23 citations · 56 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Đorđe Miladinović, Kumar Shridhar, Kushal Jain +4
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.…
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Max B. Paulus, Giulia Zarpellon, Andreas Krause +2
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern…
Instance-wise algorithm configuration with graph neural networks
Romeo Valentin, Claudio Ferrari, Jérémy Scheurer +3
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a…
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus +2
Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due t…