https://github.com/xtLyu/CrossBA
공식 코드Cross-Context Backdoor Attacks against Graph Prompt Learning
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Cross-Context Backdoor Attacks against Graph Prompt Learning
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Radio Map Estimation via Latent Domain Plug-and-Play Denoising
Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration
A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
Mixture of Attention Heads: Selecting Attention Heads Per Token
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel Inference
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood
Efficient Global Multi-object Tracking Under Minimum-cost Circulation Framework
NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
Detection of circular permutations by Protein Language Models
Union of Experts: Adapting Hierarchical Routing to Equivalently Decomposed Transformer
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.