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 ...
share
research
∙
08/15/2023
Regret Lower Bounds in Multi-agent Multi-armed Bandit
Multi-armed Bandit motivates methods with provable upper bounds on regre...
research
∙
07/01/2023
S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture
Multimodal multitask learning has attracted an increasing interest in re...
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...
research
∙
05/02/2023
Early Classifying Multimodal Sequences
Often pieces of information are received sequentially over time. When di...
research
∙
04/07/2023
A Policy for Early Sequence Classification
Sequences are often not received in their entirety at once, but instead,...
research
∙
02/28/2023
Gradient-Boosted Based Structured and Unstructured Learning
We propose two frameworks to deal with problem settings in which both st...
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...
research
∙
12/21/2022
Feature Acquisition using Monte Carlo Tree Search
Feature acquisition algorithms address the problem of acquiring informat...
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...
research
∙
10/14/2022
A Primal-Dual Algorithm for Hybrid Federated Learning
Very few methods for hybrid federated learning, where clients only hold ...
research
∙
10/11/2022
Divergence Results and Convergence of a Variance Reduced Version of ADAM
Stochastic optimization algorithms using exponential moving averages of ...
research
∙
03/01/2022
Topic Analysis for Text with Side Data
Although latent factor models (e.g., matrix factorization) obtain good p...
research
∙
03/01/2022
Tricks and Plugins to GBM on Images and Sequences
Convolutional neural networks (CNNs) and transformers, which are compose...
research
∙
01/09/2022
Open-Set Recognition of Breast Cancer Treatments
Open-set recognition generalizes a classification task by classifying te...
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...
research
∙
08/17/2021
Aggregation Delayed Federated Learning
Federated learning is a distributed machine learning paradigm where mult...
research
∙
07/06/2021
Logit-based Uncertainty Measure in Classification
We introduce a new, reliable, and agnostic uncertainty measure for class...
research
∙
05/20/2021
A Probabilistic Approach to Neural Network Pruning
Neural network pruning techniques reduce the number of parameters withou...
research
∙
02/22/2021
Non-Convex Optimization with Spectral Radius Regularization
We develop a regularization method which finds flat minima during the tr...
research
∙
01/31/2021
Classification Models for Partially Ordered Sequences
Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Unit...
research
∙
12/22/2020
Open Set Domain Adaptation by Extreme Value Theory
Common domain adaptation techniques assume that the source domain and th...
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...
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...
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...
research
∙
06/07/2020
Efficient Architecture Search for Continual Learning
Continual learning with neural networks is an important learning framewo...
research
∙
06/03/2020
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
In inference, open-set classification is to either classify a sample int...
research
∙
04/29/2020
Neural Network Retraining for Model Serving
We propose incremental (re)training of a neural network model to cope wi...
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...
research
∙
01/22/2020
Keyword-based Topic Modeling and Keyword Selection
Certain type of documents such as tweets are collected by specifying a s...
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...
research
∙
11/27/2019
Conditional Hierarchical Bayesian Tucker Decomposition
Our research focuses on studying and developing methods for reducing the...
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...
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...
research
∙
05/25/2019
Dynamic Cell Structure via Recursive-Recurrent Neural Networks
In a recurrent setting, conventional approaches to neural architecture s...
research
∙
05/23/2019
Scale Invariant Power Iteration
Power iteration has been generalized to solve many interesting problems ...
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...
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...
research
∙
01/08/2019
Autoencoders and Generative Adversarial Networks for Anomaly Detection for Sequences
We introduce synthetic oversampling in anomaly detection for multi-featu...
research
∙
12/06/2018
Layer Flexible Adaptive Computational Time for Recurrent Neural Networks
Deep recurrent neural networks show significant benefits in prediction t...
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...
research
∙
09/24/2018
Unified recurrent neural network for many feature types
There are time series that are amenable to recurrent neural network (RNN...
research
∙
08/30/2018
Nested multi-instance classification
There are classification tasks that take as inputs groups of images rath...
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...
research
∙
06/05/2018
Forecasting Crime with Deep Learning
The objective of this work is to take advantage of deep neural networks ...
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 ...
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...
research
∙
04/25/2018
Improved Classification Based on Deep Belief Networks
For better classification generative models are used to initialize the m...
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...
research
∙
02/15/2018
