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Found 7 papers, 3 papers with code
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
GFN-SR: Symbolic Regression with Generative Flow Networks
Symbolic regression (SR) is an area of interpretable machine learning that aims to identify mathematical expressions, often composed of simple functions, that best fit in a given set of covariates $X$ and response $y$.
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
A key property of neural networks (both biological and artificial) is how they learn to represent and manipulate input information in order to solve a task.
A Benchmark for Compositional Visual Reasoning
Overall, we hope that our challenge will spur interest in the development of neural architectures that can learn to harness compositionality toward more efficient learning.
Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search
The integration of behavioral phenomena into mechanistic models of cognitive function is a fundamental staple of cognitive science.
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
The terms multi-task learning and multitasking are easily confused.
A graph-theoretic approach to multitasking
A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.
