{"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/131","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":131,"pages_in_order":190,"rows_per_page":100,"rows":[13001,13100],"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/130","next":"/method/bpe/papers/132","papers":[{"paper":null,"slug":"teaching-small-language-models-to-reason","title":"Teaching Small Language Models to Reason","date":"2022-12-16","arxiv_id":"2212.08410","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-long-sequence-modeling-via-state","slug":"efficient-long-sequence-modeling-via-state","title":"Efficient Long Sequence Modeling via State Space Augmented Transformer","date":"2022-12-15","arxiv_id":"2212.08136","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["microsoft/efficientlongsequencemodeling"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/fido-fusion-in-decoder-optimized-for-stronger","slug":"fido-fusion-in-decoder-optimized-for-stronger","title":"FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference","date":"2022-12-15","arxiv_id":"2212.08153","n_code_links":0,"syntology":null},{"paper":null,"slug":"hum3dil-semi-supervised-multi-modal-3d-human","title":"HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving","date":"2022-12-15","arxiv_id":"2212.07729","n_code_links":0,"syntology":null},{"paper":"/paper/image-and-language-understanding-from-pixels","slug":"image-and-language-understanding-from-pixels","title":"CLIPPO: Image-and-Language Understanding from Pixels Only","date":"2022-12-15","arxiv_id":"2212.08045","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-the-gold-standard-grounding","slug":"revisiting-the-gold-standard-grounding","title":"Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation","date":"2022-12-15","arxiv_id":"2212.07981","n_code_links":2,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["yale-lily/rose"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scaffold-based-multi-objective-drug-candidate","title":"Scaffold-Based Multi-Objective Drug Candidate Optimization","date":"2022-12-15","arxiv_id":"2301.07175","n_code_links":0,"syntology":null},{"paper":null,"slug":"sim-to-real-transfer-for-quadrupedal","title":"Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer","date":"2022-12-15","arxiv_id":"2212.07740","n_code_links":0,"syntology":null},{"paper":"/paper/visually-augmented-pretrained-language-models","slug":"visually-augmented-pretrained-language-models","title":"Visually-augmented pretrained language models for NLP tasks without images","date":"2022-12-15","arxiv_id":"2212.07937","n_code_links":1,"syntology":null},{"paper":null,"slug":"dual-branch-cross-patch-attention-learning","title":"Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition","date":"2022-12-14","arxiv_id":"2212.07055","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-byte-and-wordpiece-level-models","title":"Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing","date":"2022-12-14","arxiv_id":"2212.07223","n_code_links":0,"syntology":null},{"paper":null,"slug":"vtcc-nlp-at-nl4opt-competition-subtask-1-an","title":"VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition","date":"2022-12-14","arxiv_id":"2212.07219","n_code_links":0,"syntology":null},{"paper":"/paper/crepe-can-vision-language-foundation-models","slug":"crepe-can-vision-language-foundation-models","title":"CREPE: Can Vision-Language Foundation Models Reason Compositionally?","date":"2022-12-13","arxiv_id":"2212.07796","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["raivnlab/crepe"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generative-artificial-intelligence-enabled","title":"Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits","date":"2022-12-13","arxiv_id":"2212.06330","n_code_links":0,"syntology":null},{"paper":"/paper/gpvit-a-high-resolution-non-hierarchical","slug":"gpvit-a-high-resolution-non-hierarchical","title":"GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation","date":"2022-12-13","arxiv_id":"2212.06795","n_code_links":2,"syntology":null},{"paper":null,"slug":"paraphrase-identification-with-deep-learning","title":"Paraphrase Identification with Deep Learning: A Review of Datasets and Methods","date":"2022-12-13","arxiv_id":"2212.06933","n_code_links":0,"syntology":null},{"paper":"/paper/rt-1-robotics-transformer-for-real-world","slug":"rt-1-robotics-transformer-for-real-world","title":"RT-1: Robotics Transformer for Real-World Control at Scale","date":"2022-12-13","arxiv_id":"2212.06817","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-research/robotics_transformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"smart-journey-in-istanbul-a-mobile","title":"Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series","date":"2022-12-13","arxiv_id":"2212.09448","n_code_links":0,"syntology":null},{"paper":"/paper/automated-icd-coding-using-extreme-multi","slug":"automated-icd-coding-using-extreme-multi","title":"Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models","date":"2022-12-12","arxiv_id":"2212.05857","n_code_links":1,"syntology":null},{"paper":"/paper/beautyrec-robust-efficient-and-content","slug":"beautyrec-robust-efficient-and-content","title":"BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer","date":"2022-12-12","arxiv_id":"2212.05855","n_code_links":0,"syntology":null},{"paper":"/paper/ctt-net-a-multi-view-cross-token-transformer","slug":"ctt-net-a-multi-view-cross-token-transformer","title":"CTT-Net: A Multi-view Cross-token Transformer for Cataract Postoperative Visual Acuity Prediction","date":"2022-12-12","arxiv_id":"2212.05794","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-approaches-to-building-rooftop","slug":"deep-learning-approaches-to-building-rooftop","title":"Deep learning approaches to building rooftop thermal bridge detection from aerial images","date":"2022-12-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/nms-strikes-back","slug":"nms-strikes-back","title":"NMS Strikes Back","date":"2022-12-12","arxiv_id":"2212.06137","n_code_links":1,"syntology":null},{"paper":"/paper/p-transformer-towards-better-document-to","slug":"p-transformer-towards-better-document-to","title":"P-Transformer: Towards Better Document-to-Document Neural Machine Translation","date":"2022-12-12","arxiv_id":"2212.05830","n_code_links":1,"syntology":null},{"paper":null,"slug":"roiformer-semantic-aware-region-of-interest","title":"ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation","date":"2022-12-12","arxiv_id":"2212.05729","n_code_links":0,"syntology":null},{"paper":"/paper/t5score-discriminative-fine-tuning-of","slug":"t5score-discriminative-fine-tuning-of","title":"T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics","date":"2022-12-12","arxiv_id":"2212.05726","n_code_links":2,"syntology":null},{"paper":"/paper/video-prediction-by-efficient-transformers","slug":"video-prediction-by-efficient-transformers","title":"Video Prediction by Efficient Transformers","date":"2022-12-12","arxiv_id":"2212.06026","n_code_links":1,"syntology":{"ran":0,"of":7,"n_ran_checked":0,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"0 ran · 7 unverified","official":{"repos":["xiye20/vptr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":[]}}},{"paper":"/paper/elixir-train-a-large-language-model-on-a","slug":"elixir-train-a-large-language-model-on-a","title":"Elixir: Train a Large Language Model on a Small GPU Cluster","date":"2022-12-10","arxiv_id":"2212.05339","n_code_links":2,"syntology":null},{"paper":"/paper/joint-spatio-temporal-modeling-for-semantic","slug":"joint-spatio-temporal-modeling-for-semantic","title":"Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images","date":"2022-12-10","arxiv_id":"2212.05245","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["ggsding/scannet"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":null,"slug":"machine-intuition-uncovering-human-like","title":"Thinking Fast and Slow in Large Language Models","date":"2022-12-10","arxiv_id":"2212.05206","n_code_links":0,"syntology":null},{"paper":"/paper/magvit-masked-generative-video-transformer","slug":"magvit-masked-generative-video-transformer","title":"MAGVIT: Masked Generative Video Transformer","date":"2022-12-10","arxiv_id":"2212.05199","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":9,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-research/magvit"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/position-embedding-needs-an-independent-layer","slug":"position-embedding-needs-an-independent-layer","title":"Position Embedding Needs an Independent Layer Normalization","date":"2022-12-10","arxiv_id":"2212.05262","n_code_links":1,"syntology":null},{"paper":"/paper/punctuation-restoration-for-singaporean","slug":"punctuation-restoration-for-singaporean","title":"Punctuation Restoration for Singaporean Spoken Languages: English, Malay, and Mandarin","date":"2022-12-10","arxiv_id":"2212.05356","n_code_links":1,"syntology":null},{"paper":null,"slug":"smile-scaling-mixture-of-experts-with","title":"SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing","date":"2022-12-10","arxiv_id":"2212.05191","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-information-extraction-from","title":"Structured information extraction from complex scientific text with fine-tuned large language models","date":"2022-12-10","arxiv_id":"2212.05238","n_code_links":0,"syntology":null},{"paper":"/paper/augnet-dynamic-test-time-augmentation-via","slug":"augnet-dynamic-test-time-augmentation-via","title":"Dynamic Test-Time Augmentation via Differentiable Functions","date":"2022-12-09","arxiv_id":"2212.04681","n_code_links":1,"syntology":null},{"paper":null,"slug":"masked-lip-sync-prediction-by-audio-visual","title":"Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers","date":"2022-12-09","arxiv_id":"2212.04970","n_code_links":0,"syntology":null},{"paper":"/paper/mimo-is-all-you-need-a-strong-multi-in-multi","slug":"mimo-is-all-you-need-a-strong-multi-in-multi","title":"MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction","date":"2022-12-09","arxiv_id":"2212.04655","n_code_links":1,"syntology":null},{"paper":null,"slug":"omnihorizon-in-the-wild-outdoors-depth-and","title":"Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding","date":"2022-12-09","arxiv_id":"2212.05040","n_code_links":0,"syntology":null},{"paper":"/paper/rcdt-relational-remote-sensing-change","slug":"rcdt-relational-remote-sensing-change","title":"RCDT: Relational Remote Sensing Change Detection with Transformer","date":"2022-12-09","arxiv_id":"2212.04869","n_code_links":1,"syntology":null},{"paper":"/paper/sparse-upcycling-training-mixture-of-experts","slug":"sparse-upcycling-training-mixture-of-experts","title":"Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints","date":"2022-12-09","arxiv_id":"2212.05055","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-research/vmoe"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-turing-deception","title":"The Turing Deception","date":"2022-12-09","arxiv_id":"2212.06721","n_code_links":0,"syntology":null},{"paper":null,"slug":"trbllmaker-transformer-reads-between-lyrics","title":"TRBLLmaker -- Transformer Reads Between Lyrics Lines maker","date":"2022-12-09","arxiv_id":"2212.04917","n_code_links":0,"syntology":null},{"paper":"/paper/explain-to-me-like-i-am-five-sentence","slug":"explain-to-me-like-i-am-five-sentence","title":"Explain to me like I am five -- Sentence Simplification Using Transformers","date":"2022-12-08","arxiv_id":"2212.04595","n_code_links":1,"syntology":null},{"paper":null,"slug":"federated-learning-for-inference-at-anytime","title":"Federated Learning for Inference at Anytime and Anywhere","date":"2022-12-08","arxiv_id":"2212.04084","n_code_links":0,"syntology":null},{"paper":null,"slug":"group-generalized-mean-pooling-for-vision","title":"Group Generalized Mean Pooling for Vision Transformer","date":"2022-12-08","arxiv_id":"2212.04114","n_code_links":0,"syntology":null},{"paper":"/paper/harnessing-the-power-of-multi-task","slug":"harnessing-the-power-of-multi-task","title":"Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations","date":"2022-12-08","arxiv_id":"2212.04231","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":6,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ofa-x/ofa-x"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/llm-planner-few-shot-grounded-planning-for","slug":"llm-planner-few-shot-grounded-planning-for","title":"LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models","date":"2022-12-08","arxiv_id":"2212.04088","n_code_links":1,"syntology":null},{"paper":"/paper/np4g-network-programming-for-generalization","slug":"np4g-network-programming-for-generalization","title":"NP4G : Network Programming for Generalization","date":"2022-12-08","arxiv_id":"2212.11118","n_code_links":1,"syntology":null},{"paper":null,"slug":"nrtr-neuron-reconstruction-with-transformer","title":"NRTR: Neuron Reconstruction with Transformer from 3D Optical Microscopy Images","date":"2022-12-08","arxiv_id":"2212.04163","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-role-of-ai-in-drug-discovery-challenges","title":"The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies","date":"2022-12-08","arxiv_id":"2212.08104","n_code_links":0,"syntology":null},{"paper":"/paper/deepspeed-data-efficiency-improving-deep","slug":"deepspeed-data-efficiency-improving-deep","title":"DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing","date":"2022-12-07","arxiv_id":"2212.03597","n_code_links":1,"syntology":null},{"paper":null,"slug":"gaussian-radar-transformer-for-semantic","title":"Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data","date":"2022-12-07","arxiv_id":"2212.03690","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-multimodal-transformers-for","slug":"hierarchical-multimodal-transformers-for","title":"Hierarchical multimodal transformers for Multi-Page DocVQA","date":"2022-12-07","arxiv_id":"2212.05935","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-embed-adopting-transformer-based","title":"Learning-To-Embed: Adopting Transformer based models for E-commerce Products Representation Learning","date":"2022-12-07","arxiv_id":"2212.03725","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-vision-transformers-with-forced","title":"Multimodal Vision Transformers with Forced Attention for Behavior Analysis","date":"2022-12-07","arxiv_id":"2212.03968","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-k-variate-time-series-is-worth-k-words","title":"A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting","date":"2022-12-06","arxiv_id":"2212.02789","n_code_links":0,"syntology":null},{"paper":null,"slug":"abhe-all-attention-based-homography","title":"AbHE: All Attention-based Homography Estimation","date":"2022-12-06","arxiv_id":"2212.03029","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-testing-of-computer-vision-models","slug":"adaptive-testing-of-computer-vision-models","title":"Adaptive Testing of Computer Vision Models","date":"2022-12-06","arxiv_id":"2212.02774","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["i-gao/adavision"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"controlled-text-generation-using-t5-based","title":"Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI","date":"2022-12-06","arxiv_id":"2212.02924","n_code_links":0,"syntology":null},{"paper":"/paper/counterfactual-reasoning-do-language-models","slug":"counterfactual-reasoning-do-language-models","title":"Counterfactual reasoning: Do language models need world knowledge for causal understanding?","date":"2022-12-06","arxiv_id":"2212.03278","n_code_links":1,"syntology":null},{"paper":"/paper/document-level-abstractive-summarization","slug":"document-level-abstractive-summarization","title":"Document-Level Abstractive Summarization","date":"2022-12-06","arxiv_id":"2212.03013","n_code_links":1,"syntology":null},{"paper":"/paper/incepformer-efficient-inception-transformer","slug":"incepformer-efficient-inception-transformer","title":"IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation","date":"2022-12-06","arxiv_id":"2212.03035","n_code_links":1,"syntology":null},{"paper":null,"slug":"modern-french-poetry-generation-with-roberta","title":"Modern French Poetry Generation with RoBERTa and GPT-2","date":"2022-12-06","arxiv_id":"2212.02911","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-world-detr-transformer-based-open-world","title":"Open World DETR: Transformer based Open World Object Detection","date":"2022-12-06","arxiv_id":"2212.02969","n_code_links":0,"syntology":null},{"paper":null,"slug":"pretrained-diffusion-models-for-unified-human","title":"Pretrained Diffusion Models for Unified Human Motion Synthesis","date":"2022-12-06","arxiv_id":"2212.02837","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-conditional-diffusion-networks-for","slug":"semantic-conditional-diffusion-networks-for","title":"Semantic-Conditional Diffusion Networks for Image Captioning","date":"2022-12-06","arxiv_id":"2212.03099","n_code_links":2,"syntology":null},{"paper":"/paper/simple-baseline-for-weather-forecasting-using","slug":"simple-baseline-for-weather-forecasting-using","title":"Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network","date":"2022-12-06","arxiv_id":"2212.02952","n_code_links":1,"syntology":{"ran":5,"of":10,"n_ran_checked":5,"n_instrument":0,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["seominseok0429/w4c22-simple-baseline-for-weather-forecasting-using-spatiotemporal-context-aggregation-network"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/unigeo-unifying-geometry-logical-reasoning","slug":"unigeo-unifying-geometry-logical-reasoning","title":"UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression","date":"2022-12-06","arxiv_id":"2212.02746","n_code_links":2,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":4,"phrase":"2 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; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["chen-judge/unigeo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"video-object-of-interest-segmentation","title":"Video Object of Interest Segmentation","date":"2022-12-06","arxiv_id":"2212.02871","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-latentmapper-view-agnostic-single-view","title":"3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes","date":"2022-12-05","arxiv_id":"2212.02184","n_code_links":0,"syntology":null},{"paper":null,"slug":"audio-driven-co-speech-gesture-video","title":"Audio-Driven Co-Speech Gesture Video Generation","date":"2022-12-05","arxiv_id":"2212.02350","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-generation-of-factual-news","title":"Automatic Generation of Factual News Headlines in Finnish","date":"2022-12-05","arxiv_id":"2212.02170","n_code_links":0,"syntology":null},{"paper":null,"slug":"inspired-by-norbert-wiener-feedback-loop","title":"FBLNet: FeedBack Loop Network for Driver Attention Prediction","date":"2022-12-05","arxiv_id":"2212.02096","n_code_links":0,"syntology":null},{"paper":"/paper/mask-matching-transformer-for-few-shot","slug":"mask-matching-transformer-for-few-shot","title":"Mask Matching Transformer for Few-Shot Segmentation","date":"2022-12-05","arxiv_id":"2301.01208","n_code_links":1,"syntology":null},{"paper":"/paper/retrieval-as-attention-end-to-end-learning-of","slug":"retrieval-as-attention-end-to-end-learning-of","title":"Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer","date":"2022-12-05","arxiv_id":"2212.02027","n_code_links":1,"syntology":null},{"paper":"/paper/unifying-vision-text-and-layout-for-universal","slug":"unifying-vision-text-and-layout-for-universal","title":"Unifying Vision, Text, and Layout for Universal Document Processing","date":"2022-12-05","arxiv_id":"2212.02623","n_code_links":5,"syntology":{"ran":15,"of":17,"n_ran_checked":14,"n_instrument":1,"unverified":2,"pointer_only":4,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 2 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["microsoft/i-code","microsoft/udop"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":null,"slug":"joint-self-supervised-image-volume","title":"Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering","date":"2022-12-04","arxiv_id":"2212.01893","n_code_links":0,"syntology":null},{"paper":null,"slug":"languages-you-know-influence-those-you-learn","title":"Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer","date":"2022-12-04","arxiv_id":"2212.01757","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-limits-of-differentially","title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","date":"2022-12-03","arxiv_id":"2212.01539","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-memory-transformer-for-processing-long","title":"Global memory transformer for processing long documents","date":"2022-12-03","arxiv_id":"2212.01650","n_code_links":0,"syntology":null},{"paper":null,"slug":"recognition-and-prediction-of-surgical","title":"Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery","date":"2022-12-03","arxiv_id":"2212.01683","n_code_links":0,"syntology":null},{"paper":"/paper/fecam-frequency-enhanced-channel-attention","slug":"fecam-frequency-enhanced-channel-attention","title":"FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting","date":"2022-12-02","arxiv_id":"2212.01209","n_code_links":1,"syntology":null},{"paper":"/paper/relation-aware-language-graph-transformer-for","slug":"relation-aware-language-graph-transformer-for","title":"Relation-Aware Language-Graph Transformer for Question Answering","date":"2022-12-02","arxiv_id":"2212.00975","n_code_links":1,"syntology":null},{"paper":"/paper/slmt-net-a-self-supervised-learning-based","slug":"slmt-net-a-self-supervised-learning-based","title":"Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis","date":"2022-12-02","arxiv_id":"2212.01108","n_code_links":2,"syntology":null},{"paper":"/paper/sumren-summarizing-reported-speech-about","slug":"sumren-summarizing-reported-speech-about","title":"SumREN: Summarizing Reported Speech about Events in News","date":"2022-12-02","arxiv_id":"2212.01146","n_code_links":1,"syntology":null},{"paper":"/paper/tackling-low-resourced-sign-language","slug":"tackling-low-resourced-sign-language","title":"Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22","date":"2022-12-02","arxiv_id":"2212.01140","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-diverse-relevant-and-coherent-open","title":"Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables","date":"2022-12-02","arxiv_id":"2212.01145","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-gpt-3","title":"a survey on GPT-3","date":"2022-12-01","arxiv_id":"2212.00857","n_code_links":0,"syntology":null},{"paper":null,"slug":"chapter-exploiting-convolutional-neural","title":"CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models","date":"2022-12-01","arxiv_id":"2212.01282","n_code_links":0,"syntology":null},{"paper":null,"slug":"concealed-object-detection-for-passive","title":"Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer","date":"2022-12-01","arxiv_id":"2212.00313","n_code_links":0,"syntology":null},{"paper":null,"slug":"cuni-non-autoregressive-system-for-the-wmt-22","title":"CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task","date":"2022-12-01","arxiv_id":"2212.00477","n_code_links":0,"syntology":null},{"paper":"/paper/data-efficient-finetuning-using-cross-task","slug":"data-efficient-finetuning-using-cross-task","title":"Data-Efficient Finetuning Using Cross-Task Nearest Neighbors","date":"2022-12-01","arxiv_id":"2212.00196","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":["allenai/data-efficient-finetuning"],"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/distilling-multi-step-reasoning-capabilities","slug":"distilling-multi-step-reasoning-capabilities","title":"Distilling Reasoning Capabilities into Smaller Language Models","date":"2022-12-01","arxiv_id":"2212.00193","n_code_links":1,"syntology":null},{"paper":"/paper/explainable-artificial-intelligence-for-8","slug":"explainable-artificial-intelligence-for-8","title":"Explainable Artificial Intelligence for Improved Modeling of Processes","date":"2022-12-01","arxiv_id":"2212.00695","n_code_links":1,"syntology":null},{"paper":"/paper/ghost-free-high-dynamic-range-imaging-via","slug":"ghost-free-high-dynamic-range-imaging-via","title":"Ghost-free High Dynamic Range Imaging via Hybrid CNN-Transformer and Structure Tensor","date":"2022-12-01","arxiv_id":"2212.00595","n_code_links":1,"syntology":null},{"paper":"/paper/learning-progressive-modality-shared","slug":"learning-progressive-modality-shared","title":"Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification","date":"2022-12-01","arxiv_id":"2212.00226","n_code_links":1,"syntology":null},{"paper":null,"slug":"dsnet-a-simple-yet-efficient-network-with","title":"DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation","date":"2022-11-30","arxiv_id":"2211.16950","n_code_links":0,"syntology":null},{"paper":"/paper/part-based-face-recognition-with-vision","slug":"part-based-face-recognition-with-vision","title":"Part-based Face Recognition with Vision Transformers","date":"2022-11-30","arxiv_id":"2212.00057","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["szlbiubiubiu/Part_fViT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/pattern-attention-transformer-with-doughnut","slug":"pattern-attention-transformer-with-doughnut","title":"Pattern Attention Transformer with Doughnut Kernel","date":"2022-11-30","arxiv_id":"2211.16961","n_code_links":0,"syntology":null}],"record_sha256":"f8c3b6d248606ad885826b145cc760dfd23e19d9b6a3a3b5593482fa13e265e5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}