Seminar
20240905_꼬리노드_윤준호.pptxAdaptive Attention-Based Graph Representation Learning to Detect Phishing Accounts on the Ethereum Blockchain
발표자: 윤준호
Sun, H., Liu, Z., Wang, S., & Wang, H. (2024). Adaptive attention-based graph representation learning to detect phishing accounts on the Ethereum blockchain. IEEE Transactions on Network Science and Engineering.
20240816_랩세미나_이현주.pptxDifferentially Private Diffusion Models
발표자: 이현주
Dockhorn, T., Cao, T., Vahdat, A., & Kreis, K. (2022). Differentially private diffusion models. arXiv preprint arXiv:2210.09929.
20240809_labseminar_최석훈.pptxchain-of-thought prompting elicits reasoning in large language models
발표자: 최석훈
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
20240726_랩세미나_윤준호.pptxgnn-rag: graph neural retrieval for large language model reasoning
발표자: 윤준호
Mavromatis, C., & Karypis, G. (2024). GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning. arXiv preprint arXiv:2405.20139.
20240719_labseminar_최수환.pptxTHe superising effectiveness of ppo in cooperative multi-agent games
발표자: 최수환
Yu, C., Velu, A., Vinitsky, E., Gao, J., Wang, Y., Bayen, A., & Wu, Y. (2022). The surprising effectiveness of ppo in cooperative multi-agent games. Advances in Neural Information Processing Systems, 35, 24611-24624.
20240607_랩세미나_강준하.pptxHierarchical prototype networks for continual graph representation learning
발표자: 강준하
Zhang, X., Song, D., & Tao, D. (2022). Hierarchical prototype networks for continual graph representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4), 4622-4636.
20240517_labseminar_최석훈.pptxFew-shot class-incremental audio classification using dynamically expanded classifier with self-attention modified prototypes
발표자: 최석훈
Li, Y., Cao, W., Xie, W., Li, J., & Benetos, E. (2023). Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes. IEEE Transactions on Multimedia.
20240503_랩세미나_백무근.pptxDiFfusion models beat gans on image synthesis
발표자: 백무근
Dhariwal, P., & Nichol, A. (2021). Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34, 8780-8794.
20240403_labseminar_최수환.pptxPROXIMAL POLICY OPTIMIZATION ALGORITHMS
발표자: 최수환
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
20240320_랩세미나_이현주_DDPM.pptxDenoising diffusion probabilistic models
발표자: 이현주
Ho, J., Ajay J., & Pieter A. (2020). Denoising Diffusion Probabilistic Models. Advances in neural information processing systems, 33, 6840-6851.
20240315_랩세미나_강준하.pptxA graph neural network-based bearing fault detection method
발표자: 강준하
Xiao, L., Yang, X., & Yang, X. (2023). A graph neural network-based bearing fault detection method. Scientific Reports, 13(1), 5286.
20240221_랩세미나_이진희.pptxHifi-gan: Generative adversarial networks for efficeint and high fidelity speech synthesis
발표자: 이진희
Jungil, K., Jaehyeon, K. & Jaekyoung, B. (2020). HiFi-GAN: Generative Adversarial Networks for Efficeint and High Fidelity Speech Synthesis. Advances in neural information processing systems, 33, 17022-17033.
20240214_랩세미나_백무근.pptxDenoising diffusion probabilistic models
발표자: 백무근
Ho, J., Ajay J., & Pieter A. (2020). Denoising Diffusion Probabilistic Models. Advances in neural information processing systems, 33, 6840-6851.
20240125_labseminar_최수환_v2.pptxMulti-agent deep reinforcement learning: a survey
발표자: 최수환
Gronauer, S., & Diepold, K. (2022). Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review, 1-49.
20240118_랩세미나_이현주_Prototype-Guided Memory Replay for Continual Learning.pptxPrototype-guided memory replay for continual learning
발표자: 이현주
Ho, S., Liu, M., Du, L., Gao, L., & Xiang, Y. (2023). Prototype-Guided Memory Replay for Continual Learning. IEEE Transactions on Neural Networks and Learning Systems.
20240103_랩세미나_강준하.pptxGenetic-GNN: Evolutionary architecture search for graph neural networks
발표자: 강준하
Shi, M., Tang, Y., Zhu, X., Huang, Y., Wilson, D., Zhuang, Y., & Liu, J. (2022). Genetic-GNN: Evolutionary architecture search for graph neural networks. Knowledge-Based Systems, 247, 108752.
20231227_랩세미나_윤준호.pptxAttention is all you need
발표자: 윤준호
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
20231121_랩세미나_이현주_Tristounet Triplet loss for Speaker Turn Embedding.pptxTRISTOUNET: Triplet loss for speaker turn embedding
발표자: 이현주
Bredin, H. (2017, March). Tristounet: triplet loss for speaker turn embedding. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5430-5434). IEEE.
20231107_랩세미나_강준하.pptxGraph autoencoder-based unsupervised outlier detection
발표자: 강준하
Du, X., Yu, J., Chu, Z., Jin, L., & Chen, J. (2022). Graph autoencoder-based unsupervised outlier detection. Information Sciences, 608, 532-550.
20231010_랩세미나_윤준호.pptxWeb2Vec: Phishing Webpage Detection Method Based on Multidimensional Features Driven by Deep Learning
발표자: 윤준호
Feng, J., Zou, L., Ye, O., & Han, J. (2020). Web2vec: Phishing webpage detection method based on multidimensional features driven by deep learning. IEEE Access, 8, 221214-221224.