Rochester Institute of Technology

research

∙ 10/06/2019

REMIND Your Neural Network to Prevent Catastrophic Forgetting

In lifelong machine learning, a robotic agent must be incrementally upda...

Tyler L. Hayes, et al. ∙

research

∙ 09/16/2021

A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

Medical image segmentation has significantly benefitted thanks to deep l...

S. M. Kamrul Hasan, et al. ∙

research

∙ 01/22/2020

UniPose: Unified Human Pose Estimation in Single Images and Videos

We propose UniPose, a unified framework for human pose estimation, based...

Bruno Artacho, et al. ∙

research

∙ 10/16/2020

Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction

Predicting protein-protein interactions (PPIs) by learning informative r...

Kishan KC, et al. ∙

research

∙ 12/06/2019

Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

We propose a new efficient architecture for semantic segmentation, based...

Bruno Artacho, et al. ∙

research

∙ 09/04/2019

Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis

When a robot acquires new information, ideally it would immediately be c...

Tyler L. Hayes, et al. ∙

research

∙ 10/13/2021

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

Probabilistic estimation of cardiac electrophysiological model parameter...

Md Shakil Zaman, et al. ∙

research

∙ 02/01/2022

WebFormer: The Web-page Transformer for Structure Information Extraction

Structure information extraction refers to the task of extracting struct...

Qifan Wang, et al. ∙

research

∙ 03/16/2020

Neighborhood-based Pooling for Population-level Label Distribution Learning

Supervised machine learning often requires human-annotated data. While a...

Tharindu Cyril Weerasooriya, et al. ∙

research

∙ 02/11/2022

Detecting out-of-context objects using contextual cues

This paper presents an approach to detect out-of-context (OOC) objects i...

Manoj Acharya, et al. ∙

research

∙ 02/25/2020

Reliable Estimation of Kullback-Leibler Divergence by Controlling Discriminator Complexity in the Reproducing Kernel Hilbert Space

Several scalable methods to compute the Kullback Leibler (KL) divergence...

Sandesh Ghimire, et al. ∙

research

∙ 08/29/2021

MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification

Recent research in disaster informatics demonstrates a practical and imp...

Firoj Alam, et al. ∙

research

∙ 10/17/2018

Online Learning of Recurrent Neural Architectures by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be qui...

Alexander Ororbia, et al. ∙

research

∙ 10/16/2020

Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

Biomedical interaction networks have incredible potential to be useful i...

Kishan KC, et al. ∙

research

∙ 05/25/2019

Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting when Learning Cumulatively

In lifelong learning systems, especially those based on artificial neura...

Alexander Ororbia, et al. ∙

research

∙ 04/12/2020

A negative case analysis of visual grounding methods for VQA

Existing Visual Question Answering (VQA) methods tend to exploit dataset...

Robik Shrestha, et al. ∙

research

∙ 03/01/2019

Answer Them All! Toward Universal Visual Question Answering Models

Visual Question Answering (VQA) research is split into two camps: the fi...

Robik Shrestha, et al. ∙

research

∙ 04/04/2019

VQD: Visual Query Detection in Natural Scenes

We propose Visual Query Detection (VQD), a new visual grounding task. In...

Manoj Acharya, et al. ∙

research

∙ 09/16/2019

They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization

Machine learning models have been found to be susceptible to adversarial...

Rayan Mosli, et al. ∙

research

∙ 03/10/2020

LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images

The intelligent perception of endoscopic vision is appealing in many com...

Shan Lin, et al. ∙

research

∙ 06/05/2020

RIT-Eyes: Rendering of near-eye images for eye-tracking applications

Deep neural networks for video-based eye tracking have demonstrated resi...

Nitinraj Nair, et al. ∙

research

∙ 03/05/2022

Deep Partial Multiplex Network Embedding

Network embedding is an effective technique to learn the low-dimensional...

Qifan Wang, et al. ∙

research

∙ 09/16/2018

Memory Efficient Experience Replay for Streaming Learning

In supervised machine learning, an agent is typically trained once and t...

Tyler L. Hayes, et al. ∙

research

∙ 10/29/2018

TallyQA: Answering Complex Counting Questions

Most counting questions in visual question answering (VQA) datasets are ...

Manoj Acharya, et al. ∙

research

∙ 06/07/2019

Recognizing American Sign Language Manual Signs from RGB-D Videos

In this paper, we propose a 3D Convolutional Neural Network (3DCNN) base...

Longlong Jing, et al. ∙

research

∙ 07/02/2019

Rethinking Continual Learning for Autonomous Agents and Robots

Continual learning refers to the ability of a biological or artificial s...

German I. Parisi, et al. ∙

research

∙ 05/22/2022

GL-RG: Global-Local Representation Granularity for Video Captioning

Video captioning is a challenging task as it needs to accurately transfo...

Liqi Yan, et al. ∙

research

∙ 08/03/2019

On the Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks

Recreating cyber-attack alert data with a high level of fidelity is chal...

Christopher Sweet, et al. ∙

research

∙ 08/05/2019

Answering Questions about Data Visualizations using Efficient Bimodal Fusion

Chart question answering (CQA) is a newly proposed visual question answe...

Kushal Kafle, et al. ∙

research

∙ 09/07/2019

The Neural State Pushdown Automata

In order to learn complex grammars, recurrent neural networks (RNNs) req...

Ankur Mali, et al. ∙

research

∙ 02/10/2020

Reducing the Computational Burden of Deep Learning with Recursive Local Representation Alignment

Training deep neural networks on large-scale datasets requires significa...

Alexander Ororbia, et al. ∙

research

∙ 11/28/2017

FearNet: Brain-Inspired Model for Incremental Learning

Incremental class learning involves sequentially learning classes in bur...

Ronald Kemker, et al. ∙

research

∙ 11/17/2017

Exploring the Use of Shatter for AllSAT Through Ramsey-Type Problems

In the context of SAT solvers, Shatter is a popular tool for symmetry br...

David E. Narváez, et al. ∙

research

∙ 10/31/2017

The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition

We study rank-1 L1-norm-based TUCKER2 (L1-TUCKER2) decomposition of 3-wa...

Panos P. Markopoulos, et al. ∙

research

∙ 11/21/2016

Multiple-View Spectral Clustering for Group-wise Functional Community Detection

Functional connectivity analysis yields powerful insights into our under...

Nathan D. Cahill, et al. ∙

research

∙ 08/07/2017

Measuring Catastrophic Forgetting in Neural Networks

Deep neural networks are used in many state-of-the-art systems for machi...

Ronald Kemker, et al. ∙

research

∙ 03/19/2017

Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning

Deep convolutional neural networks (DCNNs) have been used to achieve sta...

Ronald Kemker, et al. ∙

research

∙ 10/06/2016

Efficient L1-Norm Principal-Component Analysis via Bit Flipping

It was shown recently that the K L1-norm principal components (L1-PCs) o...

Panos P. Markopoulos, et al. ∙

research

∙ 12/23/2017

Aerial Spectral Super-Resolution using Conditional Adversarial Networks

Inferring spectral signatures from ground based natural images has acqui...

Aneesh Rangnekar, et al. ∙

research

∙ 01/27/2016

Unsupervised Learning in Neuromemristive Systems

Neuromemristive systems (NMSs) currently represent the most promising pl...

Cory Merkel, et al. ∙

research

∙ 10/22/2016

Multitask Learning of Vegetation Biochemistry from Hyperspectral Data

Statistical models have been successful in accurately estimating the bio...

Utsav B. Gewali, et al. ∙

research

∙ 10/22/2016

Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields

In this paper, we propose and compare two spectral angle based approache...

Utsav B. Gewali, et al. ∙

research

∙ 02/16/2015

On the Predictive Properties of Binary Link Functions

This paper provides a theoretical and computational justification of the...

Necla Gunduz, et al. ∙

research

∙ 02/16/2015

Random Subspace Learning Approach to High-Dimensional Outliers Detection

We introduce and develop a novel approach to outlier detection based on ...

Bohan Liu, et al. ∙

research

∙ 01/03/2015

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, ...

Ernest Fokoué, et al. ∙

research

∙ 10/03/2014

Probit Normal Correlated Topic Models

The logistic normal distribution has recently been adapted via the trans...

Xingchen Yu, et al. ∙

research

∙ 05/23/2010

The Complexity of Manipulating k-Approval Elections

An important problem in computational social choice theory is the comple...

Andrew Lin, et al. ∙

research

∙ 06/01/2006

A parent-centered radial layout algorithm for interactive graph visualization and animation

We have developed (1) a graph visualization system that allows users to ...

Andrew Pavlo, et al. ∙

research

∙ 12/16/2017

Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball

A common problem faced in statistical inference is drawing conclusions f...

Gabriel C. Phelan, et al. ∙

research

∙ 11/09/2016

Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling

Hierarchical Temporal Memory (HTM) is a biomimetic machine learning algo...

Lennard Streat, et al. ∙

Rochester Institute of Technology

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