b'UMass Lowell'

research

∙ 05/06/2022

Federated Learning with Noisy User Feedback

Machine Learning (ML) systems are getting increasingly popular, and driv...

Rahul Sharma, et al. ∙

research

∙ 04/08/2021

CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets

Financial markets are a complex dynamical system. The complexity comes f...

Jia Wang, et al. ∙

research

∙ 04/10/2019

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes

There is a growing body of work that proposes methods for mitigating bia...

Alexey Romanov, et al. ∙

research

∙ 04/03/2020

Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting ...

Alexander Ding, et al. ∙

research

∙ 11/29/2018

HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes

Hypoglycemia is common and potentially dangerous among those treated for...

Yonghao Jin, et al. ∙

research

∙ 03/26/2020

Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a primary cause of blindness in working-age...

Qilei Chen, et al. ∙

research

∙ 03/08/2021

Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks

In recent years, many learning based approaches have been studied to rea...

Wenzhao Lian, et al. ∙

research

∙ 01/27/2022

A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment

In Vitro Fertilization (IVF) is the most widely used Assisted Reproducti...

Xizhe Wang, et al. ∙

research

∙ 11/17/2015

Return of Frustratingly Easy Domain Adaptation

Unlike human learning, machine learning often fails to handle changes be...

Baochen Sun, et al. ∙

research

∙ 11/17/2015

Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

We address the problem of Visual Question Answering (VQA), which require...

Huijuan Xu, et al. ∙

research

∙ 08/03/2017

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

In this paper, we introduce a new CT image denoising method based on the...

Qingsong Yang, et al. ∙

research

∙ 12/21/2013

One-Shot Adaptation of Supervised Deep Convolutional Models

Dataset bias remains a significant barrier towards solving real world co...

Judy Hoffman, et al. ∙

research

∙ 11/13/2015

Natural Language Object Retrieval

In this paper, we address the task of natural language object retrieval,...

Ronghang Hu, et al. ∙

research

∙ 10/08/2015

Simultaneous Deep Transfer Across Domains and Tasks

Recent reports suggest that a generic supervised deep CNN model trained ...

Eric Tzeng, et al. ∙

research

∙ 04/09/2015

What Do Deep CNNs Learn About Objects?

Deep convolutional neural networks learn extremely powerful image repres...

Xingchao Peng, et al. ∙

research

∙ 12/15/2014

Translating Videos to Natural Language Using Deep Recurrent Neural Networks

Solving the visual symbol grounding problem has long been a goal of arti...

Subhashini Venugopalan, et al. ∙

research

∙ 12/10/2014

Deep Domain Confusion: Maximizing for Domain Invariance

Recent reports suggest that a generic supervised deep CNN model trained ...

Eric Tzeng, et al. ∙

research

∙ 12/02/2014

Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning

We develop methods for detector learning which exploit joint training ov...

Judy Hoffman, et al. ∙

research

∙ 07/18/2014

LSDA: Large Scale Detection Through Adaptation

A major challenge in scaling object detection is the difficulty of obtai...

Judy Hoffman, et al. ∙

research

∙ 11/27/2013

Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images

To produce images that are suitable for display, tone-mapping is widely ...

Ayan Chakrabarti, et al. ∙

research

∙ 09/23/2003

Fast Verification of Convexity of Piecewise-linear Surfaces

We show that a realization of a closed connected PL-manifold of dimensio...

Konstantin Rybnikov, et al. ∙

research

∙ 10/01/2017

DTATG: An Automatic Title Generator based on Dependency Trees

We study automatic title generation for a given block of text and presen...

Liqun Shao, et al. ∙

research

∙ 10/01/2017

Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality

Our task is to generate an effective summary for a given document with s...

Liqun Shao, et al. ∙

research

∙ 03/17/2017

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture

In this paper, we propose to use a set of simple, uniform in architectur...

Yuanliang Meng, et al. ∙

research

∙ 12/28/2016

Here's My Point: Joint Pointer Architecture for Argument Mining

One of the major goals in automated argumentation mining is to uncover t...

Peter Potash, et al. ∙

research

∙ 12/09/2016

#HashtagWars: Learning a Sense of Humor

In this work, we present a new dataset for computational humor, specific...

Peter Potash, et al. ∙

research

∙ 12/09/2016

Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting

Language generation tasks that seek to mimic human ability to use langua...

Peter Potash, et al. ∙

research

∙ 11/28/2017

A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System

We introduce a game-theoretic framework to ex- plore revenue sharing in ...

Zhi Cao, et al. ∙

research

∙ 08/28/2017

Design Decisions for Weave: A Real-Time Web-based Collaborative Visualization Framework

There are many web-based visualization systems available to date, each h...

Andrew Dufilie, et al. ∙

research

∙ 03/06/2018

CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

Clinical notes often describe important aspects of a patient's stay and ...

Willie Boag, et al. ∙

research

∙ 04/25/2018

Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers

Shrinking transistors, which powered the advancement of computing in the...

Hang Liu, et al. ∙

research

∙ 05/15/2018

IoT Security: An End-to-End View and Case Study

In this paper, we present an end-to-end view of IoT security and privacy...

Zhen Ling, et al. ∙

research

∙ 06/28/2018

Deep learning in business analytics and operations research: Models, applications and managerial implications

Business analytics refers to methods and practices that create value thr...

Mathias Kraus, et al. ∙

research

∙ 08/21/2018

Lessons from Natural Language Inference in the Clinical Domain

State of the art models using deep neural networks have become very good...

Alexey Romanov, et al. ∙

research

∙ 08/27/2018

Adversarial Decomposition of Text Representation

In this paper, we present a method for adversarial decomposition of text...

Alexey Romanov, et al. ∙

research

∙ 09/12/2018

Semantic WordRank: Generating Finer Single-Document Summarizations

We present Semantic WordRank (SWR), an unsupervised method for generatin...

Hao Zhang, et al. ∙

research

∙ 09/18/2018

Triad-based Neural Network for Coreference Resolution

We propose a triad-based neural network system that generates affinity s...

Yuanliang Meng, et al. ∙

research

∙ 10/11/2018

Adversarial Text Generation Without Reinforcement Learning

Generative Adversarial Networks (GANs) have experienced a recent surge i...

David Donahue, et al. ∙

research

∙ 03/06/2019

Evolutionary Cell Aided Design for Neural Network Architectures

Mathematical theory shows us that multilayer feedforward Artificial Neur...

Philip Colangelo, et al. ∙

research

∙ 03/27/2019

Skill Acquisition via Automated Multi-Coordinate Cost Balancing

We propose a learning framework, named Multi-Coordinate Cost Balancing (...

Harish Ravichandar, et al. ∙

research

∙ 08/21/2019

Revealing the Dark Secrets of BERT

BERT-based architectures currently give state-of-the-art performance on ...

Olga Kovaleva, et al. ∙

research

∙ 08/26/2019

A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing

We are interested in the optimal scheduling of a collection of multi-com...

Zhi Cao, et al. ∙

research

∙ 08/28/2019

Solving Math Word Problems with Double-Decoder Transformer

This paper proposes a Transformer-based model to generate equations for ...

Yuanliang Meng, et al. ∙

research

∙ 08/29/2019

3D Anchor-Free Lesion Detector on Computed Tomography Scans

Lesions are injuries and abnormal tissues in the human body. Detecting l...

Ning Zhang, et al. ∙

research

∙ 08/29/2019

NarrativeTime: Dense High-Speed Temporal Annotation on a Timeline

We present NarrativeTime, a new timeline-based annotation scheme for tem...

Anna Rogers, et al. ∙

research

∙ 09/05/2019

AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is...

Dechun Wang, et al. ∙

research

∙ 11/19/2019

Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features

Diabetic retinopathy (DR) is a diabetes complication that affects eyes. ...

Qilei Chen, et al. ∙

research

∙ 11/25/2019

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Automated ICD coding, which assigns the International Classification of ...

Fei Li, et al. ∙

research

∙ 12/11/2019

MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

Manipulating training data leads to robust neural models for MT....

Rumeng Li, et al. ∙

research

∙ 02/27/2020

A Primer in BERTology: What we know about how BERT works

Transformer-based models are now widely used in NLP, but we still do not...

Anna Rogers, et al. ∙

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