149 citations · 277 across the 25 of their papers we have counts for
31 papers
Counter Pools: Counter Representation for Efficient Stream Processing
Ran Ben Basat, Gil Einziger, Bilal Tyah +1
Due to the large data volume and number of distinct elements, space is often the bottleneck of many stream processing systems. The data structures used by these systems often consi…
Floating-floating point: a highly accurate number representation with flexible Counting ranges
Itamar Cohen, Gil Einziger
Efficient number representation is essential for federated learning, natural language processing, and network measurement solutions. Due to timing, area, and power constraints, suc…
Uncertainty Estimation based on Geometric Separation
Gabriella Chouraqui, Liron Cohen, Gil Einziger +1
In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. This process, known as uncertainty (or confidence) estim…
Virtual Service Embedding with Time-Varying Load and Provable Guarantees
Gil Einziger, Gabriel Scalosub, Carla Fabiana Chiasserini +1
Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual net…
SQUID: Faster Analytics via Sampled Quantile Estimation
Ran Ben-Basat, Gil Einziger, Wenchen Han +1
Streaming algorithms are fundamental in the analysis of large and online datasets. A key component of many such analytic tasks is -MAX, which finds the largest values in a n…
A Geometric Method for Improved Uncertainty Estimation in Real-time
Gabriella Chouraqui, Liron Cohen, Gil Einziger +1
Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertaint…