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Found 4 papers, 1 papers with code
On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations
Namely, we show that when a small number of cells (e. g. 1%-5% of cells) have their positions shifted such that a measure of global congestion is guaranteed to remain unaffected (e. g. 1% of the design adversarially shifted by 0. 001% of the layout space results in a predicted decrease in congestion of up to 90%, while no change in congestion is implied by the perturbation).
Semi-Supervised Laplacian Learning on Stiefel Manifolds
Motivated by the need to address the degeneracy of canonical Laplace learning algorithms in low label rates, we propose to reformulate graph-based semi-supervised learning as a nonconvex generalization of a \emph{Trust-Region Subproblem} (TRS).
Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario
Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states.
Assessment of Reinforcement Learning for Macro Placement
We provide open, transparent implementation and assessment of Google Brain's deep reinforcement learning approach to macro placement and its Circuit Training (CT) implementation in GitHub.
