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Paper Citation Record · LEDGER
As of 14 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 2 inbound Pith citation observations for arXiv:2412.14373.
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82 of 82 outbound references displayed
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Observation af14f367-45f6-493f-8ac0-195be67bd773 · outbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Reference 1
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models
Reference 2
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Reference 3
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Observation 9cd891f3-85b3-401c-9f9b-6461d029fc22 · outbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Fuyu-8b: A multimodal architecture for ai agents, 10 2023
Reference 4
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Language Models are Few-Shot Learners
Reference 5
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Fleet, and Geoffrey Hinton
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Yhdego, Fatemeh Khadem, and Fatemeh Afghah
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Fading focus: Mitigating visual attention degradation in large vision-language models, 2024
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Observation daf2677f-e8ca-452f-aecf-888f50a1476e · outbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Getting the most out of your tokenizer for pre-training and domain adaptation
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Observation 6a857d5a-55b1-403a-80f5-b2ab4131420e · outbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Reference 12
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Unveiling Encoder-Free Vision-Language Models
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Reference 14
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling A density-based algorithm for discovering clusters in large spatial databases with noise
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Cardiogpt: An ecg interpretation generation model
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling A new algorithm for data compression
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling 3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Mimic-iv-ecg: Diagnostic electrocardiogram matched subset, 2023
Reference 20
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling The Llama 3 Herd of Models
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Training Vision-Language Transformers from Captions
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Interpretation of Intracardiac Electrograms Through Textual Representations
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling LoRA: Low-Rank Adaptation of Large Language Models
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Reference 27
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
Reference 28
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Unresolved cited work
Reference 29
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Counties most in need of cardiologists are the most likely to have none, 07 2024
Reference 30
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Automated diagnostic reports from images of electrocardiograms at the point-of-care
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Kingma and Jimmy Ba
Reference 32
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Observation 17b9f42d-3b8e-4c80-8bc7-0875e9f408c0 · outbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Reference 33
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Byte-pair encoding for classifying routine clinical electroencephalograms in adults over the lifespan
Reference 34
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling A simple weight decay can improve generalization
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Reference 38
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Improved Baselines with Visual Instruction Tuning
Reference 40
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Teach Multimodal LLMs to Comprehend Electrocardiographic Images
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Some methods for classification and analysis of multivariate observations
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling ECG-FM: An Open Electrocardiogram Foundation Model
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
Reference 45
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Electrocardiogram classification by modified efficientnet with data augmentation
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Reference 49
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Reference 51
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Reference 52
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Rosenberg, Emerson Liu, Douglas Weber, and Ding Zhao
Reference 53
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Reference 54
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Reference 56
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling "Why Should I Trust You?": Explaining the Predictions of Any Classifier
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning
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Reference 68
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Reference 69
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Reference 70
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Reference 71
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Reference 72
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Reference 73
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Reference 74
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Reference 76
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 77
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Reference 78
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Reference 79
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Reference 80
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Reference 81
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ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
Reference 82
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Observation 5bd3f457-ba4f-4686-b92a-5c34d1ba9734 · inbound
Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
Reference 13
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Observation 040a0b35-e84c-4a4b-ab54-96ece78925f5 · inbound
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
Reference 40
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