11 citations · 25 across the 9 of their papers we have counts for
9 papers
Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Sergei Chuprov, Richard D. Lange, Leon Reznik +3
This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solution involves systems monitor…
On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
Yang Liu, Ernest Fokoue, Richard Lange +1
This paper investigates the critical role of eigenalignments between the kernel matrix and learning targets in achieving robust generalization in learning problems. We establish a…
Use and usability: concepts of representation in philosophy, neuroscience, cognitive science, and computer science
Ben Baker, Richard D. Lange, Andrew Richmond +4
Representations play a central role in the study of both biological and artificial intelligence, as well as philosophy of mind. Across neuroscience, computer science, and philosoph…
Causality in the human niche: lessons for machine learning
Richard D. Lange, Konrad P. Kording
Humans interpret the world around them in terms of cause and effect and communicate their understanding of the world to each other in causal terms. These causal aspects of human co…
A large language model-assisted education tool to provide feedback on open-ended responses
Jordan K. Matelsky, Felipe Parodi, Tony Liu +2
Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such r…
Neural Networks as Paths through the Space of Representations
Richard D. Lange, Devin Kwok, Jordan Matelsky +3
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to…
Clustering units in neural networks: upstream vs downstream information
Richard D. Lange, David S. Rolnick, Konrad P. Kording
It has been hypothesized that some form of "modular" structure in artificial neural networks should be useful for learning, compositionality, and generalization. However, defining…
Interpolating between sampling and variational inference with infinite stochastic mixtures
Richard D. Lange, Ari Benjamin, Ralf M. Haefner +1
Sampling and Variational Inference (VI) are two large families of methods for approximate inference that have complementary strengths. Sampling methods excel at approximating arbit…
A probabilistic population code based on neural samples
Sabyasachi Shivkumar, Richard D. Lange, Ankani Chattoraj +1
Sensory processing is often characterized as implementing probabilistic inference: networks of neurons compute posterior beliefs over unobserved causes given the sensory inputs. Ho…