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Found 12 papers, 6 papers with code
Equivariant Frames and the Impossibility of Continuous Canonicalization
Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures.
On the hardness of learning under symmetries
We study the problem of learning equivariant neural networks via gradient descent.
Learning Polynomial Problems with $SL(2,\mathbb{R})$ Equivariance
Moreover, we observe that these polynomial learning problems are equivariant to the non-compact group $SL(2,\mathbb{R})$, which consists of area-preserving linear transformations.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering.
GULP: a prediction-based metric between representations
Comparing the representations learned by different neural networks has recently emerged as a key tool to understand various architectures and ultimately optimize them.
Distilling Model Failures as Directions in Latent Space
Moreover, by combining our framework with off-the-shelf diffusion models, we can generate images that are especially challenging for the analyzed model, and thus can be used to perform synthetic data augmentation that helps remedy the model's failure modes.
Implicit Bias of Linear Equivariant Networks
Group equivariant convolutional neural networks (G-CNNs) are generalizations of convolutional neural networks (CNNs) which excel in a wide range of technical applications by explicitly encoding symmetries, such as rotations and permutations, in their architectures.
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression
There is a rich literature on recovering data from limited measurements under the assumption of sparsity in some basis, whether known (compressed sensing) or unknown (dictionary learning).
Practical Phase Retrieval: Low-Photon Holography with Untrained Priors
To the best of our knowledge, this is the first work to consider a dataset-free machine learning approach for holographic phase retrieval.
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imaging (CDI).
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
To establish the dimension-independent upper bound, we next show that a mini-batching algorithm provides an $ O(\frac{T}{\sqrt{K}}) $ upper bound, and therefore conclude that the minimax regret of switching-constrained OCO is $ \Theta(\frac{T}{\sqrt{K}}) $ for any $K$.
