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Found 10 papers, 3 papers with code
Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval
Furthermore, our method can also boost the uni-modal retrieval performance of image-text retrieval models, enabling it to achieve universal retrieval.
On f-Divergence Principled Domain Adaptation: An Improved Framework
Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning.
Over-training with Mixup May Hurt Generalization
Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of deep models trained with SGD.
Tighter Information-Theoretic Generalization Bounds from Supersamples
In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting of Steinke & Zakynthinou (2020)-the setting of the "conditional mutual information" framework.
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States
Using this estimate, we apply the PAC-Bayes-like information-theoretic bounds developed in both Xu & Raginsky (2017) and Negrea et al. (2019) to obtain generalization upper bounds in terms of the KL divergence between the steady-state weight distribution of SGD with respect to a prior distribution.
Information-Theoretic Analysis of Unsupervised Domain Adaptation
This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA).
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
This paper follows up on a recent work of Neu et al. (2021) and presents some new information-theoretic upper bounds for the generalization error of machine learning models, such as neural networks, trained with SGD.
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible.
On the Generalization of Neural Networks Trained with SGD: Information-Theoretical Bounds and Implications
Understanding the generalization behaviour of deep neural networks is an important theme of modern research in machine learning.
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models.
