https://github.com/unitn-sml/syn-interventions-algorithmic-recourse
공식 코드Synthesizing explainable counterfactual policies for algorithmic recourse with 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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and Retrieval
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
Unified Pre-training for Program Understanding and Generation
CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation
CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays
Neural Guided Constraint Logic Programming for Program Synthesis
Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair
Improving Molecular Design by Stochastic Iterative Target Augmentation
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
Planning-Driven Programming: A Large Language Model Programming Workflow
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
Less is More: Adaptive Program Repair with Bug Localization and Preference Learning
CoSIL: Software Issue Localization via LLM-Driven Code Repository Graph Searching
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TCN: Table Convolutional Network for Web Table Interpretation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.