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Found 5 papers, 3 papers with code
Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables
Defenses have also been proposed to guard model weights.
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
New Adversarial Image Detection Based on Sentiment Analysis
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e. g., DeepFool, are on the rise and outrunning adversarial example detection techniques.
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid.
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Hardware-friendly network quantization (e. g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones.
