Michalina Pacholska
Biographic Data
| ID | 4987600 |
|---|---|
| NAME | Michalina Pacholska |
| GIVEN NAMES | Michalina |
| FAMILY NAME | Pacholska |
| SIGNATURE | PACHOLSKA M |
| AFFILIATIONS | Google DeepMind (United Kingdom) |
| ORCID | 0000-0002-2160-6226 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Learning Interpretable Logic for Agent-Based Models from Domain Independent Primitives
Genetic programming (GP) is a powerful method applicable to Inverse Generative Social Science (IGSS) for learning non-trivial agent logic in agent-based models (ABMs). While previous attempts at using evolutionary algorithms for learning ABM structures have focused on recombining domain-specific primitives, this paper extends prior work by developing techniques to evolve interpretable agent logic from scratch using a highly flexible domain-specif…
Highly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1–4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single prot…
No prominent works on this page.
Highly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1–4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single prot…
Learning Interpretable Logic for Agent-Based Models from Domain Independent Primitives
Genetic programming (GP) is a powerful method applicable to Inverse Generative Social Science (IGSS) for learning non-trivial agent logic in agent-based models (ABMs). While previous attempts at using evolutionary algorithms for learning ABM structures have focused on recombining domain-specific primitives, this paper extends prior work by developing techniques to evolve interpretable agent logic from scratch using a highly flexible domain-specif…
Computer Science (2 works) · Machine learning (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Biology (1 work) · CASP (1 work) · Cognitive science (1 work) · Computational biology (1 work) · Domain (mathematical analysis (1 work)