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Found 13 papers, 1 papers with code
Unbalanced and Light Optimal Transport
We address this challenge and propose a novel theoretically-justified and lightweight unbalanced EOT solver.
Multi-step domain adaptation by adversarial attack to $\mathcal{H} Δ\mathcal{H}$-divergence
Adversarial examples are transferable between different models.
Adversarial Training Improves Joint Energy-Based Generative Modelling
We propose the novel framework for generative modelling using hybrid energy-based models.
Connecting adversarial attacks and optimal transport for domain adaptation
In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain.
Neural Optimal Transport with General Cost Functionals
We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.
Cycle monotonicity of adversarial attacks for optimal domain adaptation
In our algorithm, instead of mapping from target to the source domain, optimal transport maps target samples to the set of adversarial examples.
Connecting convex energy-based inference and optimal transport for domain adaptation
The connection of optimal transport and neural networks finds a rich application in machine learning problems.
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
To support the findings of this work, this paper seeks to provide an additional example of a Transformer-based RL method.
Wasserstein-2 Generative Networks
We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance).
Backronym
The problem is that the amount of information is also growing, and many methods remain unknown in a large number of papers.
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application
In this paper, we try to answer the following question: Can information about policy conditioning help to shape a more stable and general policy of reinforcement learning agents?
Interpretable Few-Shot Learning via Linear Distillation
It is important to develop mathematically tractable models than can interpret knowledge extracted from the data and provide reasonable predictions.
