5 citations · 12 across the 4 of their papers we have counts for
8 papers
QPOPSS: Query and Parallelism Optimized Space-Saving for Finding Frequent Stream Elements
Victor Jarlow, Charalampos Stylianopoulos, Marina Papatriantafilou
The frequent elements problem, a key component in demanding stream-data analytics, involves selecting elements whose occurrence exceeds a user-specified threshold. Fast, memory-eff…
Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence
Karl Bäckström, Ivan Walulya, Marina Papatriantafilou +1
Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free al…
MindTheStep-AsyncPSGD: Adaptive Asynchronous Parallel Stochastic Gradient Descent
Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas
Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several…
Piecewise Linear Approximation in Data Streaming: Algorithmic Implementations and Experimental Analysis
Romaric Duvignau, Vincenzo Gulisano, Marina Papatriantafilou +1
Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments w…
Lisco: A Continuous Approach in LiDAR Point-cloud Clustering
Hannaneh Najdataei, Yiannis Nikolakopoulos, Vincenzo Gulisano +1
The light detection and ranging (LiDAR) technology allows to sense surrounding objects with fine-grained resolution in a large areas. Their data (aka point clouds), generated conti…
Aiding Autonomous Vehicles with Fault-tolerant V2V Communication
Vladimir Savic, Elad M. Schiller, Marina Papatriantafilou
Vehicle-to-vehicle (V2V) communication is a key component of the future autonomous driving systems. V2V can provide an improved awareness of the surrounding environment, and the kn…