
Influential Sample Selection: A Graph Signal Processing Approach
With the growing complexity of machine learning techniques, understandin...
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11/15/2017
Optimizing Kernel Machines using Deep Learning
Building highly non-linear and non-parametric models is central to sever...
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11/10/2017
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
With widespread adoption of electronic health records, there is an incre...
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04/24/2017
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification
Using predictive models to identify patterns that can act as biomarkers ...
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12/28/2016
A Deep Learning Approach To Multiple Kernel Fusion
Kernel fusion is a popular and effective approach for combining multiple...
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01/22/2016
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
This paper considers the problem of high dimensional signal detection in...
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11/12/2015
Automatic Inference of the Quantile Parameter
Supervised learning is an active research area, with numerous applicatio...
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03/26/2014
Beyond L2-Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple,...
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03/12/2013
Recovering Non-negative and Combined Sparse Representations
The non-negative solution to an underdetermined linear system can be uni...
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03/03/2013