deploy: 59e61a2

Author: mallamanis

paper-abstracts.json

@@ -1 +1 @@
-[["bhatia2016automated", "Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks"], ["wu2021prototransformer", "ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback"], ["hellendoorn2018deep", "Deep Learning Type Inference"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"]]
+[["bhatia2016automated", "Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks"], ["hellendoorn2018deep", "Deep Learning Type Inference"], ["wu2021prototransformer", "ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"]]

papers.html

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-[["svyatkovskiy2020fast", "Fast and Memory-Efficient Neural Code Completion"], ["li2017code", "Code Completion with Neural Attention and Pointer Networks"], ["bhoopchand2016learning", "Learning Python Code Suggestion with a Sparse Pointer Network"], ["li2021toward", "Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models"]]
+[["svyatkovskiy2020fast", "Fast and Memory-Efficient Neural Code Completion"], ["li2017code", "Code Completion with Neural Attention and Pointer Networks"], ["li2021toward", "Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models"], ["bhoopchand2016learning", "Learning Python Code Suggestion with a Sparse Pointer Network"]]

publications-metadata/ahmed2019learning.json

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+[["parisi2022making", "Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping"], ["brauckmann2020compiler", "Compiler-based graph representations for deep learning models of code"], ["barchi2022deep", "Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"]]

publications-metadata/aye2020sequence.json

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+[["sun2019grammar", "A Grammar-Based Structural CNN Decoder for Code Generation"], ["bui2018cross", "Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks"], ["bui2018bilateral", "Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification"], ["barchi2022deep", "Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities"]]

publications-metadata/barchi2019code.json

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+[["parisi2021source", "Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers"], ["barchi2019code", "Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR"], ["parisi2022making", "Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping"], ["brauckmann2020compiler", "Compiler-based graph representations for deep learning models of code"]]

publications-metadata/barchi2021exploration.json

@@ -1 +1 @@
-[["bhatia2016automated", "Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks"], ["pu2016skp", "sk_p: a neural program corrector for MOOCs"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"], ["piech2015learning", "Learning Program Embeddings to Propagate Feedback on Student Code"]]
+[["bhatia2016automated", "Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks"], ["pu2016skp", "sk_p: a neural program corrector for MOOCs"], ["piech2015learning", "Learning Program Embeddings to Propagate Feedback on Student Code"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"]]

publications-metadata/barchi2022deep.json

@@ -1 +1 @@
-[["gupta2017deepfix", "DeepFix: Fixing Common C Language Errors by Deep Learning"], ["bouzenia2023tracefixer", "TraceFixer: Execution Trace-Driven Program Repair"], ["wang2020blended", "Blended, precise semantic program embeddings"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"]]
+[["gupta2017deepfix", "DeepFix: Fixing Common C Language Errors by Deep Learning"], ["bouzenia2023tracefixer", "TraceFixer: Execution Trace-Driven Program Repair"], ["santos2018syntax", "Syntax and Sensibility: Using language models to detect and correct syntax errors"], ["wang2020blended", "Blended, precise semantic program embeddings"]]

publications-metadata/bhatia2018neurosymbolic.json

@@ -1 +1 @@
-[["cummins2018compiler", "Compiler Fuzzing through Deep Learning"], ["mammadli2020static", "Static Neural Compiler Optimization via Deep Reinforcement Learning"], ["brauckmann2020compy", "ComPy-Learn: A toolbox for exploring machine learning representations for compilers"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"]]
+[["cummins2018compiler", "Compiler Fuzzing through Deep Learning"], ["brauckmann2020compy", "ComPy-Learn: A toolbox for exploring machine learning representations for compilers"], ["mammadli2020static", "Static Neural Compiler Optimization via Deep Reinforcement Learning"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"]]