https://github.com/mtics/feddae
공식 코드Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Program Synthesis Dialog Agents for Interactive Decision-Making
Toward Neural-Network-Guided Program Synthesis and Verification
Scaling Neural Program Synthesis with Distribution-based Search
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
NNrepair: Constraint-based Repair of Neural Network Classifiers
Learning to Reason via Program Generation, Emulation, and Search
Improving Code Generation by Training with Natural Language Feedback
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments
CFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases
Graph Neural Networks For Mapping Variables Between Programs -- Extended Version
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
PLANS: Robust Program Learning from Neurally Inferred Specifications
Execution-based Code Generation using Deep Reinforcement Learning
Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
Program Synthesis and Semantic Parsing with Learned Code Idioms
The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection
MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program Synthesis
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.