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05/07/2023
Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Transformer models are foundational to natural language processing (NLP)...
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07/21/2022
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Transformers have emerged as a preferred model for many tasks in natural...
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07/20/2022
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Comparing the functional behavior of neural network models, whether it i...
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04/15/2022
Deep Unlearning via Randomized Conditionally Independent Hessians
Recent legislation has led to interest in machine unlearning, i.e., remo...
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03/29/2022
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets
Pooling multiple neuroimaging datasets across institutions often enables...
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02/19/2022
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Uncertainty estimation in deep models is essential in many real-world ap...
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02/18/2022
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data
Panel data involving longitudinal measurements of the same set of partic...
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12/01/2021
Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Generative models which use explicit density modeling (e.g., variational...
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11/18/2021
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
Transformer-based models are widely used in natural language processing ...
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08/19/2021
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators
We study how stochastic differential equation (SDE) based ideas can insp...
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06/08/2021
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
We present an efficient stochastic algorithm (RSG+) for canonical correl...
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05/12/2021
Connecting What to Say With Where to Look by Modeling Human Attention Traces
We introduce a unified framework to jointly model images, text, and huma...
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04/13/2021
Simpler Certified Radius Maximization by Propagating Covariances
One strategy for adversarially training a robust model is to maximize it...
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02/16/2021
Learning Invariant Representations using Inverse Contrastive Loss
Learning invariant representations is a critical first step in a number ...
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02/07/2021
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
Transformers have emerged as a powerful tool for a broad range of natura...
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01/31/2021
Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center
Customer satisfaction is an important factor in creating and maintaining...
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12/18/2020
Flow-based Generative Models for Learning Manifold to Manifold Mappings
Many measurements or observations in computer vision and machine learnin...
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09/02/2020
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
Clustering techniques have been proved highly suc-cessful for Takagi-Sug...
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08/12/2020
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
The adoption of "human-in-the-loop" paradigms in computer vision and mac...
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08/10/2020
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise
Image denoising is one of the preliminary steps in image processing meth...
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04/30/2020
Physarum Powered Differentiable Linear Programming Layers and Applications
Consider a learning algorithm, which involves an internal call to an opt...
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04/30/2020
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Inverted bottleneck layers, which are built upon depthwise convolutions,...
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04/03/2020
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret
Algorithmic decision making based on computer vision and machine learnin...
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12/24/2019
mRMR-DNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines
In recent years, intelligent condition-based monitoring of rotary machin...
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12/24/2019
An Entropy-based Variable Feature Weighted Fuzzy k-Means Algorithm for High Dimensional Data
This paper presents a new fuzzy k-means algorithm for the clustering of ...
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10/05/2019
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data
Efforts are underway to study ways via which the power of deep neural ne...
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09/26/2019
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains
Data dependent regularization is known to benefit a wide variety of prob...
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09/12/2019
Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently
Rectified Linear Units (ReLUs) are among the most widely used activation...
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08/21/2019
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Positron emission tomography (PET) imaging is an imaging modality for di...
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08/20/2019
Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
For large-scale testing with graph-associated data, we present an empiri...
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05/16/2019
Fooling Computer Vision into Inferring the Wrong Body Mass Index
Recently it's been shown that neural networks can use images of human fa...
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04/08/2019
Resource Constrained Neural Network Architecture Search
The design of neural network architectures is frequently either based on...
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11/24/2018
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Generative models using neural network have opened a door to large-scale...
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06/10/2018
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty
We provide simple schemes to build Bayesian Neural Networks (BNNs), bloc...
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05/29/2018
Statistical Recurrent Models on Manifold valued Data
In a number of disciplines, the data (e.g., graphs, manifolds) to be ana...
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04/19/2018
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
There has recently been a concerted effort to derive mechanisms in visio...
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03/21/2018
Robust Blind Deconvolution via Mirror Descent
We revisit the Blind Deconvolution problem with a focus on understanding...
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03/17/2018
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
A number of results have recently demonstrated the benefits of incorpora...
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11/20/2017
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for...
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09/02/2017
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, ℓ_2-consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample size...
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08/22/2017
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
We present a new Frank-Wolfe (FW) type algorithm that is applicable to m...
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05/16/2017
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools...
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03/04/2017
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM
Permutation testing is a non-parametric method for obtaining the max nul...
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02/28/2017
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation
We study mechanisms to characterize how the asymptotic convergence of ba...
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11/17/2015
On the interplay of network structure and gradient convergence in deep learning
The regularization and output consistency behavior of dropout and layer-...
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06/10/2015
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure
Unsupervised pretraining and dropout have been well studied, especially ...
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02/12/2015
Convergence of gradient based pre-training in Denoising autoencoders
The success of deep architectures is at least in part attributed to the ...
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02/12/2015
Speeding up Permutation Testing in Neuroimaging
Multiple hypothesis testing is a significant problem in nearly all neuro...
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06/27/2012