The authors study probing, a paradigm in which a neural network is represented by its responses to a finite set of probe inputs rather than by its raw parameters.
Finite probe sets identify neural networks, but hidden activations make them far more informative
The authors prove when finite probes guarantee identifiability and universality, and show that hidden-layer responses power a state-of-the-art model, HiddenProbe.
Big Tech
Soutrik Sarangi · Yonatan Sverdlov · Adir Dayan · Haggai Maron · Nadav Dym
Microsoft · Technion – Israel Institute of Technology · NVIDIA
Research Digest··2 min read
Sarangi et al.
Why this paper
From Microsoft and 2 others
In one line
Finite probes suffice to identify and universally approximate neural functionals, with hidden probes being more informative.
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