Members
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CDFM
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Python ★ 54 7d agoExplain → -
SASA
No description.
Python ★ 46 3y agoExplain → -
CDMIR
No description.
Python ★ 38 7mo agoExplain → -
SADGA
The PyTorch implementation of paper SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL. (NeurIPS 2021)
Python ★ 35 4y agoExplain → -
SASA-pytorch
No description.
Python ★ 29 3y agoExplain → -
DSAN
No description.
Python ★ 21 4y agoExplain → -
CausalAgent
A Conversational Multi-Agent System for End-to-End Causal Inference.
Python ★ 20 5mo agoExplain → -
DSR
The implement of "Learning Disentangled Semantic Representation for Domain Adaptation" (IJCAI 2019)
Python ★ 20 7y agoExplain → -
LSTD
No description.
Python ★ 18 1y agoExplain → -
CANM
This code provide the CANM algorithim for causal discovery. Please cite "Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Models. IJCAI 2019."
R ★ 16 7y agoExplain → -
SELF
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI,2018."
R ★ 16 8y agoExplain → -
Causal-aware_LLMs
No description.
Python ★ 14 1y agoExplain → -
MGNN ⑂
No description.
Python ★ 11 2y agoExplain → -
Dr.ECI
No description.
Python ★ 11 1y agoExplain → -
IDOL
No description.
Python ★ 10 1y agoExplain → -
GCA
No description.
Python ★ 10 3y agoExplain → -
LCA
No description.
Python ★ 7 1y agoExplain → -
SHP
The python implementation of paper Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences (IJCAI 2023)
Python ★ 7 2y agoExplain → -
Track-SQL
No description.
Python ★ 6 8mo agoExplain → -
FaultAlarm_RL
Official code for FaultAlarm_RL
Python ★ 5 1y agoExplain → -
TEA
No description.
Python ★ 5 4y agoExplain → -
GenLink
No description.
Python ★ 4 8mo agoExplain → -
SCI
No description.
Python ★ 4 2y agoExplain → -
REST
No description.
Python ★ 4 3y agoExplain → -
SSD
No description.
Roff ★ 4 5y agoExplain → -
SAM-NER
No description.
★ 3 2mo agoExplain → -
CCSL
No description.
Python ★ 3 3y agoExplain → -
LASER
No description.
Python ★ 3 2y agoExplain → -
CausalDiscoveryBasedOnEntropy
No description.
Python ★ 3 2y agoExplain → -
LC-PB-SCM
The Python implementation of the paper "On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding"(ICML 2026).
Python ★ 2 1mo agoExplain → -
HAL
No description.
Python ★ 2 4mo agoExplain → -
IRRA
No description.
Python ★ 2 1y agoExplain → -
PBSCM-PGF
The Python implementation of the paper "On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function" (NeurIPS 2024)
Python ★ 2 1y agoExplain → -
FANS ⑂
No description.
Python ★ 2 1y agoExplain → -
DSSL
User activities in real systems are usually time-sensitive. But most of the existing sequential models in recommender systems neglect the time-related signals. In this paper, we find that users' temporal behaviours tend to be driven by their regularly-changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile temporal dependency of states is personalized and complex, it is challenging to represent, model and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a Deep Structured State Learning (DSSL) framework which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.
Python ★ 2 4y agoExplain → -
IDEA
No description.
Python ★ 2 2y agoExplain → -
SERE
No description.
Python ★ 1 12d agoExplain → -
S2IT
No description.
Python ★ 1 5mo agoExplain → -
MATE
No description.
Python ★ 1 11mo agoExplain → -
S2GSL
No description.
Python ★ 1 1y agoExplain → -
TAG
ACL 2020 paper [TAG : Type Auxiliary Guiding for Code Comment Generation](https://arxiv.org/abs/2005.02835).
Python ★ 1 1y agoExplain → -
PBSCM
The Python implementation of paper Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis. (AAAI 2024)
Python ★ 1 2y agoExplain → -
HGCRM
No description.
Python ★ 1 2y agoExplain → -
SASA_CNN_Extractor
SASA with CNN featrue extractor
Python ★ 1 3y agoExplain → -
DMIR_REC
No description.
★ 1 3y agoExplain → -
Dassl.pytorch ⑂
A PyTorch toolbox for domain adaptation and semi-supervised learning.
★ 1 6y agoExplain → -
tensorflow-vs-pytorch ⑂
Guide for both TensorFlow and PyTorch in comparative way
Jupyter Notebook ★ 1 7y agoExplain → -
TSDR
Official code for "Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing" (IJCAI 2026)
Python ★ 0 1mo agoExplain → -
AI2030-causal
No description.
JavaScript ★ 0 3mo agoExplain → -
introduction
No description.
JavaScript ★ 0 3mo agoExplain → -
DMM
No description.
Python ★ 0 11mo agoExplain → -
CACA
COLING 2025 PAPER (CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction)
★ 0 1y agoExplain → -
TNet
No description.
Python ★ 0 1y agoExplain → -
RRNet
Official code for "Generalization bound for estimating causal effects from observational network data"
Python ★ 0 1y agoExplain → -
NeuralTMT
No description.
Python ★ 0 1y agoExplain → -
FOM
No description.
HTML ★ 0 4y agoExplain → -
Shared_SSM ⑂
Shared State Space Model
★ 0 4y agoExplain → -
SLMGAE
No description.
Python ★ 0 6y agoExplain →
No repos match these filters.