
Chip Placement with Deep Reinforcement Learning
In this work, we present a learningbased approach to chip placement, on...
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Placement Optimization with Deep Reinforcement Learning
Placement Optimization is an important problem in systems and chip desig...
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Generalized Clustering by Learning to Optimize Expected Normalized Cuts
We introduce a novel endtoend approach for learning to cluster in the ...
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GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly af...
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Reinforcement Learning Driven Heuristic Optimization
Heuristic algorithms such as simulated annealing, Concorde, and METIS ar...
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GAP: Generalizable Approximate Graph Partitioning Framework
Graph partitioning is the problem of dividing the nodes of a graph into ...
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Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cos...
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Device Placement Optimization with Reinforcement Learning
The past few years have witnessed a growth in size and computational req...
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Outrageously Large Neural Networks: The SparselyGated MixtureofExperts Layer
The capacity of a neural network to absorb information is limited by its...
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oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e.g. Gram or similarity matrices) are essential for man...
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Azalia Mirhoseini
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