Rochester Institute of Technology

∙ Rochester , United States ∙ 0 followers

Rochester Institute of Technology is a private doctoral university within the town of Henrietta in the Rochester, New York metropolitan area.

REMIND Your Neural Network to Prevent Catastrophic Forgetting

In lifelong machine learning, a robotic agent must be incrementally upda...
10/06/2019 ∙ by Tyler L. Hayes, et al. ∙ 34 ∙ share

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UniPose: Unified Human Pose Estimation in Single Images and Videos

We propose UniPose, a unified framework for human pose estimation, based...
01/22/2020 ∙ by Bruno Artacho, et al. ∙ 24 ∙ share

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Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

We propose a new efficient architecture for semantic segmentation, based...
12/06/2019 ∙ by Bruno Artacho, et al. ∙ 22 ∙ share

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Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis

When a robot acquires new information, ideally it would immediately be c...
09/04/2019 ∙ by Tyler L. Hayes, et al. ∙ 19 ∙ share

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Neighborhood-based Pooling for Population-level Label Distribution Learning

Supervised machine learning often requires human-annotated data. While a...
03/16/2020 ∙ by Tharindu Cyril Weerasooriya, et al. ∙ 15 ∙ share

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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...
02/25/2020 ∙ by Sandesh Ghimire, et al. ∙ 14 ∙ share

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Online Learning of Recurrent Neural Architectures by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be qui...
10/17/2018 ∙ by Alexander Ororbia, et al. ∙ 8 ∙ share

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

In lifelong learning systems, especially those based on artificial neura...
05/25/2019 ∙ by Alexander Ororbia, et al. ∙ 7 ∙ share

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A negative case analysis of visual grounding methods for VQA

Existing Visual Question Answering (VQA) methods tend to exploit dataset...
04/12/2020 ∙ by Robik Shrestha, et al. ∙ 7 ∙ share

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Answer Them All! Toward Universal Visual Question Answering Models

Visual Question Answering (VQA) research is split into two camps: the fi...
03/01/2019 ∙ by Robik Shrestha, et al. ∙ 6 ∙ share

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VQD: Visual Query Detection in Natural Scenes

We propose Visual Query Detection (VQD), a new visual grounding task. In...
04/04/2019 ∙ by Manoj Acharya, et al. ∙ 6 ∙ share

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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...
09/16/2019 ∙ by Rayan Mosli, et al. ∙ 5 ∙ share

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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...
03/10/2020 ∙ by Shan Lin, et al. ∙ 5 ∙ share

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Memory Efficient Experience Replay for Streaming Learning

In supervised machine learning, an agent is typically trained once and t...
09/16/2018 ∙ by Tyler L. Hayes, et al. ∙ 4 ∙ share

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TallyQA: Answering Complex Counting Questions

Most counting questions in visual question answering (VQA) datasets are ...
10/29/2018 ∙ by Manoj Acharya, et al. ∙ 4 ∙ share

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Recognizing American Sign Language Manual Signs from RGB-D Videos

In this paper, we propose a 3D Convolutional Neural Network (3DCNN) base...
06/07/2019 ∙ by Longlong Jing, et al. ∙ 4 ∙ share

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Rethinking Continual Learning for Autonomous Agents and Robots

Continual learning refers to the ability of a biological or artificial s...
07/02/2019 ∙ by German I. Parisi, et al. ∙ 4 ∙ share

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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...
08/03/2019 ∙ by Christopher Sweet, et al. ∙ 1 ∙ share

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Answering Questions about Data Visualizations using Efficient Bimodal Fusion

Chart question answering (CQA) is a newly proposed visual question answe...
08/05/2019 ∙ by Kushal Kafle, et al. ∙ 1 ∙ share

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The Neural State Pushdown Automata

In order to learn complex grammars, recurrent neural networks (RNNs) req...
09/07/2019 ∙ by Ankur Mali, et al. ∙ 1 ∙ share

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

Training deep neural networks on large-scale datasets requires significa...
02/10/2020 ∙ by Alexander Ororbia, et al. ∙ 1 ∙ share

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FearNet: Brain-Inspired Model for Incremental Learning

Incremental class learning involves sequentially learning classes in bur...
11/28/2017 ∙ by Ronald Kemker, et al. ∙ 0 ∙ share

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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...
11/17/2017 ∙ by David E. Narváez, et al. ∙ 0 ∙ share

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

We study rank-1 L1-norm-based TUCKER2 (L1-TUCKER2) decomposition of 3-wa...
10/31/2017 ∙ by Panos P. Markopoulos, et al. ∙ 0 ∙ share

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

Functional connectivity analysis yields powerful insights into our under...
11/21/2016 ∙ by Nathan D. Cahill, et al. ∙ 0 ∙ share

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Measuring Catastrophic Forgetting in Neural Networks

Deep neural networks are used in many state-of-the-art systems for machi...
08/07/2017 ∙ by Ronald Kemker, et al. ∙ 0 ∙ share

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

Deep convolutional neural networks (DCNNs) have been used to achieve sta...
03/19/2017 ∙ by Ronald Kemker, et al. ∙ 0 ∙ share

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Efficient L1-Norm Principal-Component Analysis via Bit Flipping

It was shown recently that the K L1-norm principal components (L1-PCs) o...
10/06/2016 ∙ by Panos P. Markopoulos, et al. ∙ 0 ∙ share

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Aerial Spectral Super-Resolution using Conditional Adversarial Networks

Inferring spectral signatures from ground based natural images has acqui...
12/23/2017 ∙ by Aneesh Rangnekar, et al. ∙ 0 ∙ share

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Unsupervised Learning in Neuromemristive Systems

Neuromemristive systems (NMSs) currently represent the most promising pl...
01/27/2016 ∙ by Cory Merkel, et al. ∙ 0 ∙ share

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Multitask Learning of Vegetation Biochemistry from Hyperspectral Data

Statistical models have been successful in accurately estimating the bio...
10/22/2016 ∙ by Utsav B. Gewali, et al. ∙ 0 ∙ share

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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...
10/22/2016 ∙ by Utsav B. Gewali, et al. ∙ 0 ∙ share

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On the Predictive Properties of Binary Link Functions

This paper provides a theoretical and computational justification of the...
02/16/2015 ∙ by Necla Gunduz, et al. ∙ 0 ∙ share

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Random Subspace Learning Approach to High-Dimensional Outliers Detection

We introduce and develop a novel approach to outlier detection based on ...
02/16/2015 ∙ by Bohan Liu, et al. ∙ 0 ∙ share

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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, ...
01/03/2015 ∙ by Ernest Fokoué, et al. ∙ 0 ∙ share

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Probit Normal Correlated Topic Models

The logistic normal distribution has recently been adapted via the trans...
10/03/2014 ∙ by Xingchen Yu, et al. ∙ 0 ∙ share

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The Complexity of Manipulating k-Approval Elections

An important problem in computational social choice theory is the comple...
05/23/2010 ∙ by Andrew Lin, et al. ∙ 0 ∙ share

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

We have developed (1) a graph visualization system that allows users to ...
06/01/2006 ∙ by Andrew Pavlo, et al. ∙ 0 ∙ share

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Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball

A common problem faced in statistical inference is drawing conclusions f...
12/16/2017 ∙ by Gabriel C. Phelan, et al. ∙ 0 ∙ share

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Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling

Hierarchical Temporal Memory (HTM) is a biomimetic machine learning algo...
11/09/2016 ∙ by Lennard Streat, et al. ∙ 0 ∙ share

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Hierarchical Memory Management for Mutable State

It is well known that modern functional programming languages are natura...
01/14/2018 ∙ by Adrien Guatto, et al. ∙ 0 ∙ share

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EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking

Computer vision technologies are very attractive for practical applicati...
01/20/2018 ∙ by Burak Uzkent, et al. ∙ 0 ∙ share

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A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis

Undirected graphical models have been successfully used to jointly model...
01/25/2018 ∙ by Utsav B. Gewali, et al. ∙ 0 ∙ share

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A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts

Motivated by a project to create a system for people who are deaf or har...
01/29/2018 ∙ by Sushant Kafle, et al. ∙ 0 ∙ share

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On the Statistical Challenges of Echo State Networks and Some Potential Remedies

Echo state networks are powerful recurrent neural networks. However, the...
02/20/2018 ∙ by Qiuyi Wu, et al. ∙ 0 ∙ share

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Compassionately Conservative Balanced Cuts for Image Segmentation

The Normalized Cut (NCut) objective function, widely used in data cluste...
03/27/2018 ∙ by Nathan D. Cahill, et al. ∙ 0 ∙ share

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Cache-aware data structures for packet forwarding tables on general purpose CPUs

Longest prefix matching has long been the bottleneck of the Bloom filter...
04/24/2018 ∙ by Maksim Yegorov, et al. ∙ 0 ∙ share

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A Survey on Consensus Mechanisms and Mining Management in Blockchain Networks

The past decade has witnessed the rapid evolution in blockchain technolo...
05/07/2018 ∙ by Wenbo Wang, et al. ∙ 0 ∙ share

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Characterizing the Temporal Dynamics of Information in Visually Guided Predictive Control Using LSTM Recurrent Neural Networks

Theories for visually guided action account for online control in the pr...
05/15/2018 ∙ by Kamran Binaee, et al. ∙ 0 ∙ share

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Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit

Performing the inference step of deep learning in resource constrained e...
05/22/2018 ∙ by Seyed H. F. Langroudi, et al. ∙ 0 ∙ share

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Rochester Institute of Technology

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