Penn State University

∙ State College , United States ∙ 0 followers

The Pennsylvania State University is a state-related, land-grant, doctoral university with campuses and facilities throughout Pennsylvania.

A Robot that Learns Connect Four Using Game Theory and Demonstrations

Teaching robots new skills using minimal time and effort has long been a...
01/03/2020 ∙ by Ali Ayub, et al. ∙ 71 ∙ share

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Longitudinal Deep Kernel Gaussian Process Regression

We consider the problem of learning predictive models from longitudinal ...
05/24/2020 ∙ by Junjie Liang, et al. ∙ 26 ∙ share

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Self-supervised Learning on Graphs: Deep Insights and New Direction

The success of deep learning notoriously requires larger amounts of cost...
06/17/2020 ∙ by Wei Jin, et al. ∙ 24 ∙ share

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Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks

Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become a...
06/24/2020 ∙ by Weilin Cong, et al. ∙ 22 ∙ share

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LMLFM: Longitudinal Multi-Level Factorization Machines

Selecting important variables and learning predictive models from high-d...
11/11/2019 ∙ by Junjie Liang, et al. ∙ 19 ∙ share

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How Relevant is the Turing Test in the Age of Sophisbots?

Popular culture has contemplated societies of thinking machines for gene...
08/30/2019 ∙ by Dan Boneh, et al. ∙ 17 ∙ share

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Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Spatial-temporal prediction is a fundamental problem for constructing sm...
01/24/2019 ∙ by Huaxiu Yao, et al. ∙ 16 ∙ share

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Constrained Linear Data-feature Mapping for Image Classification

In this paper, we propose a constrained linear data-feature mapping mode...
11/23/2019 ∙ by Juncai He, et al. ∙ 13 ∙ share

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Algorithmic Bias in Recidivism Prediction: A Causal Perspective

ProPublica's analysis of recidivism predictions produced by Correctional...
11/24/2019 ∙ by Aria Khademi, et al. ∙ 11 ∙ share

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Foot Pressure from Video: A Deep Learning Approach to Predict Dynamics from Kinematics

Human gait stability analysis is a key to understanding locomotion and c...
11/30/2018 ∙ by Savinay Nagendra, et al. ∙ 10 ∙ share

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Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank mode...
12/10/2018 ∙ by Junjie Liang, et al. ∙ 10 ∙ share

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An Algorithm Unrolling Approach to Deep Blind Image Deblurring

Blind image deblurring remains a topic of enduring interest. Learning ba...
02/09/2019 ∙ by Yuelong Li, et al. ∙ 10 ∙ share

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Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling

In this paper, we propose and study a Nyström based approach to efficien...
07/11/2019 ∙ by Nicholas Sterge, et al. ∙ 10 ∙ share

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Transferable Neural Processes for Hyperparameter Optimization

Automated machine learning aims to automate the whole process of machine...
09/07/2019 ∙ by Ying Wei, et al. ∙ 10 ∙ share

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The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario

The Dynamical Gaussian Process Latent Variable Models provide an elegant...
09/25/2019 ∙ by Thanh Le, et al. ∙ 10 ∙ share

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From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion

To gain an understanding of the relation between a given human pose imag...
01/02/2020 ∙ by Jesse Scott, et al. ∙ 10 ∙ 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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PaDNet: Pan-Density Crowd Counting

Crowd counting in varying density scenes is a challenging problem in art...
11/07/2018 ∙ by Yukun Tian, et al. ∙ 6 ∙ share

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Scene Graph Generation via Conditional Random Fields

Despite the great success object detection and segmentation models have ...
11/20/2018 ∙ by Weilin Cong, et al. ∙ 6 ∙ share

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On the bias of H-scores for comparing biclusters, and how to correct it

In the last two decades several biclustering methods have been developed...
07/24/2019 ∙ by Jacopo Di Iorio, et al. ∙ 6 ∙ share

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Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, me...
01/03/2020 ∙ by Huaxiu Yao, et al. ∙ 6 ∙ share

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Superpixel Segmentation with Fully Convolutional Networks

In computer vision, superpixels have been widely used as an effective wa...
03/29/2020 ∙ by Fengting Yang, et al. ∙ 6 ∙ share

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Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representation learni...
05/20/2020 ∙ by Wei Jin, et al. ∙ 5 ∙ share

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Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding

Single-image piece-wise planar 3D reconstruction aims to simultaneously ...
02/26/2019 ∙ by Zehao Yu, et al. ∙ 4 ∙ share

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Node Injection Attacks on Graphs via Reinforcement Learning

Real-world graph applications, such as advertisements and product recomm...
09/14/2019 ∙ by Yiwei Sun, et al. ∙ 4 ∙ share

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No Peeking through My Windows: Conserving Privacy in Personal Drones

The drone technology has been increasingly used by many tech-savvy consu...
08/26/2019 ∙ by Alem Fitwi, et al. ∙ 3 ∙ share

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RISE Video Dataset: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. P...
05/13/2020 ∙ by Yen-Chia Hsu, et al. ∙ 3 ∙ share

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A Neural Temporal Model for Human Motion Prediction

We propose novel neural temporal models for short-term motion prediction...
09/09/2018 ∙ by Anand Gopalakrishnan, et al. ∙ 2 ∙ share

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Improving Image Captioning by Leveraging Knowledge Graphs

We explore the use of a knowledge graphs, that capture general or common...
01/25/2019 ∙ by Yimin Zhou, et al. ∙ 2 ∙ share

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Model-free Feature Screening and FDR Control with Knockoff Features

This paper proposes a model-free and data-adaptive feature screening met...
08/19/2019 ∙ by Wanjun Liu, et al. ∙ 1 ∙ share

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Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification

Recently, a special type of data poisoning (DP) attack targeting Deep Ne...
08/27/2019 ∙ by Zhen Xiang, 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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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Many machine learning models are vulnerable to adversarial examples: inp...
05/24/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Practical Black-Box Attacks against Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
02/08/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Some machine learning applications involve training data that is sensiti...
10/18/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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cleverhans v2.0.0: an adversarial machine learning library

cleverhans is a software library that provides standardized reference im...
10/03/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Deep neural networks, like many other machine learning models, have rece...
06/14/2016 ∙ by Kathrin Grosse, et al. ∙ 0 ∙ share

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Practical Fine-grained Privilege Separation in Multithreaded Applications

An inherent security limitation with the classic multithreaded programmi...
05/12/2013 ∙ by Jun Wang, et al. ∙ 0 ∙ share

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TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZone

The rapid evolution of Internet-of-Things (IoT) technologies has led to ...
04/19/2017 ∙ by Le Guan, et al. ∙ 0 ∙ share

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Blind Image Deblurring Using Row-Column Sparse Representations

Blind image deblurring is a particularly challenging inverse problem whe...
12/05/2017 ∙ by Mohammad Tofighi, et al. ∙ 0 ∙ share

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Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data

This paper presents a technique for reduced-order Markov modeling for co...
09/26/2017 ∙ by Devesh K Jha, et al. ∙ 0 ∙ share

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Characteristic and Universal Tensor Product Kernels

Kernel mean embeddings provide a versatile and powerful nonparametric re...
08/28/2017 ∙ by Zoltan Szabo, et al. ∙ 0 ∙ share

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Crafting Adversarial Input Sequences for Recurrent Neural Networks

Machine learning models are frequently used to solve complex security pr...
04/28/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

The Soil Moisture Active Passive (SMAP) mission has delivered valuable s...
07/20/2017 ∙ by Kuai Fang, et al. ∙ 0 ∙ share

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The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally effi...
11/24/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on ma...
11/14/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Dimension of Marginals of Kronecker Product Models

A Kronecker product model is the set of visible marginal probability dis...
11/10/2015 ∙ by Guido Montufar, et al. ∙ 0 ∙ share

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Statistical Consistency of Kernel PCA with Random Features

Kernel methods are powerful learning methodologies that provide a simple...
06/20/2017 ∙ by Bharath Sriperumbudur, et al. ∙ 0 ∙ share

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Information, Privacy and Stability in Adaptive Data Analysis

Traditional statistical theory assumes that the analysis to be performed...
06/02/2017 ∙ by Adam Smith, et al. ∙ 0 ∙ share

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