A Sparse Tensor Generator with Efficient Feature Extraction
Tugba Torun, Eren Yenigul, Ameer Taweel
No strong AI-core implementation/artifact signals were detected from current providers.
Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most sui ...
table storage format, decomposition algorithm, and reordering methods. However, due to the large size of real-world tensors, even extracting these features can be computationally expensive without careful optimization. To address these limitations, we have developed a smart sparse tensor generator that replicates key characteristics of real sparse tensors. Additionally, we propose efficient methods for extracting a comprehensive set of sparse tensor features. The effectiveness of our generator is validated through the quality of extracted features and the performance of decomposition on the generated tensors. Both the sparse tensor feature extractor and the tensor generator are open source with all the artifacts available at https://github.com/sparcityeu/FeaTensor and https://github.com/sparcityeu/GenTensor, respectively.
Results & Benchmarks
No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.
Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis.
Implementation Evidence Summary
This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
Reproduction Risks
- No maintained paper-verified implementation is currently available
Evidence disclosure
Evidence graph: 2 refs, 1 links.
Utility signals: depth 60/100, grounding 58/100, status medium.
Implementation Status
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
- Start with framework-native implementations (e.g. PyTorch optimizer module, Optax, or Transformers training loops).
- Replicate the paper ablation settings first, then compare against modern baselines.
Reproduction readiness
No verified implementation available
- · No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet.
Continue with targeted Hugging Face searches derived from the paper title and method context:
Datasets
Tip: start with models, then check datasets/spaces if you need evaluation data or demos.
Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.
Research context
0
Citations
0
References
Tasks
Generator (circuit theory), Tensor (intrinsic definition), Computer science, Feature extraction, Pattern recognition (psychology), Physical Sciences
Methods
None detected
Domains
Extraction (chemistry), Artificial intelligence, Mathematics, Computational Mathematics
Evaluation & Human Feedback Data
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXExplore Similar Papers
Jump to Paper2Code search queries derived from this paper's research context.
Related papers
-
Search on Paper2Code
ICA and PCA integrated feature extraction for classification (2016) Semantic similarity
-
Search on Paper2Code
Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition (2010) Semantic similarity
-
Search on Paper2Code
Image feature extraction from wavelet scalogram based on kernel principle component analysis (2009) Semantic similarity
-
Search on Paper2Code
Partial discharge pattern recognition using multiscale feature extraction and support vector machine (2013) Semantic similarity
-
Search on Paper2Code
Comparative studies of Feature Extraction methods with application to face recognition (2007) Semantic similarity
-
Search on Paper2Code
Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples (2014) Semantic similarity
Need human evaluators for your AI research? Scale annotation with expert AI Trainers.