Icml2022 article intéressant

ICML2022C'est parti.,Ça fait longtemps que je n'ai pas blogué.,Aujourd'hui, le poissonICML2022La liste de réception de,J'ai choisi quelque chose qui m'intéresse pour la prochaine lecture, D'abordListe des articles,Intéressé par l'auto - extraction.

Oral

• Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
• Bounding Training Data Reconstruction in Private (Deep) Learning
• Measuring Representational Robustness of Neural Networks Through Shared Invariances
• ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
• Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models

Spotlights

• Does the Data Induce Capacity Control in Deep Learning?
• Accelerating Shapley Explanation via Contributive Cooperator Selection
• Prototype Based Classification from Hierarchy to Fairness
• Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
• Fair Representation Learning through Implicit Path Alignment
• Feature selection using e-values
• Mitigating Neural Network Overconfidence with Logit Normalization
• Active Multi-Task Representation Learning
• Dataset Condensation with Contrastive Signals
• Extracting Latent State Representations with Linear Dynamics from Rich Observations
• How to Fill the Optimal Set? Population Gradient Descent with Harmless Diversity
• Fair and Fast k-Center Clustering for Data Summarization
• Channel Importance Matters in Few-Shot Image Classification
• Label-Free Explainability for Unsupervised Models
• A psychological theory of explainability
• From data to functa: Your data point is a function and you should treat it like one
• Understanding Robust Overfitting of Adversarial Training and Beyond
• Learning Stable Classifiers by Transferring Unstable Features
• Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
• XAI for Transformers: Better Explanations through Conservative Propagation
• Role-based Multiplex Network Embedding
• Meaningfully debugging model mistakes using conceptual counterfactual explanations
• Forgetting-free Continual Learning with Winning Subnetworks
• Wide Neural Networks Forget Less Catastrophically
• Measuring dissimilarity with diffeomorphism invariance
• Efficient Learning of CNNs using Patch Based Features
• Multi-scale Feature Learning Dynamics: Insights for Double Descent

Accepted papers

• Achieving Fairness at No Utility Cost via Data Reweighing
• Confidence Score for Source-Free Unsupervised Domain Adaptation
• Probabilistic Bilevel Coreset Selection
• Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
• On the Effects of Artificial Data Modification
• Provable Domain Generalization via Invariant-Feature Subspace Recovery
• More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
• Information-Intensive Dataset Condensation
• Datamodels: Understanding Predictions with Data and Data with Predictions
• Benefits of Deep and Wide Convolutional Residual Networks: Function Approximation under Smoothness Constraint
• What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
• Understanding Instance-Level Impact of Fairness Constraints
• Disentangling Disease-related Representation from Obscure for Disease Prediction
• A new similarity measure for covariate shift with applications to nonparametric regression
• Representation Topology Divergence: A Method for Comparing Neural Network Representations