Awesome Multitask Learning Resources
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Updated
Apr 13, 2021
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Awesome Multitask Learning Resources
machine-learning deep-learning transfer-learning convex-optimization domain-adaptation multi-task-learning meta-learning federated-learning few-shot-learning sparse-learning
Modular and scalable computational imaging in Python with GPU/out-of-core computing.
machine-learning modular scalable computer-vision image-reconstruction python-library image-processing artificial-intelligence bayesian-methods computational-imaging image-restoration image-enhancement sensing matrix-free sparse-learning inverse-problem foundation-models array-api python-hpc array-computing
Sparse Switchable Normalization with sparse activation function SparestMax
SLIDE (Sub-LInear Deep learning Engine) written in Go
Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
machine-learning data-mining bioinformatics clustering feature-selection dimensionality-reduction representation-learning unsupervised-learning subspace-clustering sparse-learning
SPoT: Subpixel Placement of Tokens in Vision Transformers
Low-rank Dictionary Learning for Unsupervised Feature Selection
machine-learning data-mining clustering high-dimensional-data feature-selection artificial-intelligence dimensionality-reduction pattern-recognition feature-engineering unsupervised-learning dictionary-learning spectral-analysis low-rank-representation sparse-learning unsupervised-feature-selection
A Pytorch library for stochastic variational inference with sparsity inducing priors.
Multi-task learning with orthogonality / disjoint-support constraints (R package)
machine-learning r feature-selection rstats survival-analysis r-package structured-learning multi-task-learning sparse-learning
[AAAI 2019] "A Probabilistic Derivation of LASSO and L12-Norm Feature Selections", Di Ming, Chris Ding, Feiping Nie
This repo is an educational POC showing how thermodynamic sampling units can produce sparse polynomial approximations. It’s intended for demonstration, not production.
[JASA] Reconstruct Ising Model with Global Optimality via SLIDE
QSELM: 34.1M CPU LM, 215,771 training tok/s (8,529x measured Qwen training); sealed QA 90.6% vs Qwen3.5-0.8B 45.8%, memory 69.6% vs Qwen3-0.6B-FC 3.2%. No GPU. | QSELM:3408.7万参数CPU模型,训练21.6万token/s(Qwen训练实测的8,529倍);封存问答90.6%对Qwen3.5-0.8B 45.8%,跨轮记忆69.6%对Qwen3-0.6B-FC 3.2%;无需GPU。
nlp training machine-learning cpu inference text-generation language-model sparse-learning tool-use cpu-training llm function-calling local-ai cpu-native agent-memory qselm
[IJCAI 2019] "Robust Flexible Feature Selection via Exclusive L21 Regularization", Di Ming, Chris Ding
[ICML24] Better Locally Private Sparse Estimation Given Multiple Samples Per User
R package for Sparse Learning with weighted SVMs
SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning
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