1 citations · 1 across the 10 of their papers we have counts for
17 papers
Thinking at the Right Size: Amortized Distillation Across Post-Trained LLMs
Yan Zhou, Sara Kangaslahti, Jonathan Geuter +4
Practical deployment of large language models (LLMs) requires families of post-trained variants---instruction-tuned, reasoning-tuned, and chat-style models---each at multiple sizes…
Understanding Layer Patching in Model Size Interpolation
Sara Kangaslahti, Jonathan Geuter, Nihal V. Nayak +3
Zero-shot model size interpolation aims to create new models of intermediate target sizes by combining existing models without additional training. Recent work on boomerang distill…
Assessing Sample Quality in Conditional Generation under Compositional Shift
Berker Demirel, Valentino Maiorca, Marco Fumero +2
Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimenta…
Statistical and structural identifiability in representation learning
Walter Nelson, Marco Fumero, Theofanis Karaletsos +1
Representation learning models exhibit a surprising stability in their internal representations. Whereas most prior work treats this stability as a single property, we formalize it…
MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
Berker Demirel, Marco Fumero, Theofanis Karaletsos +1
Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene edit…
Boomerang Distillation Enables Zero-Shot Model Size Interpolation
Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter +3
Large language models (LLMs) are typically deployed under diverse memory and compute constraints. Existing approaches build model families by training each size independently, whic…