401 citations · 792 across the 14 of their papers we have counts for
14 papers
GenoML: Automated Machine Learning for Genomics
Mary B. Makarious, Hampton L. Leonard, Dan Vitale +14
GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain e…
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Brighten Godfrey +1
The method of choice for parameter aggregation in Deep Neural Network (DNN) training, a network-intensive task, is shifting from the Parameter Server model to decentralized aggrega…
R-Storm: Resource-Aware Scheduling in Storm
Boyang Peng, Mohammad Hosseini, Zhihao Hong +2
The era of big data has led to the emergence of new systems for real-time distributed stream processing, e.g., Apache Storm is one of the most popular stream processing systems in…
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos +11
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically require…
Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data
Vipul Satone, Rachneet Kaur, Faraz Faghri +3
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are chara…
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell
State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communicatio…
Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan +2
Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpre…
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh +3
In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most…
Decentralized User-Centric Access Control using PubSub over Blockchain
Sayed Hadi Hashemi, Faraz Faghri, Roy H Campbell
We present a mechanism that puts users in the center of control and empowers them to dictate the access to their collections of data. Revisiting the fundamental mechanisms in secur…
A Novel Scheduling Framework Leveraging Hardware Cache Partitioning for Cache-Side-Channel Elimination in Clouds
Read Sprabery, Konstantin Evchenko, Abhilash Raj +3
While there exist many isolation mechanisms that are available to cloud service providers, including virtual machines, containers, etc., the problem of side-channel increases in im…
Fast Generation for Convolutional Autoregressive Models
Prajit Ramachandran, Tom Le Paine, Pooya Khorrami +6
Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial…
Performance Modeling of Distributed Deep Neural Networks
Sayed Hadi Hashemi, Shadi A. Noghabi, William Gropp +1
During the past decade, machine learning has become extremely popular and can be found in many aspects of our every day life. Nowayadays with explosion of data while rapid growth o…
NoiseOut: A Simple Way to Prune Neural Networks
Mohammad Babaeizadeh, Paris Smaragdis, Roy H. Campbell
Neural networks are usually over-parameterized with significant redundancy in the number of required neurons which results in unnecessary computation and memory usage at inference…
Leveraging Social-Network Infrastructure to Improve Peer-to-Peer Overlay Performance: Results from Orkut
Zahid Anwar, William Yurcik, Vivek Pandey +3
Application-level peer-to-peer (P2P) network overlays are an emerging paradigm that facilitates decentralization and flexibility in the scalable deployment of applications such as…