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cs.LG2026
A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks
Pankhil Gawade, Kirsten Fischer, Guy Wolf +1
How depth and width of neural networks shape learned representations is usually studied only after training. We show that, in a tractable feature-learning setting, this dependence…
cs.LG2026
Few-Shot Bioactivity Prediction with Meta-Learning under Assay Heterogeneity
Michal Kmicikiewicz, Tommy Rochussen, Vincent Fortuin +1
Accurate bioactivity prediction is a central challenge in early-stage drug discovery, as individual assays often contain too few measurements to train reliable models independently…
cs.LG2026
Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
Serra Korkmaz, Adam Izdebski, Jonathan Pirnay +5
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yiel…