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Found 15 papers, 6 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.
On Leakage in Machine Learning Pipelines
Machine learning (ML) provides powerful tools for predictive modeling.
Getting aligned on representational alignment
Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine.
Do Invariances in Deep Neural Networks Align with Human Perception?
One necessary criterion for a network's invariances to align with human perception is for its IRIs look 'similar' to humans.
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
Furthermore, an LCN's predictions can be used in a two-stage approach to encourage a high-capacity network (HCN) to rely on deeper invariant features that should generalize broadly.
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of human similarity judgments that supplement the ILSVRC validation set.
Robust priors for regularized regression
We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data.
The perceptual boost of visual attention is task-dependent in naturalistic settings
Is the resulting perceptual boost task-dependent in naturalistic settings?
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
Motivated by selective attention in categorisation models, we developed a goal-directed attention mechanism that can process naturalistic (photographic) stimuli.
Learning as the Unsupervised Alignment of Conceptual Systems
Concept induction requires the extraction and naming of concepts from noisy perceptual experience.
Conceptual Organization is Revealed by Consumer Activity Patterns
Topics tended to be goal-directed and situational, consistent with the notion that human conceptual knowledge is tailored to support action.
Cognitive Capacity Limits and Electoral Districting
Partisan gerrymandering poses a threat to democracy.
Computers and Society Physics and Society
Optimal Teaching for Limited-Capacity Human Learners
We apply a machine teaching procedure to a cognitive model that is either limited capacity (as humans are) or unlimited capacity (as most machine learning systems are).
