12 citations · 12 across the 11 of their papers we have counts for
12 papers
How Much is Brain Data Worth for Machine Learning?
Lane Lewis, Zhixin Wang, David Schwab +1
If a person can solve a task, can measuring their brain make it easier to train a model to solve that task too? Recent NeuroAI work suggests that supplementing task training with n…
Compiling molecular ultrastructure into neural dynamics
Konrad P. Kording, Anton Arkhipov, Davy Deng +22
High-resolution brain imaging can now capture not just synapse locations but their molecular composition, with the cost of such mapping falling exponentially. Yet such ultrastructu…
Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics
Reece Keller, Alyn Kirsch, Felix Pei +3
Autonomy is a hallmark of animal intelligence, enabling adaptive and intelligent behavior in complex environments without relying on external reward or task structure. Existing rei…
How does the brain compute with probabilities?
Ralf M. Haefner, Jeff Beck, Cristina Savin +2
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'. We have…
Generalization properties of contrastive world models
Kandan Ramakrishnan, R. James Cotton, Xaq Pitkow +1
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypoth…
Understanding robustness and generalization of artificial neural networks through Fourier masks
Nikos Karantzas, Emma Besier, Josue Ortega Caro +4
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generaliz…
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks
Zhe Li, Andreas S. Tolias, Xaq Pitkow
Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive…
Two-argument activation functions learn soft XOR operations like cortical neurons
Kijung Yoon, Emin Orhan, Juhyun Kim +1
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this com…
AvaTr: One-Shot Speaker Extraction with Transformers
Shell Xu Hu, Md Rifat Arefin, Viet-Nhat Nguyen +3
To extract the voice of a target speaker when mixed with a variety of other sounds, such as white and ambient noises or the voices of interfering speakers, we extend the Transforme…
Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang +5
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, a…
Learning From Brains How to Regularize Machines
Zhe Li, Wieland Brendel, Edgar Y. Walker +7
Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input…
Exact feature probabilities in images with occlusion
Xaq Pitkow
To understand the computations of our visual system, it is important to understand also the natural environment it evolved to interpret. Unfortunately, existing models of the visua…