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Markus Weimer

2 accepted papers

2019

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach

ICLR 2019poster

Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can lear…

Cited by 120SourcePDFScholar
2018

Batch-Expansion Training: An Efficient Optimization Framework

AISTATS 2018poster

We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset. As opposed to stochastic approaches, batches do not need to be resampled i.i.d. at every iteration, thus making BET more resource efficient in a distributed setting, and when disk-a…

Cited by 0SourcePDFScholar