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
35 papers · 2 filters
RTN: Reparameterized Ternary Network
Yuhang Li, Xin Dong, Sai Qian Zhang +3
To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj +3
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchi…
Tropical Geometry and Piecewise-Linear Approximation of Curves and Surfaces on Weighted Lattices
Petros Maragos, Emmanouil Theodosis
Tropical Geometry and Mathematical Morphology share the same max-plus and min-plus semiring arithmetic and matrix algebra. In this chapter we summarize some of their main ideas and…
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters
Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang
In this paper, we evaluate training of deep recurrent neural networks with half-precision floats. We implement a distributed, data-parallel, synchronous training algorithm by integ…
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports
Neil Deshmukh, Selin Gumustop, Romane Gauriau +6
Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning appr…
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
Michael Lingzhi Li, Meng Dong, Jiawei Zhou +1
Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been prop…
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Int…
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complet…
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack, Sophie Hilgard, Emily Jia +2
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for e…
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Sai Qian Zhang, Qi Zhang, Jieyu Lin
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving effic…
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover +1
Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram…
Extracting Incentives from Black-Box Decisions
Yonadav Shavit, William S. Moses
An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and…
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li, Junpu Wang
Federated learning enables the creation of a powerful centralized model without compromising data privacy of multiple participants. While successful, it does not incorporate the ca…
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders. Specifically, we are interested in performing inference on data s…
Prediction Focused Topic Models via Feature Selection
Jason Ren, Russell Kunes, Finale Doshi-Velez
Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver…
The FAST Algorithm for Submodular Maximization
Adam Breuer, Eric Balkanski, Yaron Singer
In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint whose ap…
Beyond Imitation: Generative and Variational Choreography via Machine Learning
Mariel Pettee, Chase Shimmin, Douglas Duhaime +1
Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences…
Generalized Principal Component Analysis
F. William Townes
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on opt…
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers
Matthew Halpern, Behzad Boroujerdian, Todd Mummert +2
Today's cloud service architectures follow a "one size fits all" deployment strategy where the same service version instantiation is provided to the end users. However, consumers a…
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Jiayu Yao, Weiwei Pan, Soumya Ghosh +1
Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network. Inference in BNNs, however, is difficult; all inference methods for BNNs are approximate. In t…
CheckNet: Secure Inference on Untrusted Devices
Marcus Comiter, Surat Teerapittayanon, H. T. Kung
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integ…
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
Nathan Kallus, Masatoshi Uehara
Off-policy evaluation (OPE) in both contextual bandits and reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is of…
Robust Attacks against Multiple Classifiers
Juan C. Perdomo, Yaron Singer
We address the challenge of designing optimal adversarial noise algorithms for settings where a learner has access to multiple classifiers. We demonstrate how this problem can be f…
Finding Friend and Foe in Multi-Agent Games
Jack Serrino, Max Kleiman-Weiner, David C. Parkes +1
Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of coope…