Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
On the properties of neural machine translation: Encoder -- decoder approaches
6 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
MPU is a framework that achieves privacy-preserving unlearning for LLMs by distributing perturbed model copies for local client-side unlearning followed by server-side aggregation with harmonic denoising.
TOFU is a new benchmark with synthetic profiles and metrics demonstrating that existing unlearning algorithms for LLMs fail to achieve effective forgetting of targeted information.
A neural network fuses wheel and motor speed signals to cut wheel-speed estimation error by up to 85% versus the production sensor on real Volkswagen ID.7 data.
citing papers explorer
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End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
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Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
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MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
MPU is a framework that achieves privacy-preserving unlearning for LLMs by distributing perturbed model copies for local client-side unlearning followed by server-side aggregation with harmonic denoising.
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TOFU: A Task of Fictitious Unlearning for LLMs
TOFU is a new benchmark with synthetic profiles and metrics demonstrating that existing unlearning algorithms for LLMs fail to achieve effective forgetting of targeted information.
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Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation
A neural network fuses wheel and motor speed signals to cut wheel-speed estimation error by up to 85% versus the production sensor on real Volkswagen ID.7 data.
- How Language Models Process Negation