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Found 12 papers, 2 papers with code
Asymmetry in Low-Rank Adapters of Foundation Models
Specifically, when updating the parameter matrices of a neural network by adding a product $BA$, we observe that the $B$ and $A$ matrices have distinct functions: $A$ extracts features from the input, while $B$ uses these features to create the desired output.
Breaking the Curse of Dimensionality with Distributed Neural Computation
This improves the optimal bounds for traditional non-distributed deep learning models, namely ReLU MLPs, which need $\mathcal{O}(\varepsilon^{-n/2})$ parameters to achieve the same accuracy.
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
More importantly, our interpretability framework provides a general approach for quantitatively comparing embedding spaces across different tasks based on their contributions, a dimension that has been overlooked in previous literature on latent graph inference.
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Furthermore, when the latent graph can be represented in the feature space of a sufficiently regular kernel, we show that the combined neural snowflake and MLP encoder do not succumb to the curse of dimensionality by using only a low-degree polynomial number of parameters in the number of nodes.
Closed-Form Diffusion Models
In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training.
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures
We find that the network complexity of HNN implementing the graph representation is independent of the representation fidelity/distortion.
Projections of Model Spaces for Latent Graph Inference
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias.
Latent Graph Inference using Product Manifolds
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks.
Sheaf Neural Networks with Connection Laplacians
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.
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Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks
Memory replay may be key to learning in biological brains, which manage to learn new tasks continually without catastrophically interfering with previous knowledge.
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
The Reynolds-averaged Navier-Stokes (RANS) equations require accurate modeling of the anisotropic Reynolds stress tensor.
