A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yiming Li, Yong Jiang, Zhifeng Li, et al.
The first comprehensive survey of backdoor learning, categorizing attacks and defenses, providing a unified framework for poisoning-based attacks, and outlining future directions.
Mathieu Jacomy, Tommaso Venturini, Sébastien Heymann, et al.
ForceAtlas2 is a force-directed graph layout algorithm designed for Gephi, integrating techniques like Barnes-Hut simulation and adaptive temperatures for network visualization.
Yagmur Yigit, Mohamed Amine Ferrag, Mohamed Chahine Ghanem, et al.
A review of AI-driven approaches for critical infrastructure protection, covering LLM cybersecurity benchmarks, trust and privacy issues, and agentic AI for proactive defense.
Xunyu Zhu, Jian Li, Yong Liu, et al.
This survey reviews model compression techniques for large language models, covering quantization, pruning, and knowledge distillation, along with benchmarking and evaluation metrics.
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.
HuBERT uses offline clustering to provide aligned targets for masked prediction, matching or improving wav2vec 2.0 on speech benchmarks.
Baoguang Shi, Xiang Bai, Cong Yao
A novel end-to-end trainable neural network integrates feature extraction, sequence modeling, and transcription for scene text recognition without character segmentation.
Catherine Wah
CUB-200-2011 extends the CUB-200 dataset to 200 bird species with doubled images per category, new part localization annotations, and benchmarks for categorization and part localization.
Juyong Jiang, Fan Wang, Jiasi Shen, et al.
A systematic survey of LLMs for code generation, covering data curation, advances, evaluation, ethics, environmental impact, and applications, with comparative analysis on HumanEval, MBPP, and BigCodeBench.
Kai Han, Yunhe Wang, Hanting Chen, et al.
A comprehensive survey of vision transformer models, categorizing them by tasks, analyzing advantages and disadvantages, and discussing challenges and future directions.
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
SegNet introduces a deep encoder-decoder architecture for semantic segmentation that uses max-pooling indices for efficient non-linear upsampling, achieving competitive accuracy with low memory and inference time.
Bryan Y. Lim, Sercan Ö. Arık, Nicolas Loeff, et al.
Introduces the Temporal Fusion Transformer, an attention-based architecture for interpretable multi-horizon time series forecasting that combines high performance with insights into temporal dynamics.
Silva, Gabriel, Rodrigues, Mário, Teixeira, António, et al.
A graph neural network trained via genetic algorithm selects branching vertices in branch-and-reduce solvers, achieving a median 24% speedup on 73% of benchmark instances.