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Found 32 papers, 10 papers with code
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
How much explicit guidance is necessary for conditional diffusion?
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
On the generalization of learning algorithms that do not converge
To reduce this discrepancy between theory and practice, this paper focuses on the generalization of neural networks whose training dynamics do not necessarily converge to fixed points.
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects.
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors.
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs.
Neural Extensions: Training Neural Networks with Set Functions
Our framework includes well-known extensions such as the Lovasz extension of submodular set functions and facilitates the design of novel continuous extensions based on problem-specific considerations, including constraints.
Partition and Code: learning how to compress graphs
Can we use machine learning to compress graph data?
What training reveals about neural network complexity
This work explores the Benevolent Training Hypothesis (BTH) which argues that the complexity of the function a deep neural network (NN) is learning can be deduced by its training dynamics.
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited.
GG-GAN: A Geometric Graph Generative Adversarial Network
We study the fundamental problem of graph generation.
Multi-Head Attention: Collaborate Instead of Concatenate
We also show that it is possible to re-parametrize a pre-trained multi-head attention layer into our collaborative attention layer.
Building powerful and equivariant graph neural networks with structural message-passing
We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node.
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances.
How hard is to distinguish graphs with graph neural networks?
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs.
On the Relationship between Self-Attention and Convolutional Layers
This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.
Ranked #143 on
Image Classification
on CIFAR-10
What graph neural networks cannot learn: depth vs width
This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp).
Discriminative structural graph classification
This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes.
The role of invariance in spectral complexity-based generalization bounds
Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels.
Extrapolating paths with graph neural networks
We consider the problem of path inference: given a path prefix, i. e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix.
Approximating Spectral Clustering via Sampling: a Review
Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph.
Graph reduction with spectral and cut guarantees
Can one reduce the size of a graph without significantly altering its basic properties?
Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph?
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?
How many samples are sufficient to guarantee that the eigenvectors of the sample covariance matrix are close to those of the actual covariance matrix?
Fast Approximate Spectral Clustering for Dynamic Networks
Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size.
A Time-Vertex Signal Processing Framework
An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph.
How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?
How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix?
Stationary time-vertex signal processing
This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some {known} graph topology.
Predicting the evolution of stationary graph signals
An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph.
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks.
Autoregressive Moving Average Graph Filtering
We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation.
Frequency Analysis of Temporal Graph Signals
This letter extends the concept of graph-frequency to graph signals that evolve with time.
