Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis | Paper Citation Record

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This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Chatgpt for good? on opportunities and challenges of large language models for education,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Large language models in healthcare and medical domain: A review,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Revolutionizing finance with llms: An overview of applications and insights,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Gpt-4 technical report,

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This paper cites CVE-2024-3568: Remote Code Execution Vulnerability in Hugging Face Transformers,.

Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis CVE-2024-3568: Remote Code Execution Vulnerability in Hugging Face Transformers,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Hugging face model hub statistics,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Tiktok owner sacks intern for sabotaging ai project,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Cradle: cross-backend validation to detect and localize bugs in deep learning libraries,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Deep learning library testing via effective model generation,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Audee: Automated testing for deep learning frameworks,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Comet: Coverage-guided model generation for deep learning library testing,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Free lunch for testing: Fuzzing deep-learning libraries from open source,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Devmut: Testing deep learning framework via developer expertise-based mutation,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Improving deep learning framework testing with model-level metamorphic testing,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Toward understanding deep learning framework bugs,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis The symptoms, causes, and repairs of bugs inside a deep learning library,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Silent bugs in deep learning frameworks: an empirical study of keras and tensorflow,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Investigating and detecting silent bugs in pytorch programs,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis An empirical study on program failures of deep learning jobs,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis What do programmers discuss about deep learning frameworks,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Automatic unit test generation for machine learning libraries: How far are we?

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Evaluating spectrum-based fault localization on deep learning libraries,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis N-gram statistics for natural language understanding and text processing,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis A tutorial on hidden markov models and selected applications in speech recognition,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis A Critical Review of Recurrent Neural Networks for Sequence Learning

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Learning to forget: Continual prediction with lstm,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Attention is all you need,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis A Survey of Large Language Models

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Improving language understanding by generative pre-training,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Language models are unsupervised multitask learners,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Language models are few-shot learners,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Exploring the limits of transfer learning with a unified text-to-text transformer,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Xlnet: Generalized autoregressive pretraining for language understanding,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Reading wikipedia to answer open-domain questions,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Convolutional neural networks for sentence classification,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Textrank: Bringing order into text,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Get To The Point: Summarization with Pointer-Generator Networks

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Deep residual learning for image recognition,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Learning both weights and connections for efficient neural networks,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Tensorflow model optimization toolkit,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Pruning Convolutional Neural Networks for Resource Efficient Inference

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Knowledge distillation: A survey,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mixed Precision Training

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Optimization of deep learning inference on edge devices,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Learning phrase representations using rnn encoder-decoder for statistical machine translation,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Neural machine translation by jointly learning to align and translate,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Glove: Global vectors for word representation,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Enriching word vectors with subword information,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Unresolved cited work

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Unresolved cited work

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Beyond data and model parallelism for deep neural networks

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Large scale distributed deep networks,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Parallel and distributed deep learning,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Scaling distributed machine learning with the parameter server,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Memory-efficient backpropagation through time,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mindspore lite: A lightweight ai inference framework for edge devices,

Reference 62

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Deepdriving: Learning affordance for direct perception in autonomous driving,

Reference 63

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

Reference 64

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis A comprehensive study on challenges in deploying deep learning based software,

Reference 65

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Stable architectures for deep neural networks,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Ai slop: Github devs swamped by fake bug reports made by artificial morons,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Pytorch,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Tensorflow,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mindspore,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Deepspeed,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Megatron-lm,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Tensorrt-llm,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Colossai,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mindspeed,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mindnlp,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Mindformers,

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Data avaiable,

Reference 80

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Predicting defects for eclipse,

Reference 81

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Automatic topic naming to support cross-project analysis of software maintenance activities,

Reference 82

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Failures and fixes: A study of software system incident response,

Reference 83

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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis Bug taxonomies: Use them to generate better tests,

Reference 84

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