b'Diego Klabjan'

research

∙ 09/03/2023

Semi-supervised 3D Video Information Retrieval with Deep Neural Network and Bi-directional Dynamic-time Warping Algorithm

This paper presents a novel semi-supervised deep learning algorithm for ...

0 Yintai Ma, et al. ∙

research

∙ 08/15/2023

Regret Lower Bounds in Multi-agent Multi-armed Bandit

Multi-armed Bandit motivates methods with provable upper bounds on regre...

0 Mengfan Xu, et al. ∙

research

∙ 07/01/2023

S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture

Multimodal multitask learning has attracted an increasing interest in re...

0 Ye Xue, et al. ∙

research

∙ 06/08/2023

Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards

We study a decentralized multi-agent multi-armed bandit problem in which...

0 Mengfan Xu, et al. ∙

research

∙ 05/02/2023

Early Classifying Multimodal Sequences

Often pieces of information are received sequentially over time. When di...

0 Alexander Cao, et al. ∙

research

∙ 04/07/2023

A Policy for Early Sequence Classification

Sequences are often not received in their entirety at once, but instead,...

0 Alexander Cao, et al. ∙

research

∙ 02/28/2023

Gradient-Boosted Based Structured and Unstructured Learning

We propose two frameworks to deal with problem settings in which both st...

0 Andrea Treviño Gavito, et al. ∙

research

∙ 02/28/2023

Multi-Layer Attention-Based Explainability via Transformers for Tabular Data

We propose a graph-oriented attention-based explainability method for ta...

0 Andrea Treviño Gavito, et al. ∙

research

∙ 12/21/2022

Feature Acquisition using Monte Carlo Tree Search

Feature acquisition algorithms address the problem of acquiring informat...

0 Sungsoo Lim, et al. ∙

research

∙ 12/01/2022

Pareto Regret Analyses in Multi-objective Multi-armed Bandit

We study Pareto optimality in multi-objective multi-armed bandit by prov...

0 Mengfan Xu, et al. ∙

research

∙ 10/14/2022

A Primal-Dual Algorithm for Hybrid Federated Learning

Very few methods for hybrid federated learning, where clients only hold ...

0 Tom Overman, et al. ∙

research

∙ 10/11/2022

Divergence Results and Convergence of a Variance Reduced Version of ADAM

Stochastic optimization algorithms using exponential moving averages of ...

0 Ruiqi Wang, et al. ∙

research

∙ 03/01/2022

Topic Analysis for Text with Side Data

Although latent factor models (e.g., matrix factorization) obtain good p...

0 Biyi Fang, et al. ∙

research

∙ 03/01/2022

Tricks and Plugins to GBM on Images and Sequences

Convolutional neural networks (CNNs) and transformers, which are compose...

0 Biyi Fang, et al. ∙

research

∙ 01/09/2022

Open-Set Recognition of Breast Cancer Treatments

Open-set recognition generalizes a classification task by classifying te...

0 Alexander Cao, et al. ∙

research

∙ 11/16/2021

Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation

Existing methods of pruning deep neural networks focus on removing unnec...

0 Abdolghani Ebrahimi, et al. ∙

research

∙ 08/17/2021

Aggregation Delayed Federated Learning

Federated learning is a distributed machine learning paradigm where mult...

0 Ye Xue, et al. ∙

research

∙ 07/06/2021

Logit-based Uncertainty Measure in Classification

We introduce a new, reliable, and agnostic uncertainty measure for class...

0 Huiyu Wu, et al. ∙

research

∙ 05/20/2021

A Probabilistic Approach to Neural Network Pruning

Neural network pruning techniques reduce the number of parameters withou...

0 Xin Qian, et al. ∙

research

∙ 02/22/2021

Non-Convex Optimization with Spectral Radius Regularization

We develop a regularization method which finds flat minima during the tr...

0 Adam Sandler, et al. ∙

research

∙ 01/31/2021

Classification Models for Partially Ordered Sequences

Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Unit...

0 Stephanie Ger, et al. ∙

research

∙ 12/22/2020

Open Set Domain Adaptation by Extreme Value Theory

Common domain adaptation techniques assume that the source domain and th...

0 Yiming Xu, et al. ∙

research

∙ 12/08/2020

Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels

In model serving, having one fixed model during the entire often life-lo...

0 Yiming Xu, et al. ∙

research

∙ 09/29/2020

Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples

Most recent machine learning research focuses on developing new classifi...

1 Jaehoon Koo, et al. ∙

research

∙ 09/20/2020

Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms

EXP-based algorithms are often used for exploration in multi-armed bandi...

9 Mengfan Xu, et al. ∙

research

∙ 06/07/2020

Efficient Architecture Search for Continual Learning

Continual learning with neural networks is an important learning framewo...

36 Qiang Gao, et al. ∙

research

∙ 06/03/2020

Open-Set Recognition with Gaussian Mixture Variational Autoencoders

In inference, open-set classification is to either classify a sample int...

5 Alexander Cao, et al. ∙

research

∙ 04/29/2020

Neural Network Retraining for Model Serving

We propose incremental (re)training of a neural network model to cope wi...

7 Diego Klabjan, et al. ∙

research

∙ 04/27/2020

The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent

The mini-batch stochastic gradient descent (SGD) algorithm is widely use...

0 Xin Qian, et al. ∙

research

∙ 01/22/2020

Keyword-based Topic Modeling and Keyword Selection

Certain type of documents such as tweets are collected by specifying a s...

7 Xingyu Wang, et al. ∙

research

∙ 01/07/2020

Listwise Learning to Rank by Exploring Unique Ratings

In this paper, we propose new listwise learning-to-rank models that miti...

6 Xiaofeng Zhu, et al. ∙

research

∙ 11/27/2019

Conditional Hierarchical Bayesian Tucker Decomposition

Our research focuses on studying and developing methods for reducing the...

4 Adam Sandler, et al. ∙

research

∙ 08/12/2019

Mixture-based Multiple Imputation Models for Clinical Data with a Temporal Dimension

The problem of missing values in multivariable time series is a key chal...

1 Ye Xue, et al. ∙

research

∙ 08/12/2019

Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension

The problem of missing values in multivariable time series is a key chal...

0 Ye Xue, et al. ∙

research

∙ 05/25/2019

Dynamic Cell Structure via Recursive-Recurrent Neural Networks

In a recurrent setting, conventional approaches to neural architecture s...

4 Xin Qian, et al. ∙

research

∙ 05/23/2019

Scale Invariant Power Iteration

Power iteration has been generalized to solve many interesting problems ...

0 Cheolmin Kim, et al. ∙

research

∙ 05/22/2019

Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network

Nowadays, online learning is an appealing learning paradigm, which is of...

0 Biyi Fang, et al. ∙

research

∙ 03/07/2019

Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data

Ontology learning is a critical task in industry, dealing with identifyi...

20 Yiming Xu, et al. ∙

research

∙ 01/08/2019

Autoencoders and Generative Adversarial Networks for Anomaly Detection for Sequences

We introduce synthetic oversampling in anomaly detection for multi-featu...

6 Stephanie Ger, et al. ∙

research

∙ 12/06/2018

Layer Flexible Adaptive Computational Time for Recurrent Neural Networks

Deep recurrent neural networks show significant benefits in prediction t...

12 Lida Zhang, et al. ∙

research

∙ 09/25/2018

Combined convolutional and recurrent neural networks for hierarchical classification of images

Deep learning models based on CNNs are predominantly used in image class...

12 Jaehoon Koo, et al. ∙

research

∙ 09/24/2018

Unified recurrent neural network for many feature types

There are time series that are amenable to recurrent neural network (RNN...

8 Alexander Stec, et al. ∙

research

∙ 08/30/2018

Nested multi-instance classification

There are classification tasks that take as inputs groups of images rath...

4 Alexander Stec, et al. ∙

research

∙ 07/02/2018

Dynamic Prediction Length for Time Series with Sequence to Sequence Networks

Recurrent neural networks and sequence to sequence models require a pred...

6 Mark Harmon, et al. ∙

research

∙ 06/05/2018

Forecasting Crime with Deep Learning

The objective of this work is to take advantage of deep neural networks ...

4 Alexander Stec, et al. ∙

research

∙ 05/04/2018

Bayesian active learning for choice models with deep Gaussian processes

In this paper, we propose an active learning algorithm and models which ...

0 Jie Yang, et al. ∙

research

∙ 04/27/2018

k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks

k-Nearest Neighbors is one of the most fundamental but effective classif...

0 Yiming Xu, et al. ∙

research

∙ 04/25/2018

Improved Classification Based on Deep Belief Networks

For better classification generative models are used to initialize the m...

0 Jaehoon Koo, et al. ∙

research

∙ 03/29/2018

A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations

As the size of modern data sets exceeds the disk and memory capacities o...

0 Biyi Fang, et al. ∙

research

∙ 02/15/2018

Truth Validation with Evidence

In the modern era, abundant information is easily accessible from variou...

0 Papis Wongchaisuwat, et al. ∙

Diego Klabjan

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