10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS735 papers
- Massachusetts Institute of TechnologyUS455 papers
- University of OxfordGB379 papers
- University of ChicagoUS368 papers
- University of MichiganUS356 papers
- Lawrence Berkeley National LaboratoryUS354 papers
- The Ohio State UniversityUS350 papers
- Yale UniversityUS348 papers
- University of PittsburghUS317 papers
- Argonne National LaboratoryUS316 papers
- University College LondonGB312 papers
- University of PisaIT310 papers
77 papers · 1 filter
Detecting Hate Speech in Social Media
Shervin Malmasi, Marcos Zampieri
In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by app…
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox
Ruobin Gong, Xiao-Li Meng
Statistical learning using imprecise probabilities is gaining more attention because it presents an alternative strategy for reducing irreplicable findings by freeing the user from…
The inverse Monge-Ampere flow and applications to Kahler-Einstein metrics
Tristan C. Collins, Tomoyuki Hisamoto, Ryosuke Takahashi
We introduce the inverse Monge-Ampere flow as the gradient flow of the Ding energy functional on the space of Kahler metrics in for . We prove the long-time e…
Key Science Goals for the Next Generation Very Large Array (ngVLA): Report from the ngVLA Science Advisory Council
Alberto D. Bolatto, Shami Chatterjee, Caitlin M. Casey +20
This document describes some of the fundamental astrophysical problems that require observing capabilities at millimeter- and centimeter wavelengths well beyond those of existing,…
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Andrew Slavin Ross, Finale Doshi-Velez
Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their…
Automatic differentiation in quantum chemistry with an application to fully variational Hartree-Fock
Teresa Tamayo-Mendoza, Christoph Kreisbeck, Roland Lindh +1
Automatic Differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, wit…
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step…
Practical Whole-System Provenance Capture
Thomas Pasquier, Xueyuan Han, Mark Goldstein +4
Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, f…
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen, Udit Gupta, Robert Adolf +4
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of t…
FunTAL: Reasonably Mixing a Functional Language with Assembly
Daniel Patterson, Jamie Perconti, Christos Dimoulas +1
We present FunTAL, the first multi-language system to formalize safe interoperability between a high-level functional language and low-level assembly code while supporting composit…
Intriguing Properties of Adversarial Examples
Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz +1
It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previo…
Single-mode instability in standing-wave lasers: The quantum cascade laser as a self-pumped parametric oscillator
Tobias S. Mansuripur, Camille Vernet, Paul Chevalier +13
We report the observation of a clear single-mode instability threshold in continuous-wave Fabry-Perot quantum cascade lasers (QCLs). The instability is characterized by the appeara…
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
Jeremiah Zhe Liu, Brent Coull
This work constructs a hypothesis test for detecting whether an data-generating function belongs to a specific reproducing kernel Hilbert space $\mathcal{H}_…
Complex Word Identification: Challenges in Data Annotation and System Performance
Marcos Zampieri, Shervin Malmasi, Gustavo Paetzold +1
This paper revisits the problem of complex word identification (CWI) following up the SemEval CWI shared task. We use ensemble classifiers to investigate how well computational met…
Limiting empirical spectral distribution for the non-backtracking matrix of an Erdős-Rényi random graph
Ke Wang, Philip Matchett Wood
In this note, we give a precise description of the limiting empirical spectral distribution (ESD) for the non-backtracking matrices for an Erdős-Rényi graph assuming te…
Combined Forward-Backward Asymmetry Measurements in Top-Antitop Quark Production at the Tevatron
CDF collaboration, D0 collaboration
The CDF and D0 experiments at the Fermilab Tevatron have measured the asymmetry between yields of forward- and backward-produced top and antitop quarks based on their rapidity diff…
Metastable Ultracondensed Solid Hydrogenous Materials
W. J. Nellis
The primary purpose of this paper is to stimulate theoretical predictions of how to retain metastably hydrogenous materials made at high pressure P on release to ambient. Ultracond…
Embedded Binarized Neural Networks
Bradley McDanel, Surat Teerapittayanon, H. T. Kung
We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently…
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate…
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved…
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
Miriam Cha, Youngjune Gwon, H. T. Kung
We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embedding…
Adversarial nets with perceptual losses for text-to-image synthesis
Miriam Cha, Youngjune Gwon, H. T. Kung
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated ima…
Cryptographically Secure Information Flow Control on Key-Value Stores
Lucas Waye, Pablo Buiras, Owen Arden +2
We present Clio, an information flow control (IFC) system that transparently incorporates cryptography to enforce confidentiality and integrity policies on untrusted storage. Clio…
Better together? Statistical learning in models made of modules
Pierre E. Jacob, Lawrence M. Murray, Chris C. Holmes +1
In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it…