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Found 21 papers, 1 papers with code
Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity
The increasing context window size in Large Language Models (LLMs), such as the GPT and LLaMA series, has improved their ability to tackle complex, long-text tasks, but at the cost of inference efficiency, particularly regarding memory and computational complexity.
Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
This integration forms three hybrid NNs (long-term, slow, and fast MLPs) that predict the entire system dynamics across multiple time scales.
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
In this paper, we study the adversarial vulnerability of recent VLP transformers and design a novel Joint Multimodal Transformer Feature Attack (JMTFA) that concurrently introduces adversarial perturbations in both visual and textual modalities under white-box settings.
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields.
Quantum Federated Learning Experiments in the Cloud with Data Encoding
Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy.
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions
Among existing methods for UTST tasks, attention masking approach and Large Language Models (LLMs) are deemed as two pioneering methods.
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality.
UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
Recommending items to potentially interested users has been an important commercial task that faces two main challenges: accuracy and explainability.
PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance
The model achieves a more comprehensive feature representation by the features which connect global and local features.
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System
As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios.
Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy
By utilizing this representation, a generic subsystem decomposition method is proposed to partition the entire IES vertically based on the dynamic time scale and horizontally based on the closeness of interconnections between the operating units.
Security and Privacy Problems in Voice Assistant Applications: A Survey
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication
It reduces the success rate of rPPG spoofing attacks in user authentication to 0. 05.
Economic model predictive control of integrated energy systems: A multi-time-scale framework
Subsequently, the CEMPC, which includes slow economic model predictive control (EMPC), medium EMPC and fast EMPC, is developed.
Towards Privacy-Preserving Neural Architecture Search
Machine learning promotes the continuous development of signal processing in various fields, including network traffic monitoring, EEG classification, face identification, and many more.
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification
Specifically, Sa-GCN and Se-GCN are proposed to extract the spatial and spectral features by modeling correlations between spatial pixels and between spectral bands, respectively.
ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks
Moreover, the model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples.
Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey
Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years.
On Robustness of Neural Semantic Parsers
In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification
Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs.
Automated Big Traffic Analytics for Cyber Security
Network traffic analytics technology is a cornerstone for cyber security systems.
Cryptography and Security
