Asia
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.
Byzantine Dan Alistarh ISTAustria dan.alistarh@ist.ac.at Zeyuan
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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/
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
Neural Information Processing Systems http://nips.cc/
Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry
Finally, we suggest a modification to weight-normalization, which improves its performanceonlarge-scaletasks. 2
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
Detecting 3D keypoints with semantic consistency is widely used in many scenarios such as pose estimation, shape registration and robotics.
Post training 4-bit quantization of convolutional networks for rapid-deployment
Ron Banner, Yury Nahshan, Daniel Soudry
Neural Information Processing Systems http://nips.cc/
Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework
Predicting the effects of protein mutations is crucial for analyzing protein functions and understanding genetic diseases.