activity
20172024
most citedSparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

341 citations · 371 across the 17 of their papers we have counts for

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10 papers · 1 filter

cs.DC2021

Wait-free approximate agreement on graphs

Dan Alistarh, Faith Ellen, Joel Rybicki

Approximate agreement is one of the few variants of consensus that can be solved in a wait-free manner in asynchronous systems where processes communicate by reading and writing to…

cs.DC2021

Fast Graphical Population Protocols

Dan Alistarh, Rati Gelashvili, Joel Rybicki

Let be a graph on nodes. In the stochastic population protocol model, a collection of indistinguishable, resource-limited nodes collectively solve tasks via pairwise in…

cs.DC2020

The Splay-List: A Distribution-Adaptive Concurrent Skip-List

Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova +1

The design and implementation of efficient concurrent data structures have seen significant attention. However, most of this work has focused on concurrent data structures providin…

cs.DC20201 cited

Fast General Distributed Transactions with Opacity using Global Time

Alex Shamis, Matthew Renzelmann, Stanko Novakovic +6

Transactions can simplify distributed applications by hiding data distribution, concurrency, and failures from the application developer. Ideally the developer would see the abstra…

cs.DC2020

Relaxed Scheduling for Scalable Belief Propagation

Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen

The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been consi…

cs.DC2018

Why Extension-Based Proofs Fail

Dan Alistarh, James Aspnes, Faith Ellen +2

We introduce extension-based proofs, a class of impossibility proofs that includes valency arguments. They are modelled as an interaction between a prover and a protocol. Using pro…