5 citations · 9 across the 6 of their papers we have counts for
7 papers
ReproBAIT: Can coding agents build their own biological AI tools?
Shiv Muthupandiyan, Tolga H. Dur, Mac Walker +8
Large language models are increasingly competent at leveraging scientific knowledge and existing computational tools to execute complex workflows. At present, little information ex…
Seven simple steps for log analysis in AI systems
Magda Dubois, Ekin Zorer, Maia Hamin +17
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess…
Skewed Score: A statistical framework to assess autograders
Magda Dubois, Harry Coppock, Mario Giulianelli +3
The evaluation of large language model (LLM) outputs is increasingly performed by other LLMs, a setup commonly known as "LLM-as-a-judge", or autograders. While autograders offer a…
Are task representations gated in macaque prefrontal cortex?
Timo Flesch, Valerio Mante, William Newsome +3
A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques. This…
Out-of-sample scoring and automatic selection of causal estimators
Egor Kraev, Timo Flesch, Hudson Taylor Lekunze +2
Recently, many causal estimators for Conditional Average Treatment Effect (CATE) and instrumental variable (IV) problems have been published and open sourced, allowing to estimate…
Continual task learning in natural and artificial agents
Timo Flesch, Andrew Saxe, Christopher Summerfield
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how task…
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals
Timo Flesch, David G. Nagy, Andrew Saxe +1
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep…