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Sociology

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Byshkin/EquilibriumExpectation

공식 코드

A Simple Algorithm for Scalable Monte Carlo Inference

https://github.com/Byshkin/RBM

공식 코드

A Simple Algorithm for Scalable Monte Carlo Inference

https://github.com/RUC-GSAI/YuLan-OneSim

공식 코드

YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models

https://github.com/TristaCao/into_inclusivecoref

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Toward Gender-Inclusive Coreference Resolution

https://github.com/allenai/discoverybench

공식 코드

DiscoveryBench: Towards Data-Driven Discovery with Large Language Models

https://github.com/aurelien-pelissier/regir

공식 코드

Practical and scalable simulations of non-Markovian stochastic processes and temporal networks with individual node properties

https://github.com/bigdata-ustc/educat

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Survey of Computerized Adaptive Testing: A Machine Learning Perspective

https://github.com/druidowm/occamnet_socialsci

공식 코드

AI-Assisted Discovery of Quantitative and Formal Models in Social Science

https://github.com/fcarichon/Cultural_Novelty

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Crossing Boundaries: Leveraging Semantic Divergences to Explore Cultural Novelty in Cooking Recipes

https://github.com/fdtomasi/regain

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Latent Variable Time-varying Network Inference

https://github.com/flxclxc/rl-graph-search

공식 코드

Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies

https://github.com/henhagg/semple_mem

공식 코드

Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology

https://github.com/jacobh140/century-of-college-football

공식 코드

A Spectral Framework for Tracking Communities in Evolving Networks

https://github.com/jacobh140/spectral-dcd

공식 코드

A Spectral Framework for Tracking Communities in Evolving Networks

https://github.com/kamtoazr/image-transcription-and-grading

공식 코드

Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology course

https://github.com/kentchang/dramatic-conversation-disentanglement

공식 코드

Dramatic Conversation Disentanglement

https://github.com/lporcaro/music-diversity-analysis

공식 코드

Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics

https://github.com/martingerlach/hSBM_Topicmodel

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A network approach to topic models

https://github.com/mayaokawa/opinion_dynamics

공식 코드

Predicting Opinion Dynamics via Sociologically-Informed Neural Networks

https://github.com/mrbhimani/employee_survey_analysis

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Decoding the Workplace & EOR: An Employee Survey Analysis by Data Science Techniques and Visualization

https://github.com/nvedant07/effort_reward_fairness

공식 코드

On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

https://github.com/octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference

공식 코드

Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

https://github.com/samuelhorvath/granger-causality-with-nn

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Granger Causality using Neural Networks

https://github.com/schmalwb/topic_model_time_series

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Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide

https://github.com/sijingtu/kdd-23-adversaries-with-limited-information

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Adversaries with Limited Information in the Friedkin--Johnsen Model

https://github.com/sinelnikovmaxim/mpp-vae

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Latent variable model for high-dimensional point process with structured missingness

https://github.com/snap-stanford/POPPER

공식 코드

Automated Hypothesis Validation with Agentic Sequential Falsifications

https://github.com/stivalaa/EstimNetDirected

공식 코드

A Simple Algorithm for Scalable Monte Carlo Inference

https://github.com/zhiqinma/surrogate_data_based_machine_learning

공식 코드

Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data

https://github.com/Arda-Bati/Reddit-Hyperlinks-Conflict-Prediction

Community detection in graphs

https://github.com/YunjinPark/modu_project

Semantics derived automatically from language corpora contain human-like biases

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/bioannidis/adaptive_recurrent_graph_neural_network

A Recurrent Graph Neural Network for Multi-Relational Data

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/emiliasiv/flexible-parametric-inference-for-space-time-hawkes-processes

Flexible Parametric Inference for Space-Time Hawkes Processes

https://github.com/jstrappa/jllcm

Efficient comparison of independence structures of log-linear models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.