# Lifting Biomolecular Data Acquisition

arXiv: https://arxiv.org/abs/2512.15984 · HTML: https://mlsanity.com/paper/2512.15984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023)
- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [Measuring and regularizing networks in function space](https://mlsanity.com/paper/1805.08289/) (2018)
- [Robust Estimation via Robust Gradient Estimation](https://mlsanity.com/paper/1802.06485/) (2018)
- [All You Need is a Good Functional Prior for Bayesian Deep Learning](https://mlsanity.com/paper/2011.12829/) (2020)
- [Concept Algebra for (Score-Based) Text-Controlled Generative Models](https://mlsanity.com/paper/2302.03693/) (2023)
- [Deep Compressed Sensing](https://mlsanity.com/paper/1905.06723/) (2019)
- [Finding Global Minima via Kernel Approximations](https://mlsanity.com/paper/2012.11978/) (2020)
- [Oblique Decision Trees from Derivatives of ReLU Networks](https://mlsanity.com/paper/1909.13488/) (2019)
- [Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization](https://mlsanity.com/paper/1812.10539/) (2018)
- [Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization](https://mlsanity.com/paper/2012.11554/) (2020)
- [Accelerated Learning on Large Scale Screens using Generative Library Models](https://mlsanity.com/paper/2510.16612/) (2025)

## Built on by (most cited first, 1 of 1)

- [Continuous Variational Synthesis](https://mlsanity.com/paper/2609.35083/) (2026)

## Abstract (arXiv)

One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to function space. We measure the activity of multiple different molecules simultaneously, rather than individually. Then, we deconvolute the molecule-activity map during model training. Co-design of wet lab experiments and learning algorithms provably leads to orders-of-magnitude gains in information density. We demonstrate on antibodies and cell therapies.