Program Synthesis
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Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.
Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anish-lu-yihe/pySVRT
공식 코드https://github.com/anonuser4213/oeis-synthesis
공식 코드Learning Program Synthesis for Integer Sequences from Scratch
https://github.com/ant-research/BayesianLinearAttributionForRootCauseLocalization_BALANCE
공식 코드BALANCE: Bayesian Linear Attribution for Root Cause Localization
https://github.com/aqasch/gadget_rl
공식 코드Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
https://github.com/arabelatso/deepfd
공식 코드DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
https://github.com/arbaazQureshi/attention_based_multimodal_fusion_for_estimating_depression
공식 코드Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations
https://github.com/arise-lab/semcoder
공식 코드SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
https://github.com/assert-kth/repairllama
공식 코드RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
https://github.com/atrautsch/nlbse2022_replication_kit
공식 코드https://github.com/autoswap/autoswap_cvpr_2022
공식 코드Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
https://github.com/barakeel/oeis-synthesis
공식 코드Learning Program Synthesis for Integer Sequences from Scratch
https://github.com/biasvariancelabs/aitlas
공식 코드AiTLAS: Artificial Intelligence Toolbox for Earth Observation
https://github.com/black-yt/ExtremeCast
공식 코드ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
https://github.com/capergroup/houdini
공식 코드https://github.com/celinehocquette/aaai24-maxsynth
공식 코드https://github.com/celinehocquette/ijcai24-stevie
공식 코드Learning logic programs by discovering higher-order abstractions
https://github.com/celinehocquette/ijcai25-relational-decomposition
공식 코드https://github.com/celinehocquette/magicpopper
공식 코드https://github.com/celinehocquette/numsynth-aaai23
공식 코드https://github.com/charlesjin/emergent-semantics
공식 코드Emergent Representations of Program Semantics in Language Models Trained on Programs
https://github.com/chengjunyan1/graph-sparse-transformer
공식 코드Graph Conditioned Sparse-Attention for Improved Source Code Understanding
https://github.com/chenlabgccri/CancerTypePrediction
공식 코드Convolutional neural network models for cancer type prediction based on gene expression
https://github.com/clement-bonnet/lpn
공식 코드https://github.com/clulab/releases
공식 코드From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
https://github.com/coesch/ptree
공식 코드https://github.com/computationalpathologygroup/hooknet
공식 코드https://github.com/cooelf/Auto-UI
공식 코드https://github.com/cooelf/auto-gui
공식 코드https://github.com/copilot-eval-replication-package/copilotevaluation
공식 코드https://github.com/crazydonkey200/neural-symbolic-machines
공식 코드Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/cuhk-shenzhen-se/d4c
공식 코드https://github.com/cuplv/text-to-sql-wizardcoder
공식 코드Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
https://github.com/danaesavi/poi-type-prediction
공식 코드https://github.com/daniel442li/gpt-human-eval
공식 코드https://github.com/deepdiagnosis/icse2022
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.