{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/bpe/papers/135","list_of":"/method/bpe","method":"BPE","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":135,"pages_in_order":190,"rows_per_page":100,"rows":[13401,13500],"of":18975,"counts":{"archive_papers_tagged":18975,"with_a_code_link":8675,"where_syntology_ran_a_sample":2895,"not_listed_spam_title":0,"listed":18975,"listed_where_code_ran":2895,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2443,"every_run_a_failure_of_syntologys_instrument":452,"listed_with_a_run_with_no_instrument_failure":2443,"listed_every_run_a_failure_of_syntologys_instrument":452,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/bpe","prev":"/method/bpe/papers/134","next":"/method/bpe/papers/136","papers":[{"paper":"/paper/eeny-meeny-miny-moe-how-to-choose-data-for","slug":"eeny-meeny-miny-moe-how-to-choose-data-for","title":"Eeny, meeny, miny, moe. How to choose data for morphological inflection","date":"2022-10-26","arxiv_id":"2210.14465","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-robustness-of-prefix-tuning-in","title":"Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis","date":"2022-10-26","arxiv_id":"2211.05584","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-a-task-specific-descriptor-for","title":"Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds","date":"2022-10-26","arxiv_id":"2210.14899","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-affirmative-interpretations-from","slug":"leveraging-affirmative-interpretations-from","title":"Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding","date":"2022-10-26","arxiv_id":"2210.14486","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-demonstrations-with-latent-space","slug":"leveraging-demonstrations-with-latent-space","title":"Leveraging Demonstrations with Latent Space Priors","date":"2022-10-26","arxiv_id":"2210.14685","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["facebookresearch/latent-space-priors"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"multilevel-transformer-for-multimodal-emotion","title":"Multilevel Transformer For Multimodal Emotion Recognition","date":"2022-10-26","arxiv_id":"2211.07711","n_code_links":0,"syntology":null},{"paper":"/paper/pretrained-audio-neural-networks-for-speech","slug":"pretrained-audio-neural-networks-for-speech","title":"Pretrained audio neural networks for Speech emotion recognition in Portuguese","date":"2022-10-26","arxiv_id":"2210.14716","n_code_links":1,"syntology":null},{"paper":"/paper/semformer-semantic-guided-activation","slug":"semformer-semantic-guided-activation","title":"SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation","date":"2022-10-26","arxiv_id":"2210.14618","n_code_links":1,"syntology":null},{"paper":"/paper/xiaoicesing-2-a-high-fidelity-singing-voice","slug":"xiaoicesing-2-a-high-fidelity-singing-voice","title":"Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network","date":"2022-10-26","arxiv_id":"2210.14666","n_code_links":1,"syntology":null},{"paper":"/paper/audio-mfcc-gram-transformers-for-respiratory","slug":"audio-mfcc-gram-transformers-for-respiratory","title":"Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19","date":"2022-10-25","arxiv_id":"2210.14085","n_code_links":1,"syntology":null},{"paper":null,"slug":"dynamic-survival-transformers-for-causal","title":"Dynamic Survival Transformers for Causal Inference with Electronic Health Records","date":"2022-10-25","arxiv_id":"2210.15417","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-transformer-for-compressed-video","title":"End-to-end Transformer for Compressed Video Quality Enhancement","date":"2022-10-25","arxiv_id":"2210.13827","n_code_links":0,"syntology":null},{"paper":"/paper/explicitly-increasing-input-information","slug":"explicitly-increasing-input-information","title":"Explicitly Increasing Input Information Density for Vision Transformers on Small Datasets","date":"2022-10-25","arxiv_id":"2210.14319","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":2,"n_instrument":2,"unverified":3,"pointer_only":7,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["xiangyu8/densevt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ielm-an-open-information-extraction-benchmark","title":"IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models","date":"2022-10-25","arxiv_id":"2210.14128","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowgl-knowledge-generation-and-linking-from","title":"KnowGL: Knowledge Generation and Linking from Text","date":"2022-10-25","arxiv_id":"2210.13952","n_code_links":0,"syntology":null},{"paper":"/paper/mew-unet-multi-axis-representation-learning","slug":"mew-unet-multi-axis-representation-learning","title":"MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation","date":"2022-10-25","arxiv_id":"2210.14007","n_code_links":1,"syntology":null},{"paper":null,"slug":"minutiae-guided-fingerprint-embeddings-via","title":"Minutiae-Guided Fingerprint Embeddings via Vision Transformers","date":"2022-10-25","arxiv_id":"2210.13994","n_code_links":0,"syntology":null},{"paper":"/paper/moformer-self-supervised-transformer-model","slug":"moformer-self-supervised-transformer-model","title":"MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction","date":"2022-10-25","arxiv_id":"2210.14188","n_code_links":1,"syntology":null},{"paper":"/paper/thor-net-end-to-end-graformer-based-realistic","slug":"thor-net-end-to-end-graformer-based-realistic","title":"THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision","date":"2022-10-25","arxiv_id":"2210.13853","n_code_links":1,"syntology":null},{"paper":null,"slug":"xricl-cross-lingual-retrieval-augmented-in","title":"XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing","date":"2022-10-25","arxiv_id":"2210.13693","n_code_links":0,"syntology":null},{"paper":"/paper/abductive-action-inference","slug":"abductive-action-inference","title":"Inferring Past Human Actions in Homes with Abductive Reasoning","date":"2022-10-24","arxiv_id":"2210.13984","n_code_links":1,"syntology":null},{"paper":null,"slug":"effective-pre-training-objectives-for","title":"Effective Pre-Training Objectives for Transformer-based Autoencoders","date":"2022-10-24","arxiv_id":"2210.13536","n_code_links":0,"syntology":null},{"paper":"/paper/elmer-a-non-autoregressive-pre-trained","slug":"elmer-a-non-autoregressive-pre-trained","title":"ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation","date":"2022-10-24","arxiv_id":"2210.13304","n_code_links":1,"syntology":null},{"paper":"/paper/emergent-world-representations-exploring-a","slug":"emergent-world-representations-exploring-a","title":"Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task","date":"2022-10-24","arxiv_id":"2210.13382","n_code_links":4,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["likenneth/othello_world"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/exploring-euphemism-detection-in-few-shot-and","slug":"exploring-euphemism-detection-in-few-shot-and","title":"Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings","date":"2022-10-24","arxiv_id":"2210.12926","n_code_links":1,"syntology":null},{"paper":"/paper/foreground-guidance-and-multi-layer-feature","slug":"foreground-guidance-and-multi-layer-feature","title":"Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with Transformers","date":"2022-10-24","arxiv_id":"2210.13053","n_code_links":1,"syntology":null},{"paper":"/paper/high-fidelity-neural-audio-compression","slug":"high-fidelity-neural-audio-compression","title":"High Fidelity Neural Audio Compression","date":"2022-10-24","arxiv_id":"2210.13438","n_code_links":6,"syntology":null},{"paper":"/paper/perfectly-secure-steganography-using-minimum","slug":"perfectly-secure-steganography-using-minimum","title":"Perfectly Secure Steganography Using Minimum Entropy Coupling","date":"2022-10-24","arxiv_id":"2210.14889","n_code_links":2,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["schroederdewitt/perfectly-secure-steganography"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/sequential-recommendation-with-auxiliary-item","slug":"sequential-recommendation-with-auxiliary-item","title":"Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer","date":"2022-10-24","arxiv_id":"2210.13572","n_code_links":1,"syntology":null},{"paper":"/paper/video-based-object-6d-pose-estimation-using","slug":"video-based-object-6d-pose-estimation-using","title":"Video based Object 6D Pose Estimation using Transformers","date":"2022-10-24","arxiv_id":"2210.13540","n_code_links":1,"syntology":null},{"paper":"/paper/vlc-bert-visual-question-answering-with","slug":"vlc-bert-visual-question-answering-with","title":"VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge","date":"2022-10-24","arxiv_id":"2210.13626","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["aditya10/vlc-bert"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/anticipative-feature-fusion-transformer-for","slug":"anticipative-feature-fusion-transformer-for","title":"Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation","date":"2022-10-23","arxiv_id":"2210.12649","n_code_links":1,"syntology":null},{"paper":"/paper/delving-into-masked-autoencoders-for-multi","slug":"delving-into-masked-autoencoders-for-multi","title":"Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification","date":"2022-10-23","arxiv_id":"2210.12843","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["lambert-x/medical_mae"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/holistic-interaction-transformer-network-for","slug":"holistic-interaction-transformer-network-for","title":"Holistic Interaction Transformer Network for Action Detection","date":"2022-10-23","arxiv_id":"2210.12686","n_code_links":1,"syntology":null},{"paper":"/paper/on-cross-domain-pre-trained-language-models","slug":"on-cross-domain-pre-trained-language-models","title":"Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition","date":"2022-10-23","arxiv_id":"2210.12770","n_code_links":2,"syntology":null},{"paper":null,"slug":"uia-vit-unsupervised-inconsistency-aware","title":"UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection","date":"2022-10-23","arxiv_id":"2210.12752","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comprehensive-comparison-of-neural-networks","title":"A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection","date":"2022-10-22","arxiv_id":"2210.12321","n_code_links":0,"syntology":null},{"paper":"/paper/ham-hierarchical-attention-model-with-high","slug":"ham-hierarchical-attention-model-with-high","title":"Learning Point-Language Hierarchical Alignment for 3D Visual Grounding","date":"2022-10-22","arxiv_id":"2210.12513","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ppjmchen/ham"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/leveraging-large-language-models-for-multiple","slug":"leveraging-large-language-models-for-multiple","title":"Leveraging Large Language Models for Multiple Choice Question Answering","date":"2022-10-22","arxiv_id":"2210.12353","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":3,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["byu-pccl/leveraging-llms-for-mcqa"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"meta-learning-pathologies-from-radiology","title":"Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks","date":"2022-10-22","arxiv_id":"2210.13979","n_code_links":0,"syntology":null},{"paper":null,"slug":"ms-dc-unext-an-mlp-based-multi-scale-feature","title":"MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images","date":"2022-10-22","arxiv_id":"2210.12361","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrence-boosts-diversity-revisiting","title":"Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation","date":"2022-10-22","arxiv_id":"2210.12409","n_code_links":0,"syntology":null},{"paper":"/paper/s2wat-image-style-transfer-via-hierarchical","slug":"s2wat-image-style-transfer-via-hierarchical","title":"S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention","date":"2022-10-22","arxiv_id":"2210.12381","n_code_links":1,"syntology":null},{"paper":null,"slug":"speech-emotion-recognition-via-an-attentive","title":"Speech Emotion Recognition via an Attentive Time-Frequency Neural Network","date":"2022-10-22","arxiv_id":"2210.12430","n_code_links":0,"syntology":null},{"paper":"/paper/syngec-syntax-enhanced-grammatical-error","slug":"syngec-syntax-enhanced-grammatical-error","title":"SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser","date":"2022-10-22","arxiv_id":"2210.12484","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-based-conditioned-variational","title":"Transformer-Based Conditioned Variational Autoencoder for Dialogue Generation","date":"2022-10-22","arxiv_id":"2210.12326","n_code_links":0,"syntology":null},{"paper":"/paper/a-causal-framework-to-quantify-the-robustness","slug":"a-causal-framework-to-quantify-the-robustness","title":"A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models","date":"2022-10-21","arxiv_id":"2210.12023","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":3,"n_instrument":2,"unverified":2,"pointer_only":7,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["alestolfo/causal-math"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/amos-an-adam-style-optimizer-with-adaptive","slug":"amos-an-adam-style-optimizer-with-adaptive","title":"Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale","date":"2022-10-21","arxiv_id":"2210.11693","n_code_links":1,"syntology":{"ran":14,"of":18,"n_ran_checked":14,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["google-research/jestimator"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/context-enhanced-stereo-transformer","slug":"context-enhanced-stereo-transformer","title":"Context-Enhanced Stereo Transformer","date":"2022-10-21","arxiv_id":"2210.11719","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["guoweiyu/context-enhanced-stereo-transformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/decoding-a-neural-retriever-s-latent-space","slug":"decoding-a-neural-retriever-s-latent-space","title":"Decoding a Neural Retriever's Latent Space for Query Suggestion","date":"2022-10-21","arxiv_id":"2210.12084","n_code_links":1,"syntology":null},{"paper":"/paper/diffuser-efficient-transformers-with-multi","slug":"diffuser-efficient-transformers-with-multi","title":"Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences","date":"2022-10-21","arxiv_id":"2210.11794","n_code_links":1,"syntology":null},{"paper":"/paper/do-vision-and-language-transformers-learn","slug":"do-vision-and-language-transformers-learn","title":"Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?","date":"2022-10-21","arxiv_id":"2210.12079","n_code_links":1,"syntology":null},{"paper":"/paper/face-pyramid-vision-transformer","slug":"face-pyramid-vision-transformer","title":"Face Pyramid Vision Transformer","date":"2022-10-21","arxiv_id":"2210.11974","n_code_links":1,"syntology":null},{"paper":null,"slug":"is-encoder-decoder-redundant-for-neural","title":"Is Encoder-Decoder Redundant for Neural Machine Translation?","date":"2022-10-21","arxiv_id":"2210.11807","n_code_links":0,"syntology":null},{"paper":"/paper/shift-reduce-task-oriented-semantic-parsing","slug":"shift-reduce-task-oriented-semantic-parsing","title":"Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers","date":"2022-10-21","arxiv_id":"2210.11984","n_code_links":1,"syntology":null},{"paper":"/paper/sling-sino-linguistic-evaluation-of-large","slug":"sling-sino-linguistic-evaluation-of-large","title":"SLING: Sino Linguistic Evaluation of Large Language Models","date":"2022-10-21","arxiv_id":"2210.11689","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["yixiao-song/sling_data_code"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/syntax-guided-localized-self-attention-by","slug":"syntax-guided-localized-self-attention-by","title":"Syntax-guided Localized Self-attention by Constituency Syntactic Distance","date":"2022-10-21","arxiv_id":"2210.11759","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["lumia-group/distance_transformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/translist-a-transformer-based-linguistically","slug":"translist-a-transformer-based-linguistically","title":"TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer","date":"2022-10-21","arxiv_id":"2210.11753","n_code_links":1,"syntology":null},{"paper":null,"slug":"university-of-cape-town-s-wmt22-system","title":"University of Cape Town's WMT22 System: Multilingual Machine Translation for Southern African Languages","date":"2022-10-21","arxiv_id":"2210.11757","n_code_links":0,"syntology":null},{"paper":null,"slug":"wikiwhy-answering-and-explaining-cause-and","title":"WikiWhy: Answering and Explaining Cause-and-Effect Questions","date":"2022-10-21","arxiv_id":"2210.12152","n_code_links":0,"syntology":null},{"paper":null,"slug":"3dall-e-integrating-text-to-image-ai-in-3d","title":"3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows","date":"2022-10-20","arxiv_id":"2210.11603","n_code_links":0,"syntology":null},{"paper":"/paper/composing-ensembles-of-pre-trained-models-via","slug":"composing-ensembles-of-pre-trained-models-via","title":"Composing Ensembles of Pre-trained Models via Iterative Consensus","date":"2022-10-20","arxiv_id":"2210.11522","n_code_links":0,"syntology":null},{"paper":"/paper/general-image-descriptors-for-open-world","slug":"general-image-descriptors-for-open-world","title":"General Image Descriptors for Open World Image Retrieval using ViT CLIP","date":"2022-10-20","arxiv_id":"2210.11141","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ivanaer/g-universal-clip"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"identifying-human-strategies-for-generating","title":"Identifying Human Strategies for Generating Word-Level Adversarial Examples","date":"2022-10-20","arxiv_id":"2210.11598","n_code_links":0,"syntology":null},{"paper":"/paper/scaling-instruction-finetuned-language-models","slug":"scaling-instruction-finetuned-language-models","title":"Scaling Instruction-Finetuned Language Models","date":"2022-10-20","arxiv_id":"2210.11416","n_code_links":9,"syntology":{"ran":8,"of":17,"n_ran_checked":1,"n_instrument":7,"unverified":9,"pointer_only":2,"phrase":"8 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 9 unverified","official":null}},{"paper":"/paper/self-supervised-learning-with-masked-image","slug":"self-supervised-learning-with-masked-image","title":"Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs","date":"2022-10-20","arxiv_id":"2210.11404","n_code_links":1,"syntology":null},{"paper":"/paper/simpleclick-interactive-image-segmentation","slug":"simpleclick-interactive-image-segmentation","title":"SimpleClick: Interactive Image Segmentation with Simple Vision Transformers","date":"2022-10-20","arxiv_id":"2210.11006","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["uncbiag/simpleclick"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"single-image-super-resolution-using-2","title":"Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer","date":"2022-10-20","arxiv_id":"2210.11019","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-reasoning-tasks-with-a-slot","title":"Solving Reasoning Tasks with a Slot Transformer","date":"2022-10-20","arxiv_id":"2210.11394","n_code_links":0,"syntology":null},{"paper":"/paper/ssit-saliency-guided-self-supervised-image","slug":"ssit-saliency-guided-self-supervised-image","title":"SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading","date":"2022-10-20","arxiv_id":"2210.10969","n_code_links":1,"syntology":null},{"paper":"/paper/a-unified-view-of-masked-image-modeling","slug":"a-unified-view-of-masked-image-modeling","title":"A Unified View of Masked Image Modeling","date":"2022-10-19","arxiv_id":"2210.10615","n_code_links":1,"syntology":null},{"paper":"/paper/biogpt-generative-pre-trained-transformer-for","slug":"biogpt-generative-pre-trained-transformer-for","title":"BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining","date":"2022-10-19","arxiv_id":"2210.10341","n_code_links":4,"syntology":null},{"paper":null,"slug":"grounded-video-situation-recognition","title":"Grounded Video Situation Recognition","date":"2022-10-19","arxiv_id":"2210.10828","n_code_links":0,"syntology":null},{"paper":"/paper/language-detoxification-with-attribute","slug":"language-detoxification-with-attribute","title":"Language Detoxification with Attribute-Discriminative Latent Space","date":"2022-10-19","arxiv_id":"2210.10329","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-view-gait-recognition-based-on-siamese","title":"Multi-view Gait Recognition based on Siamese Vision Transformer","date":"2022-10-19","arxiv_id":"2210.10421","n_code_links":0,"syntology":null},{"paper":"/paper/museformer-transformer-with-fine-and-coarse","slug":"museformer-transformer-with-fine-and-coarse","title":"Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation","date":"2022-10-19","arxiv_id":"2210.10349","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["microsoft/muzic"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/posegpt-quantization-based-3d-human-motion","slug":"posegpt-quantization-based-3d-human-motion","title":"PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting","date":"2022-10-19","arxiv_id":"2210.10542","n_code_links":1,"syntology":null},{"paper":"/paper/revision-transformers-getting-rit-of-no-nos","slug":"revision-transformers-getting-rit-of-no-nos","title":"Revision Transformers: Instructing Language Models to Change their Values","date":"2022-10-19","arxiv_id":"2210.10332","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-graph-masking-pre-training","slug":"self-supervised-graph-masking-pre-training","title":"Self-supervised Graph Masking Pre-training for Graph-to-Text Generation","date":"2022-10-19","arxiv_id":"2210.10599","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-a-neural-architecture-of-language","title":"Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processing","date":"2022-10-19","arxiv_id":"2210.10543","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformers-learn-shortcuts-to-automata","title":"Transformers Learn Shortcuts to Automata","date":"2022-10-19","arxiv_id":"2210.10749","n_code_links":0,"syntology":null},{"paper":"/paper/a-hybrid-system-of-sound-event-detection","slug":"a-hybrid-system-of-sound-event-detection","title":"A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4","date":"2022-10-18","arxiv_id":"2210.09529","n_code_links":1,"syntology":null},{"paper":"/paper/cross-domain-aspect-extraction-using","slug":"cross-domain-aspect-extraction-using","title":"Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs","date":"2022-10-18","arxiv_id":"2210.10144","n_code_links":1,"syntology":null},{"paper":"/paper/ctgan-cloud-transformer-generative","slug":"ctgan-cloud-transformer-generative","title":"CTGAN : Cloud Transformer Generative Adversarial Network","date":"2022-10-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"from-play-to-policy-conditional-behavior","title":"From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data","date":"2022-10-18","arxiv_id":"2210.10047","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-image-fusion-based-on-hybrid-cnn","slug":"multimodal-image-fusion-based-on-hybrid-cnn","title":"Multimodal Image Fusion based on Hybrid CNN-Transformer and Non-local Cross-modal Attention","date":"2022-10-18","arxiv_id":"2210.09847","n_code_links":1,"syntology":null},{"paper":"/paper/swinv2-imagen-hierarchical-vision-transformer","slug":"swinv2-imagen-hierarchical-vision-transformer","title":"Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation","date":"2022-10-18","arxiv_id":"2210.09549","n_code_links":0,"syntology":null},{"paper":null,"slug":"systematicity-in-gpt-3-s-interpretation-of","title":"Systematicity in GPT-3's Interpretation of Novel English Noun Compounds","date":"2022-10-18","arxiv_id":"2210.09492","n_code_links":0,"syntology":null},{"paper":null,"slug":"team-flow-at-drc2022-pipeline-system-for","title":"Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue","date":"2022-10-18","arxiv_id":"2210.09518","n_code_links":0,"syntology":null},{"paper":null,"slug":"tiny-attention-adapter-contexts-are-more","title":"Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters","date":"2022-10-18","arxiv_id":"2211.01979","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-for-video-classification","title":"Transfer-learning for video classification: Video Swin Transformer on multiple domains","date":"2022-10-18","arxiv_id":"2210.09969","n_code_links":0,"syntology":null},{"paper":"/paper/vitcod-vision-transformer-acceleration-via","slug":"vitcod-vision-transformer-acceleration-via","title":"ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design","date":"2022-10-18","arxiv_id":"2210.09573","n_code_links":1,"syntology":null},{"paper":"/paper/a-generative-user-simulator-with-gpt-based","slug":"a-generative-user-simulator-with-gpt-based","title":"A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems","date":"2022-10-17","arxiv_id":"2210.08692","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thu-spmi/gus"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-mixing-time-lower-bound-for-a-simplified","title":"A Mixing Time Lower Bound for a Simplified Version of BART","date":"2022-10-17","arxiv_id":"2210.09352","n_code_links":0,"syntology":null},{"paper":"/paper/deep-bidirectional-language-knowledge-graph","slug":"deep-bidirectional-language-knowledge-graph","title":"Deep Bidirectional Language-Knowledge Graph Pretraining","date":"2022-10-17","arxiv_id":"2210.09338","n_code_links":2,"syntology":{"ran":9,"of":18,"n_ran_checked":8,"n_instrument":1,"unverified":9,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 9 unverified","official":{"repos":["michiyasunaga/dragon"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":"/paper/histopathological-image-classification-based","slug":"histopathological-image-classification-based","title":"Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels","date":"2022-10-17","arxiv_id":"2210.09021","n_code_links":1,"syntology":null},{"paper":"/paper/prompting-gpt-3-to-be-reliable","slug":"prompting-gpt-3-to-be-reliable","title":"Prompting GPT-3 To Be Reliable","date":"2022-10-17","arxiv_id":"2210.09150","n_code_links":1,"syntology":null},{"paper":null,"slug":"sgram-improving-scene-graph-parsing-via","title":"SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation","date":"2022-10-17","arxiv_id":"2210.08675","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-shot-ranking-socio-political-texts-with","title":"Zero-Shot Ranking Socio-Political Texts with Transformer Language Models to Reduce Close Reading Time","date":"2022-10-17","arxiv_id":"2210.09179","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-transfer-learning-with-near-data","slug":"accelerating-transfer-learning-with-near-data","title":"Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores","date":"2022-10-16","arxiv_id":"2210.08650","n_code_links":1,"syntology":null}],"record_sha256":"25e2c9a0ac1402046866832eba2549376eef0f20853e181329bff75d9f4382ac","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}