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Found 14 papers, 8 papers with code
NeuroAI for AI Safety
As AI systems become increasingly powerful, the need for safe AI has become more pressing.
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Echoing the 19th-century revolutions that gave rise to non-Euclidean geometry, an emerging line of research is redefining modern machine learning with non-Euclidean structures.
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks
An important problem in signal processing and deep learning is to achieve \textit{invariance} to nuisance factors not relevant for the task.
Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks
In this work, we formally prove that, under certain conditions, if a neural network is invariant to a finite group then its weights recover the Fourier transform on that group.
Exploring the hierarchical structure of human plans via program generation
Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions.
A General Framework for Robust G-Invariance in G-Equivariant Networks
We introduce a general method for achieving robust group-invariance in group-equivariant convolutional neural networks ($G$-CNNs), which we call the $G$-triple-correlation ($G$-TC) layer.
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems
Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks
The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein.
Quantifying Extrinsic Curvature in Neural Manifolds
The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables.
Bispectral Neural Networks
We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the space over which a signal is defined.
Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning.
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes
Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Representational efficiency outweighs action efficiency in human program induction
The importance of hierarchically structured representations for tractable planning has long been acknowledged.
