University of Pennsylvania

∙ Philadelphia , United States ∙ 0 followers

The University of Pennsylvania is a private Ivy League research university located in the University City neighborhood of Philadelphia, Pennsylvania.

Featured Researchers

Yu Chen
108 publications
Alejandro Ribeiro
95 publications
Dan Roth
94 publications
Hao Wu
79 publications
Ufuk Topcu
74 publications
Xiao Zhang
60 publications
Vijay Kumar
59 publications
Kostas Daniilidis
57 publications
Rui Li
57 publications
Jiaming Xu
56 publications
Sham M. Kakade
55 publications

Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers

Understanding the strengths and weaknesses of machine learning (ML) algo...
07/14/2021 ∙ by Patryk Orzechowski, et al. ∙ 51 ∙ share

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Sample Complexity of Kalman Filtering for Unknown Systems

In this paper, we consider the task of designing a Kalman Filter (KF) fo...
12/27/2019 ∙ by Anastasios Tsiamis, et al. ∙ 41 ∙ share

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SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Semantic GP is a promising approach that introduces semantic awareness d...
01/30/2020 ∙ by Stefano Ruberto, et al. ∙ 31 ∙ share

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EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...
01/21/2020 ∙ by Elvin Isufi, et al. ∙ 29 ∙ share

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Finite Sample Analysis of Stochastic System Identification

In this paper, we analyze the finite sample complexity of stochastic sys...
03/21/2019 ∙ by Anastasios Tsiamis, et al. ∙ 26 ∙ share

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Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis

For recovering 3D object poses from 2D images, a prevalent method is to ...
12/29/2018 ∙ by Jianqiao Wangni, et al. ∙ 24 ∙ share

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Optimal Algorithms for Submodular Maximization with Distributed Constraints

We consider a class of discrete optimization problems that aim to maximi...
09/30/2019 ∙ by Alexander Robey, et al. ∙ 22 ∙ share

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Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...
05/02/2020 ∙ by Sanjay Subramanian, et al. ∙ 22 ∙ share

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Continual Learning for Sentence Representations Using Conceptors

Distributed representations of sentences have become ubiquitous in natur...
04/18/2019 ∙ by Tianlin Liu, et al. ∙ 20 ∙ share

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Foreshadowing the Benefits of Incidental Supervision

Learning theory mostly addresses the standard learning paradigm, assumin...
06/09/2020 ∙ by Hangfeng He, et al. ∙ 19 ∙ share

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Genetic programming approaches to learning fair classifiers

Society has come to rely on algorithms like classifiers for important de...
04/28/2020 ∙ by William La Cava, et al. ∙ 18 ∙ share

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Minibatch Processing in Spiking Neural Networks

Spiking neural networks (SNNs) are a promising candidate for biologicall...
09/05/2019 ∙ by Daniel J. Saunders, et al. ∙ 17 ∙ share

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Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

Driven by a wide range of applications, many principal subspace estimati...
02/18/2020 ∙ by T. Tony Cai, et al. ∙ 17 ∙ share

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EventGAN: Leveraging Large Scale Image Datasets for Event Cameras

Event cameras provide a number of benefits over traditional cameras, suc...
12/03/2019 ∙ by Alex Zihao Zhu, et al. ∙ 16 ∙ share

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Safe Predictors for Enforcing Input-Output Specifications

We present an approach for designing correct-by-construction neural netw...
01/29/2020 ∙ by Stephen Mell, et al. ∙ 16 ∙ share

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TransOMCS: From Linguistic Graphs to Commonsense Knowledge

Commonsense knowledge acquisition is a key problem for artificial intell...
05/01/2020 ∙ by Hongming Zhang, et al. ∙ 16 ∙ share

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Automating Artifact Detection in Video Games

In spite of advances in gaming hardware and software, gameplay is often ...
11/30/2020 ∙ by Parmida Davarmanesh, et al. ∙ 15 ∙ share

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Goal-Oriented Script Construction

The knowledge of scripts, common chains of events in stereotypical scena...
07/28/2021 ∙ by Qing Lyu, et al. ∙ 15 ∙ share

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Cross-Domain 3D Equivariant Image Embeddings

Spherical convolutional networks have been introduced recently as tools ...
12/06/2018 ∙ by Carlos Esteves, et al. ∙ 14 ∙ share

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Equivariant Multi-View Networks

Several approaches to 3D vision tasks process multiple views of the inpu...
04/01/2019 ∙ by Carlos Esteves, et al. ∙ 14 ∙ share

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An Exploration of Embodied Visual Exploration

Embodied computer vision considers perception for robots in general, uns...
01/07/2020 ∙ by Santhosh K. Ramakrishnan, et al. ∙ 14 ∙ share

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Object-centric Video Prediction without Annotation

In order to interact with the world, agents must be able to predict the ...
05/06/2021 ∙ by Karl Schmeckpeper, et al. ∙ 14 ∙ share

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On the Capabilities and Limitations of Reasoning for Natural Language Understanding

Recent systems for natural language understanding are strong at overcomi...
01/08/2019 ∙ by Daniel Khashabi, et al. ∙ 12 ∙ share

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Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses

Deep neural networks are often called black-boxes due to their difficult...
09/12/2019 ∙ by Rodney LaLonde, et al. ∙ 12 ∙ share

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Learning Predictive Models From Observation and Interaction

Learning predictive models from interaction with the world allows an age...
12/30/2019 ∙ by Karl Schmeckpeper, et al. ∙ 12 ∙ share

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Depth Completion via Deep Basis Fitting

In this paper we consider the task of image-guided depth completion wher...
12/21/2019 ∙ by Chao Qu, et al. ∙ 11 ∙ share

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Active Learning in Video Tracking

Active learning methods, like uncertainty sampling, combined with probab...
12/29/2019 ∙ by Sima Behpour, et al. ∙ 11 ∙ share

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Causal Feature Discovery through Strategic Modification

We consider an online regression setting in which individuals adapt to t...
02/17/2020 ∙ by Yahav Bechavod, et al. ∙ 11 ∙ share

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Spin-Weighted Spherical CNNs

Learning equivariant representations is a promising way to reduce sample...
06/18/2020 ∙ by Carlos Esteves, et al. ∙ 11 ∙ share

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Predicting with Proxies

Predictive analytics is increasingly used to guide decision-making in ma...
12/28/2018 ∙ by Hamsa Bastani, et al. ∙ 10 ∙ share

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Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as s...
04/18/2019 ∙ by William La Cava, et al. ∙ 10 ∙ share

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Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Recent work has exhibited the surprising cross-lingual abilities of mult...
12/17/2019 ∙ by Karthikeyan K, et al. ∙ 10 ∙ share

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Is deep learning necessary for simple classification tasks?

Automated machine learning (AutoML) and deep learning (DL) are two cutti...
06/11/2020 ∙ by Joseph D. Romano, et al. ∙ 10 ∙ share

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A Baseline for Few-Shot Image Classification

Fine-tuning a deep network trained with the standard cross-entropy loss ...
09/06/2019 ∙ by Guneet S. Dhillon, et al. ∙ 9 ∙ share

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All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks

Image feature matching is a fundamental part of many geometric computer ...
01/07/2019 ∙ by Stephen Phillips, et al. ∙ 8 ∙ share

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Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

Empirical research in Natural Language Processing (NLP) has adopted a na...
11/10/2019 ∙ by Erfan Sadeqi Azer, et al. ∙ 8 ∙ share

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Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning

We aim to guard swarm-robotics applications against denial-of-service (D...
10/02/2019 ∙ by Lifeng Zhou, et al. ∙ 7 ∙ share

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Labeling Panoramas with Spherical Hourglass Networks

With the recent proliferation of consumer-grade 360 cameras, it is worth...
09/06/2018 ∙ by Carlos Esteves, et al. ∙ 6 ∙ share

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Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion

In this work, we propose a method that combines unsupervised deep learni...
12/20/2018 ∙ by Alex Zihao Zhu, et al. ∙ 6 ∙ share

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Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...
01/28/2019 ∙ by Lin Chen, et al. ∙ 6 ∙ share

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Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in repres...
06/09/2019 ∙ by Hao Peng, et al. ∙ 6 ∙ share

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Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion

Datasets containing sensitive information are often sequentially analyze...
03/10/2020 ∙ by Qinqing Zheng, et al. ∙ 6 ∙ share

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Graph Neural Networks for Motion Planning

This paper investigates the feasibility of using Graph Neural Networks (...
06/11/2020 ∙ by Arbaaz Khan, et al. ∙ 6 ∙ share

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Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...
02/06/2019 ∙ by Yahav Bechavod, et al. ∙ 4 ∙ share

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Understanding Spatial Relations through Multiple Modalities

Recognizing spatial relations and reasoning about them is essential in m...
07/19/2020 ∙ by Soham Dan, et al. ∙ 4 ∙ share

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Know Thyself: Transferable Visuomotor Control Through Robot-Awareness

Training visuomotor robot controllers from scratch on a new robot typica...
07/19/2021 ∙ by Edward S. Hu, et al. ∙ 4 ∙ share

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Question Answering as Global Reasoning over Semantic Abstractions

We propose a novel method for exploiting the semantic structure of text ...
06/09/2019 ∙ by Daniel Khashabi, et al. ∙ 3 ∙ share

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Joint Reasoning for Temporal and Causal Relations

Understanding temporal and causal relations between events is a fundamen...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 3 ∙ share

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Stochastic optimization approaches to learning concise representations

We propose and study a method for learning interpretable features via st...
07/03/2018 ∙ by William La Cava, et al. ∙ 2 ∙ share

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EBIC: an open source software for high-dimensional and big data biclustering analyses

Motivation: In this paper we present the latest release of EBIC, a next-...
07/26/2018 ∙ by Patryk Orzechowski, et al. ∙ 2 ∙ share

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