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Found 11 papers, 3 papers with code
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
As an application, we derive a hyperparameter selection policy that finds the best balance between the supervised and the unsupervised terms of our learning criterion.
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation
Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.
Density Estimation for Conservative Q-Learning
We hence introduce Density Conservative Q-Learning (D-CQL), a batch-RL algorithm with strong theoretical guarantees that carefully penalizes the value function based on the amount of information collected in the state-action space.
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
To address this issue, we derive a theoretical analysis of the Lipschitz continuity of attention modules and introduce LipschitzNorm, a simple and parameter-free normalization for self-attention mechanisms that enforces the model to be Lipschitz continuous.
Ego-based Entropy Measures for Structural Representations on Graphs
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Do all adversarial examples have the same consequences?
EMTL: A Generative Domain Adaptation Approach
We propose an unsupervised domain adaptation approach based on generative models.
Ego-based Entropy Measures for Structural Representations
Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes.
Coloring graph neural networks for node disambiguation
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs).
Lipschitz regularity of deep neural networks: analysis and efficient estimation
First, we show that, even for two layer neural networks, the exact computation of this quantity is NP-hard and state-of-art methods may significantly overestimate it.
