Alternatives and similar repositories for tpp-short-course

Users that are interested in tpp-short-course are comparing it to the libraries listed below

• hongyuanmei / neural-hawkes-particle-smoothing
  Source code of the neural Hawkes particle smoothing (ICML 2019)
  ☆44Updated 6 years ago
• shchur / triangular-tpp
  Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
  ☆23Updated 2 years ago
• yangalan123 / anhp-andtt
  Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)
  ☆62Updated last year
• xiaoshuai09 / Wasserstein-Learning-For-Point-Process
  learning point processes by means of optimal transport and wasserstein distance
  ☆54Updated 7 years ago
• mtsang / interaction_interpretability
  Feature Interaction Interpretability via Interaction Detection
  ☆35Updated 2 years ago
• HongtengXu / PoPPy
  A Point Process Toolbox Based on PyTorch
  ☆138Updated 5 years ago
• shchur / ifl-tpp
  Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)
  ☆89Updated 4 years ago
• Networks-Learning / tpprl
  Code and data for "Deep Reinforcement Learning of Marked Temporal Point Processes", NeurIPS 2018
  ☆81Updated 6 years ago
• meowoodie / Learning-Temporal-Point-Processes-via-Reinforcement-Learning
  PPG (Point Process Generator) is a Reinforcement Learning framework that is able to produce actions by imitating expert sequences.
  ☆14Updated 6 years ago
• omitakahiro / NeuralNetworkPointProcess
  code for "Fully Neural Network based Model for General Temporal Point Processes"
  ☆64Updated 4 years ago
• hongyuanmei / nce-mpp
  Source code for Noise-Contrastive Estimation for Multivariate Point Processes (NeurIPS 2020).
  ☆15Updated 5 years ago
• musically-ut / tf_rmtpp
  Recurrent Marked Temporal Point Processes
  ☆56Updated 4 years ago
• xiaoshuai09 / Recurrent-Point-Process
  Modeling the asynchronous event sequence via Recurrent Point Process
  ☆61Updated 8 years ago
• crazysal / VariationalRNN
  A Recurrent Latent Variable Model for Sequential Data
  ☆28Updated 7 years ago
• hongyuanmei / neurawkes
  Source code of The Neural Hawkes Process (NIPS 2017)
  ☆229Updated 4 years ago
• ermongroup / BCD-Nets
  Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
  ☆27Updated 4 years ago
• stmorse / hawkes
  Python class for generation and parameter estimation of multivariate Hawkes processes
  ☆174Updated 4 months ago
• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆26Updated 3 years ago
• sjblim / rmsn_nips_2018
  ☆40Updated 7 years ago
• razhangwei / CAUSE
  ☆32Updated 3 years ago
• BorisMuzellec / EllipticalEmbeddings
  Python code for implementing embeddings in the Wasserstein space of elliptical distributions
  ☆11Updated 5 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• xiao03 / nh
  A PyTorch Implementation of Neural Hawkes Process. Redefined.
  ☆35Updated 5 years ago
• meowoodie / Reinforcement-Learning-of-Spatio-Temporal-Point-Processes
  A general framework for learning spatio-temporal point processes via reinforcement learning
  ☆30Updated 5 years ago
• Hongrui24 / NeuralHawkesPytorch
  ☆24Updated 5 years ago
• d909b / ame
  🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of featu…
  ☆43Updated 2 years ago
• dunan / MultiVariatePointProcess
  ☆77Updated 8 years ago
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆64Updated 5 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆104Updated 5 years ago
• grobgl / rnnsm
  ☆18Updated 7 years ago