Harnessing smoothness to accelerate distributed optimization
There has been a growing effort in studying the distributed optimization problem over a
network. The objective is to optimize a global function formed by a sum of local functions …
network. The objective is to optimize a global function formed by a sum of local functions …
Learning local search heuristics for boolean satisfiability
We present an approach to learn SAT solver heuristics from scratch through deep
reinforcement learning with a curriculum. In particular, we incorporate a graph neural …
reinforcement learning with a curriculum. In particular, we incorporate a graph neural …
Learning to solve combinatorial optimization problems on real-world graphs in linear time
I Drori, A Kharkar, WR Sickinger, B Kates… - 2020 19th IEEE …, 2020 - ieeexplore.ieee.org
Combinatorial optimization algorithms for graph problems are usually designed afresh for
each new problem with careful attention by an expert to the problem structure. In this work …
each new problem with careful attention by an expert to the problem structure. In this work …
A sequential importance sampling algorithm for generating random graphs with prescribed degrees
J Blitzstein, P Diaconis - Internet mathematics, 2011 - Taylor & Francis
Random graphs with given degrees are a natural next step in complexity beyond the Erdős–
Rényi model, yet the degree constraint greatly complicates simulation and estimation. We …
Rényi model, yet the degree constraint greatly complicates simulation and estimation. We …
Embedding algorithms for quantum annealers with chimera and pegasus connection topologies
We propose two new algorithms–Spring-Based MinorMiner (SPMM) and Clique-Based
MinorMiner (CLMM)–which take as input the connectivity graph of a Quadratic …
MinorMiner (CLMM)–which take as input the connectivity graph of a Quadratic …
Revised note on learning quadratic assignment with graph neural networks
Inverse problems correspond to a certain type of optimization problems formulated over
appropriate input distributions. Recently, there has been a growing interest in understanding …
appropriate input distributions. Recently, there has been a growing interest in understanding …
Classical variational simulation of the quantum approximate optimization algorithm
A key open question in quantum computing is whether quantum algorithms can potentially
offer a significant advantage over classical algorithms for tasks of practical interest …
offer a significant advantage over classical algorithms for tasks of practical interest …
[BOOK][B] The science of deep learning
I Drori - 2022 - books.google.com
The Science of Deep Learning emerged from courses taught by the author that have
provided thousands of students with training and experience for their academic studies, and …
provided thousands of students with training and experience for their academic studies, and …
Fractional repetition codes for repair in distributed storage systems
We introduce a new class of exact Minimum-Bandwidth Regenerating (MBR) codes for
distributed storage systems, characterized by a low-complexity uncoded repair process that …
distributed storage systems, characterized by a low-complexity uncoded repair process that …
[PDF][PDF] A note on learning algorithms for quadratic assignment with graph neural networks
Many inverse problems are formulated as optimization problems over certain appropriate
input distributions. Recently, there has been a growing interest in understanding the …
input distributions. Recently, there has been a growing interest in understanding the …