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Works14 from public data
- DO-GAN: A Double Oracle Framework for Generative Adversarial Networks9
This paper proposes a new approach to train Gen-erative Adversarial Networks (GANs) where a double-oracle framework is deployed using the generator and discrim-inator oracles, and applies this framework to established GAN architectures such as vanilla GAN, Deep Convolutional GGAN, Spectral Normalization GAN and Stacked GAN.
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- Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models4
A new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where the authors deploy a double-oracle framework using best response oracles to Adversarial Neural Architecture Search and Adversarial Training algorithms.
- Planning sequential interventions to tackle depression in large uncertain social networks using deep reinforcement learning4
A new architecture called DRLPSO (Deep Reinforcement Learning with Particle Swarm Optimization) is proposed to enhance learning performance in a partially observable environment with large state and action space and outperforms the state-of-the-art DRL methods by an average of 32%.
- Self-evolving Autoencoder Embedded Q-Network2
SAQN, a novel approach wherein a self-evolving autoencoder (SA) is embedded with a Q-Network (QN), highlights the effectiveness of the self-evolving autoencoder and its collaboration with the Q-Network in tackling sequential decision-making tasks.
- Whole-Body Semantic-to-Actuation Grounding of Elephant-Inspired Soft-Trunk Motion via Lightweight Flow Matching1
A whole-body semantic-to-actuation grounding framework for elephant-inspired soft-trunk HRI based on lightweight flow matching is proposed and shows that adding the generated soft-trunk motion channel increases the positive overall-satisfaction rating from 46% to 82% over the audiovisual-only baseline.
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- We Mind Your Well-Being: Preventing Depression in Uncertain Social Networks by Sequential Interventions1
This paper proposes a new model that addresses the sequential intervention of participants while considering the propagation of emotions and formulate it as a Partially Observable Markov Decision Process (POMDP) to handle uncertainties about their mental states and the influence between them.
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- Caption, Create, Continue: Continual Learning with Pre-trained Generative Vision-Language Models–
The proposed CLTS (Continual Learning via Text-Image Synergy), a novel class-incremental framework that mitigates forgetting without storing real task data, introduces a novel perspective by integrating generative text-image augmentation for scalable continual learning.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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