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Found 10 papers, 2 papers with code
An Empirical Study of Self-supervised Learning with Wasserstein Distance
We find that the model performance depends on the combination of TWD and probability model, and that the Jeffrey divergence regularization helps in model training.
Embarrassingly Simple Text Watermarks
LLMs can generate texts that cannot be distinguished from human-written texts.
Necessary and Sufficient Watermark for Large Language Models
Although existing watermarking methods have successfully detected texts generated by LLMs, they significantly degrade the quality of the generated texts.
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
In this study, we propose Momentum Tracking, which is a method with momentum whose convergence rate is proven to be independent of data heterogeneity.
Approximating 1-Wasserstein Distance with Trees
In this paper, we aim to approximate the 1-Wasserstein distance by the tree-Wasserstein distance (TWD), where TWD is a 1-Wasserstein distance with tree-based embedding and can be computed in linear time with respect to the number of nodes on a tree.
Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization
However, the convergence rate of the ECL is provided only when the objective function is convex, and has not been shown in a standard machine learning setting where the objective function is non-convex.
Communication Compression for Decentralized Learning with Operator Splitting Methods
Moreover, we demonstrate that the C-ECL is more robust to heterogeneous data than the Gossip-based algorithms.
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following
An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to interact with objects in 3D environments.
Fixed Support Tree-Sliced Wasserstein Barycenter
By contrast, the Wasserstein distance on a tree, called the tree-Wasserstein distance, can be computed in linear time and allows for the fast comparison of a large number of distributions.
Supervised Tree-Wasserstein Distance
Specifically, we rewrite the Wasserstein distance on the tree metric by the parent-child relationships of a tree and formulate it as a continuous optimization problem using a contrastive loss.
