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  1. Home
  2. /Repositories
  3. /tensorflow/probability
Read original ↗
repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago

tensorflow/probability

Probabilistic reasoning and statistical analysis in TensorFlow

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Why these links exist

Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

  • PossiblePossibly related (embedding) · 45%Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP →
  • FuzzySimilar title/name (fuzzy) · 84%Production and Perception in LLMs: A Token Probability Approach →

    “Fuzzy title match (0.92): “Production and Perception in LLMs: A Token Probability Appro” ≈ “tensorflow/probability””

  • FuzzySimilar title/name (fuzzy) · 84%When are likely answers right? On Sequence Probability and Correctness in LLMs →

    “Fuzzy title match (0.92): “When are likely answers right? On Sequence Probability and C” ≈ “tensorflow/probability””

  • PossiblePossibly related (embedding) · 51%Quantitative Gaussian-Process limits of Tensor Programs →
  • PossiblePossibly related (embedding) · 48%From Global to Factor-Wise Expert Composition in Discrete Diffusion Models →

Covers

newsProfiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Implements

paperProduction and Perception in LLMs: A Token Probability ApproachpaperWhen are likely answers right? On Sequence Probability and Correctness in LLMs

Implements (incoming)

paperQuantitative Gaussian-Process limits of Tensor ProgramspaperFrom Global to Factor-Wise Expert Composition in Discrete Diffusion Models

Related across the graph

paperProduction and Perception in LLMs: A Token Probability ApproachnewsProfiling in PyTorch (Part 2): From nn.Linear to a Fused MLPpaperWhen are likely answers right? On Sequence Probability and Correctness in LLMspaperQuantitative Gaussian-Process limits of Tensor ProgramspaperFrom Global to Factor-Wise Expert Composition in Discrete Diffusion Models
Knowledge path·PProduction and Perception in LLMs: A Token Probability Approach→NProfiling in PyTorch (Part 2): From nn.Linear to a Fused MLP→PWhen are likely answers right? On Sequence Probability and Correctness in LLMs→Rtensorflow/probability

Topics

bayesian-methodsdata-sciencedeep-learningmachine-learningneural-networksprobabilistic-programmingstatisticstensorflow

Explore

Search similar →Knowledge graph →All repos →Full intelligence feed →
Graph trust82Primary
Graph score4421