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cs.LG2026
FORGE: Foundational Optimization Representations from Graph Embeddings
Zohair Shafi, Serdar Kadioglu
Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a lar…
cs.LG2026
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Koyena Pal, Serdar Kadioglu
Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enab…
cs.LG2024
Scalable iterative pruning of large language and vision models using block coordinate descent
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +4
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certa…