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【第二期】学海对拳——2020.7.18周论文精选

No 1. 《Closed-Form Factorization of Latent Semantics in GANs》
No 2. *《Graph Structure of Neural Networks》
No 3. 《Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations》
No 4. 效果不错的像素图矢量化
No 5. 《Xiaomingbot: A Multilingual Robot News Reporter》
No 6. 《Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration》
No 7. 《Visualizing Transfer Learning》
No 8. 对比代码表示学习(ContraCode)
No 9. 《SegFix: Model-Agnostic Boundary Refinement for Segmentation》
No 10. 《Explainable Deep One-Class Classification》
No 11. 《Sudo rm -rf: Efficient Networks for Universal Audio Source Separation》
No 12. 《Contrastive Training for Improved Out-of-Distribution Detection》
No 13. 《Graph Neural Network Based Coarse-Grained Mapping Prediction》
No 14. 《The Computational Limits of Deep Learning》
No 15. 《A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection》
No 16. 《Video Object Segmentation with Episodic Graph Memory Networks》
No 17. 《Deep Reinforcement Learning and its Neuroscientific Implications》
No 18. 《Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations》
No 19. *《Meta-Learning Requires Meta-Augmentation》
No 20. 《Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP》
No 21. 《Deep Placental Vessel Segmentation for Fetoscopic Mosaicking》
No 22. 《Causal Inference using Gaussian Processes with Structured Latent Confounders》
No 23. 《A Survey of Privacy Attacks in Machine Learning》
No 24. 《D2D: Learning to find good correspondences for image matching and manipulation》
No 25. 《Uncertainty-Aware Lookahead Factor Models for Quantitative Investing》
No 26. 《Transfer Learning for Brain-Computer Interfaces: A Complete Pipeline》
No 27. 《RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval》
No 28. 《ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation》
No 29. 《All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference》
No 30. 《Attack of the Tails: Yes, You Really Can Backdoor Federated Learning》

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