Results for “manifold-learning”

24 skills

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

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Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

30.2k · bundle

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Merges mature lessons from a domain memory file into its instruction file, preserving knowledge with minimal redundancy.

Matryoshka Representation Learning Arxiv 2205 13147v4

Matryoshka Representation Learning

Multimodal Few Shot Learning With Frozen Language Models Arx

Multimodal Few-Shot Learning with Frozen Language Models

Chameleon Mixed Modal Early Fusion Foundation Models Arxiv 2

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Masked Autoencoders Are Scalable Vision Learners Arxiv 2111

Masked Autoencoders Are Scalable Vision Learners

Mantis Interleaved Multi Image Instruction Tuning Arxiv 2405

Mantis: Interleaved Multi-Image Instruction Tuning

Autoaugment Learning Augmentation Strategies From Data Arxiv

AutoAugment: Learning Augmentation Strategies from Data

Use when simulating stochastic folding kinetics of single-stranded nucleic acids, computing first passage times between structures, or analyzing RNA/DNA folding trajectories.

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.

Multimodal Learning With Transformers A Survey Arxiv 2206 06

Multimodal Learning with Transformers: A Survey

Multimodal Neurons In Artificial Neural Networks Arxiv 2103

Multimodal Neurons in Artificial Neural Networks

Influence Functions In Deep Learning Arxiv 2002 08484v3

Influence Functions in Deep Learning

Use when predicting RNA secondary structures, calculating minimum free energy (MFE) folds, or computing partition functions and base pairing probabilities for RNA sequences.

Scaffold Exercises

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting, then commit with git.

Scaffold Exercises

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

Emu2 Generative Multimodal Models Are In Context Learners Ar

Emu2: Generative Multimodal Models are In-Context Learners

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

metinduraktr-44

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Learn By Teaching

当需要深化对某个主题的理解、检验知识掌握程度或克服“冒名顶替综合征”时

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.