Papers with Code 아카이브
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/dhairyadalal/nmt_if_then
공식 코드Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
https://github.com/dhdhagar/byokg
공식 코드Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
https://github.com/dibyaghosh/icvf_release
공식 코드Reinforcement Learning from Passive Data via Latent Intentions
https://github.com/dice-group/PYKE
공식 코드https://github.com/dpfried/incoder
공식 코드InCoder: A Generative Model for Code Infilling and Synthesis
https://github.com/ease-crc/vr-neem-converter
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/ease-crc/vr-program-synthesis
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/ejmichaud/neural-verification
공식 코드Opening the AI black box: program synthesis via mechanistic interpretability
https://github.com/ekinakyurek/marc
공식 코드The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
https://github.com/elisim/piven
공식 코드PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
https://github.com/ernie-research/ma-rlhf
공식 코드MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
https://github.com/erp12/CodeBuildingGeneticProgramming-ProtoType
공식 코드https://github.com/esteng/regal_program_learning
공식 코드ReGAL: Refactoring Programs to Discover Generalizable Abstractions
https://github.com/eth-sri/sven
공식 코드Large Language Models for Code: Security Hardening and Adversarial Testing
https://github.com/eunomia-bpf/ken
공식 코드https://github.com/evalplus/evalplus
공식 코드https://github.com/evanthebouncy/icml2018_selecting_representative_examples
공식 코드https://github.com/evanthebouncy/larc_gpt4
공식 코드https://github.com/evanthebouncy/pragmatic_synthesis_ranking
공식 코드https://github.com/evo-eval/evoeval
공식 코드https://github.com/ezelikman/parsel
공식 코드Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
https://github.com/ezhan94/neurosymbolic-encoders
공식 코드https://github.com/frankhlchi/infer_data_valuation
공식 코드Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
https://github.com/fsoft-ai4code/hyperagent
공식 코드HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
https://github.com/fudanselab/linuxflbench
공식 코드Benchmarking and Enhancing LLM Agents in Localizing Linux Kernel Bugs
https://github.com/futianfan/mimosa
공식 코드MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
https://github.com/fveler/fvel
공식 코드FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
https://github.com/gabegrand/lilo
공식 코드LILO: Learning Interpretable Libraries by Compressing and Documenting Code
https://github.com/galprz/simqa
공식 코드https://github.com/gammatauai/opentau
공식 코드Type Prediction With Program Decomposition and Fill-in-the-Type Training
https://github.com/giganticode/out_of_context_paper_data
공식 코드Out of Context: How important is Local Context in Neural Program Repair?
https://github.com/gintool/gin
공식 코드Enhancing Genetic Improvement Mutations Using Large Language Models
https://github.com/gladia-research-group/explanatory-learning
공식 코드https://github.com/glycanml/glycanml
공식 코드GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning
https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.