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Atlas of AI: power, politics, and the planetary costs of artificial intelligence.
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BEiT: BERT Pre-Training of Image Transformers
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MERLOT: Multimodal Neural Script Knowledge Models
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Measuring Coding Challenge Competence With APPS
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Women’s Participation in Open Source Software: A Survey of the Literature
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Carbon Emissions and Large Neural Network Training
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Generating bug-fixes using pretrained transformers
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Alignment of Language Agents
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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Learning Transferable Visual Models From Natural Language Supervision
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Zero-Shot Text-to-Image Generation
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Calibrate Before Use: Improving Few-Shot Performance of Language Models
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
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In-IDE Code Generation from Natural Language: Promise and Challenges
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Persistent Anti-Muslim Bias in Large Language Models
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
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Extracting Training Data from Large Language Models
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Learning Autocompletion from Real-World Datasets
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PyMT5: Multi-mode Translation of Natural Language and Python Code with Transformers
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CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
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Unit Test Case Generation with Transformers
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Learning to summarize from human feedback
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Generative Pretraining From Pixels
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Contrastive Code Representation Learning
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You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
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wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
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DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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Unsupervised Translation of Programming Languages
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Language Models are Few-Shot Learners
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP
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SourceFinder: Finding Malware Source-Code from Publicly Available Repositories
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Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks
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Jukebox: A Generative Model for Music
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Recalibrating global data center energy-use estimates
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
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5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
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Scaling Laws for Neural Language Models
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What distinguishes great software engineers?
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Unified rational protein engineering with sequence-based deep representation learning
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CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
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CTRL: A Conditional Transformer Language Model for Controllable Generation
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Automatic programming: The open issue?
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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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SPoC: Search-based Pseudocode to Code
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Protecting Visual Information in Augmented Reality from Malicious Application Developers
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Learning Compositional Neural Programs with Recursive Tree Search and Planning
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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Generating Long Sequences with Sparse Transformers
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The Curious Case of Neural Text Degeneration
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The Wrong Kind of Ai? Artificial Intelligence and the Future of Labor Demand
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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
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Handbook of Applied Cryptography
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Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
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code2seq: Generating Sequences from Structured Representations of Code
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Representation Learning with Contrastive Predictive Coding
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Deep Contextualized Word Representations
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On the difficulty of benchmarking inductive program synthesis methods
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A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation
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Attention is All you Need
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RobustFill: Neural Program Learning under Noisy I/O
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Robots and Jobs: Evidence from US Labor Markets
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DeepCoder: Learning to Write Programs
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Hybrid computing using a neural network with dynamic external memory
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WaveNet: A Generative Model for Raw Audio
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Latent Predictor Networks for Code Generation
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Pixel Recurrent Neural Networks
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Neural GPUs Learn Algorithms
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Neural Programmer-Interpreters
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Semi-supervised Sequence Learning
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General Program Synthesis Benchmark Suite
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Bimodal Modelling of Source Code and Natural Language
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An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
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Representation Learning
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End-To-End Memory Networks
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Sequence to Sequence Learning with Neural Networks
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Structured Generative Models of Natural Source Code
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Distributed Representations of Words and Phrases and their Compositionality
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Generating Sequences With Recurrent Neural Networks
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Temporal Logics for Hyperproperties
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Spreadsheet data manipulation using examples
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A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each
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On the naturalness of software
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The Economics of Software Quality
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Automating string processing in spreadsheets using input-output examples
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BugFix: A learning-based tool to assist developers in fixing bugs
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Predictive Resource Management for Wearable Computing
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Genetic programming III: Darwinian invention and problem solving [Book Review]
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Application of Dynamic Slicing in Program Debugging
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Fault localization using execution slices and dataflow tests
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Toward automatic program synthesis
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Experiments with a Heuristic Compiler
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
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A first look at rote learning in github copilot suggestions., Jun 2021. URL https://docs.github.com/en/ github/copilot/research-recitation
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Supplemental Bias Analysis Generative models have been shown to encode bias in modalities such as natural language (Brown et al., 2020; Blodgett et al., 2020) and images (Radford et al., 2021)
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Fun and dystopia with ai-based code generation us-ing gpt-j-6b
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2020) for an analysis of conventional language models
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Python Software Foundation and JetBrains
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Working in public: the making and maintenance of open source software
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formers is to fine-tune large pre-trained models with curated or human-generated datasets of the desired behavior (e.g., Raffel et al
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Language Models are Unsupervised Multitask Learners
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Comment regarding request for comments on intellectual property protection for artificial intelligence innovation
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Improving the standard risk matrix: Part 1. 2019
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Improving Language Understanding by Generative Pre-Training
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Improving Neural Program Synthesis with Inferred Execution Traces
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Protecting applications with automated software diversity, Sep 2018
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Open-sourcing gvisor, a sandboxed container runtime, 2018
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A6:2017-security misconfiguration
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A syntactic neural model for generalpurpose code generation
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Cwe-780: Use of rsa algorithm without oaep
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Cwe-327: Use of a broken or risky cryptographic algorithm
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The economic impacts of inadequate infrastructure for software testing
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Online; accessed 29-June-2000
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Lecture 3: Nondeterministic computation
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Clarifying ”ai alignment”
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Evaluating Large Language usenixsecurity21/presentation/carlini-extracting
