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Concept Algebra for (Score-Based) Text-Controlled Generative Models Zihao Wang 1, Lin Gui
This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski
Recent progress in the development of voltage indicators [1-8] has brought us closer to a longstanding goal incellular neuroscience: imaging the full spatiotemporal voltageonadendritic tree. These recordings have the potential (pun not intended) to resolve fundamental questions about the computations performed by dendrites -- questions that have remained open for more than a century[9,10].
Learning Auctions with Robust Incentive Guarantees
Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H. Morgenstern
Neural Information Processing Systems http://nips.cc/
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes into the original graph and pose realistic threats.
Learning low-dimensional state embeddings and metastable clusters from time series data
Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang
Neural Information Processing Systems http://nips.cc/