Penn State University

research

∙ 01/03/2020

A Robot that Learns Connect Four Using Game Theory and Demonstrations

Teaching robots new skills using minimal time and effort has long been a...

Ali Ayub, et al. ∙

research

∙ 02/10/2022

Using Navigational Information to Learn Visual Representations

Children learn to build a visual representation of the world from unsupe...

Lizhen Zhu, et al. ∙

research

∙ 05/24/2020

Longitudinal Deep Kernel Gaussian Process Regression

We consider the problem of learning predictive models from longitudinal ...

Junjie Liang, et al. ∙

research

∙ 06/17/2020

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

The success of deep learning notoriously requires larger amounts of cost...

Wei Jin, et al. ∙

research

∙ 08/07/2021

Jointly Attacking Graph Neural Network and its Explanations

Graph Neural Networks (GNNs) have boosted the performance for many graph...

Wenqi Fan, et al. ∙

research

∙ 06/24/2020

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...

Weilin Cong, et al. ∙

research

∙ 11/11/2019

LMLFM: Longitudinal Multi-Level Factorization Machines

Selecting important variables and learning predictive models from high-d...

Junjie Liang, et al. ∙

research

∙ 09/09/2021

FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

Federated Semi-Supervised Learning (FedSSL) has gained rising attention ...

Zewei Long, et al. ∙

research

∙ 08/30/2019

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

Popular culture has contemplated societies of thinking machines for gene...

Dan Boneh, et al. ∙

research

∙ 10/12/2021

On the Security Risks of AutoML

Neural Architecture Search (NAS) represents an emerging machine learning...

Ren Pang, et al. ∙

research

∙ 01/24/2019

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

Spatial-temporal prediction is a fundamental problem for constructing sm...

Huaxiu Yao, et al. ∙

research

∙ 11/23/2019

Constrained Linear Data-feature Mapping for Image Classification

In this paper, we propose a constrained linear data-feature mapping mode...

Juncai He, et al. ∙

research

∙ 07/26/2020

Don't Overlook the Support Set: Towards Improving Generalization in Meta-learning

Meta-learning has proven to be a powerful paradigm for transferring the ...

Huaxiu Yao, et al. ∙

research

∙ 12/09/2020

Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews

Objective: Systematic reviews of scholarly documents often provide compl...

Xiaoxiao Li, et al. ∙

research

∙ 09/24/2021

DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning

In an emergency room (ER) setting, the diagnosis of stroke is a common c...

Tongan Cai, et al. ∙

research

∙ 09/08/2021

AppQ: Warm-starting App Recommendation Based on View Graphs

Current app ranking and recommendation systems are mainly based on user-...

Dan Su, et al. ∙

research

∙ 10/14/2021

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

This paper investigates unsupervised learning of Full-Waveform Inversion...

Peng Jin, et al. ∙

research

∙ 04/18/2022

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Graph Neural Networks (GNNs) have made rapid developments in the recent ...

Enyan Dai, et al. ∙

research

∙ 11/24/2019

Algorithmic Bias in Recidivism Prediction: A Causal Perspective

ProPublica's analysis of recidivism predictions produced by Correctional...

Aria Khademi, et al. ∙

research

∙ 09/01/2021

Approximation Properties of Deep ReLU CNNs

This paper is devoted to establishing L^2 approximation properties for d...

Juncai He, et al. ∙

research

∙ 10/26/2021

Meta-learning with an Adaptive Task Scheduler

To benefit the learning of a new task, meta-learning has been proposed t...

Huaxiu Yao, et al. ∙

research

∙ 04/05/2022

SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering

While Visual Question Answering (VQA) has progressed rapidly, previous w...

Vipul Gupta, et al. ∙

research

∙ 11/30/2018

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...

Savinay Nagendra, et al. ∙

research

∙ 12/10/2018

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...

Junjie Liang, et al. ∙

research

∙ 02/09/2019

An Algorithm Unrolling Approach to Deep Blind Image Deblurring

Blind image deblurring remains a topic of enduring interest. Learning ba...

Yuelong Li, et al. ∙

research

∙ 07/11/2019

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

In this paper, we propose and study a Nyström based approach to efficien...

Nicholas Sterge, et al. ∙

research

∙ 09/07/2019

Transferable Neural Processes for Hyperparameter Optimization

Automated machine learning aims to automate the whole process of machine...

Ying Wei, et al. ∙

research

∙ 09/25/2019

The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario

The Dynamical Gaussian Process Latent Variable Models provide an elegant...

Thanh Le, et al. ∙

research

∙ 01/02/2020

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...

Jesse Scott, et al. ∙

research

∙ 10/22/2020

UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data

Successful health risk prediction demands accuracy and reliability of th...

Chacha Chen, et al. ∙

research

∙ 11/11/2021

Fair AutoML

We present an end-to-end automated machine learning system to find machi...

Qingyun Wu, et al. ∙

research

∙ 11/19/2021

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespre...

Weilin Cong, et al. ∙

research

∙ 03/15/2022

Machine Learning and Cosmology

Methods based on machine learning have recently made substantial inroads...

Cora Dvorkin, et al. ∙

research

∙ 05/06/2022

Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting

Crowd Counting has important applications in public safety and pandemic ...

Jiaqi Gao, et al. ∙

research

∙ 07/09/2021

Ill-posed Surface Emissivity Retrieval from Multi-Geometry Hyperspectral Images using a Hybrid Deep Neural Network

Atmospheric correction is a fundamental task in remote sensing because o...

Fangcao Xu, et al. ∙

research

∙ 01/04/2022

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

Deep neural networks (DNNs) are threatened by adversarial examples. Adve...

Hui Liu, et al. ∙

research

∙ 05/01/2022

Adversarial Plannning

Planning algorithms are used in computational systems to direct autonomo...

Valentin Vie, 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

∙ 02/16/2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data t...

Enyan Dai, et al. ∙

research

∙ 03/05/2022

Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers

River bathymetry is critical for many aspects of water resources managem...

Xiaofeng Liu, et al. ∙

research

∙ 04/29/2022

Designing for Responsible Trust in AI Systems: A Communication Perspective

Current literature and public discourse on "trust in AI" are often focus...

Q Vera Liao, et al. ∙

research

∙ 11/07/2018

PaDNet: Pan-Density Crowd Counting

Crowd counting in varying density scenes is a challenging problem in art...

Yukun Tian, et al. ∙

research

∙ 11/20/2018

Scene Graph Generation via Conditional Random Fields

Despite the great success object detection and segmentation models have ...

Weilin Cong, et al. ∙

research

∙ 07/24/2019

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...

Jacopo Di Iorio, et al. ∙

research

∙ 01/03/2020

Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, me...

Huaxiu Yao, et al. ∙

research

∙ 03/29/2020

Superpixel Segmentation with Fully Convolutional Networks

In computer vision, superpixels have been widely used as an effective wa...

Fengting Yang, et al. ∙

research

∙ 10/15/2021

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Various pruning approaches have been proposed to reduce the footprint re...

Shaoyi Huang, et al. ∙

research

∙ 01/13/2022

S^2FPR: Crowd Counting via Self-Supervised Coarse to Fine Feature Pyramid Ranking

Most conventional crowd counting methods utilize a fully-supervised lear...

Jiaqi Gao, et al. ∙

research

∙ 01/29/2022

AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

Knowledge distillation (KD) methods compress large models into smaller s...

Dongkuan Xu, et al. ∙

research

∙ 05/20/2020

Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representation learni...

Wei Jin, et al. ∙

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