{"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/linear-layer/papers/165","list_of":"/method/linear-layer","method":"Linear Layer","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":165,"pages_in_order":255,"rows_per_page":100,"rows":[16401,16500],"of":25421,"counts":{"archive_papers_tagged":25421,"with_a_code_link":11479,"where_syntology_ran_a_sample":3523,"not_listed_spam_title":0,"listed":25421,"listed_where_code_ran":3523,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2976,"every_run_a_failure_of_syntologys_instrument":547,"listed_with_a_run_with_no_instrument_failure":2976,"listed_every_run_a_failure_of_syntologys_instrument":547,"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/linear-layer","prev":"/method/linear-layer/papers/164","next":"/method/linear-layer/papers/166","papers":[{"paper":null,"slug":"loggd-detecting-anomalies-from-system-logs-by","title":"LogGD:Detecting Anomalies from System Logs by Graph Neural Networks","date":"2022-09-16","arxiv_id":"2209.07869","n_code_links":0,"syntology":null},{"paper":"/paper/ppt-token-pruned-pose-transformer-for","slug":"ppt-token-pruned-pose-transformer-for","title":"PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation","date":"2022-09-16","arxiv_id":"2209.08194","n_code_links":2,"syntology":null},{"paper":"/paper/psychologically-informed-chain-of-thought","slug":"psychologically-informed-chain-of-thought","title":"Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models","date":"2022-09-16","arxiv_id":"2209.08141","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["benpry/chain-of-thought-metaphor"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"robust-ensemble-morph-detection-with-domain","title":"Robust Ensemble Morph Detection with Domain Generalization","date":"2022-09-16","arxiv_id":"2209.08130","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-learning-of-phenotypic","slug":"self-supervised-learning-of-phenotypic","title":"Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels","date":"2022-09-16","arxiv_id":"2209.07819","n_code_links":1,"syntology":null},{"paper":null,"slug":"text-and-patterns-for-effective-chain-of","title":"Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango","date":"2022-09-16","arxiv_id":"2209.07686","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-offline-reinforcement-learning-help","title":"Can Offline Reinforcement Learning Help Natural Language Understanding?","date":"2022-09-15","arxiv_id":"2212.03864","n_code_links":0,"syntology":null},{"paper":null,"slug":"hydra-attention-efficient-attention-with-many","title":"Hydra Attention: Efficient Attention with Many Heads","date":"2022-09-15","arxiv_id":"2209.07484","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-reading-fast-and-slow-when-do-models","title":"Machine Reading, Fast and Slow: When Do Models \"Understand\" Language?","date":"2022-09-15","arxiv_id":"2209.07430","n_code_links":0,"syntology":null},{"paper":"/paper/multi-modal-masked-autoencoders-for-medical","slug":"multi-modal-masked-autoencoders-for-medical","title":"Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training","date":"2022-09-15","arxiv_id":"2209.07098","n_code_links":1,"syntology":null},{"paper":null,"slug":"number-of-attention-heads-vs-number-of","title":"Number of Attention Heads vs Number of Transformer-Encoders in Computer Vision","date":"2022-09-15","arxiv_id":"2209.07221","n_code_links":0,"syntology":null},{"paper":"/paper/pizza-a-powerful-image-only-zero-shot-zero","slug":"pizza-a-powerful-image-only-zero-shot-zero","title":"PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking","date":"2022-09-15","arxiv_id":"2209.07589","n_code_links":1,"syntology":{"ran":18,"of":18,"n_ran_checked":15,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nv-nguyen/pizza"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/priorlane-a-prior-knowledge-enhanced-lane","slug":"priorlane-a-prior-knowledge-enhanced-lane","title":"PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer","date":"2022-09-15","arxiv_id":"2209.06994","n_code_links":1,"syntology":null},{"paper":"/paper/stateful-memory-augmented-transformers-for","slug":"stateful-memory-augmented-transformers-for","title":"Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling","date":"2022-09-15","arxiv_id":"2209.07634","n_code_links":1,"syntology":null},{"paper":"/paper/stpotr-simultaneous-human-trajectory-and-pose","slug":"stpotr-simultaneous-human-trajectory-and-pose","title":"STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead","date":"2022-09-15","arxiv_id":"2209.07600","n_code_links":1,"syntology":null},{"paper":null,"slug":"uchecker-masked-pretrained-language-models-as","title":"uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers","date":"2022-09-15","arxiv_id":"2209.07068","n_code_links":0,"syntology":null},{"paper":"/paper/ufal-corpipe-at-crac-2022-effectivity-of","slug":"ufal-corpipe-at-crac-2022-effectivity-of","title":"ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution","date":"2022-09-15","arxiv_id":"2209.07278","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-fidelity-assessment-for-strategy","title":"Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing","date":"2022-09-14","arxiv_id":"2209.06727","n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-based-ensemble-approaches-for-hate","title":"BERT-based Ensemble Approaches for Hate Speech Detection","date":"2022-09-14","arxiv_id":"2209.06505","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-quantized-sparse-matrix-operations","slug":"efficient-quantized-sparse-matrix-operations","title":"Efficient Quantized Sparse Matrix Operations on Tensor Cores","date":"2022-09-14","arxiv_id":"2209.06979","n_code_links":1,"syntology":null},{"paper":null,"slug":"out-of-one-many-using-language-models-to","title":"Out of One, Many: Using Language Models to Simulate Human Samples","date":"2022-09-14","arxiv_id":"2209.06899","n_code_links":0,"syntology":null},{"paper":null,"slug":"paratts-learning-linguistic-and-prosodic","title":"ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS","date":"2022-09-14","arxiv_id":"2209.06484","n_code_links":0,"syntology":null},{"paper":null,"slug":"pre-training-for-information-retrieval-are","title":"Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?","date":"2022-09-14","arxiv_id":"2209.06583","n_code_links":0,"syntology":null},{"paper":"/paper/uit-vicov19qa-a-dataset-for-covid-19","slug":"uit-vicov19qa-a-dataset-for-covid-19","title":"UIT-ViCoV19QA: A Dataset for COVID-19 Community-based Question Answering on Vietnamese Language","date":"2022-09-14","arxiv_id":"2209.06668","n_code_links":1,"syntology":null},{"paper":null,"slug":"vec2text-with-round-trip-translations","title":"vec2text with Round-Trip Translations","date":"2022-09-14","arxiv_id":"2209.06792","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-lightweight-transformer-based-model-for","title":"A lightweight Transformer-based model for fish landmark detection","date":"2022-09-13","arxiv_id":"2209.05777","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-self-attention-head-diversity-for","title":"Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition","date":"2022-09-13","arxiv_id":"2209.06096","n_code_links":0,"syntology":null},{"paper":null,"slug":"cnn-trans-enc-a-cnn-enhanced-transformer","title":"CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification","date":"2022-09-13","arxiv_id":"2209.06344","n_code_links":0,"syntology":null},{"paper":null,"slug":"completr-reducing-the-cost-of-annotations-for","title":"ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers","date":"2022-09-13","arxiv_id":"2209.05654","n_code_links":0,"syntology":null},{"paper":null,"slug":"dmtnet-dynamic-multi-scale-network-for-dual","title":"DMTNet: Dynamic Multi-scale Network for Dual-pixel Images Defocus Deblurring with Transformer","date":"2022-09-13","arxiv_id":"2209.06040","n_code_links":0,"syntology":null},{"paper":null,"slug":"document-aware-positional-encoding-and","title":"Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization","date":"2022-09-13","arxiv_id":"2209.05929","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-view-performers-for-shape-completion","title":"Multiple View Performers for Shape Completion","date":"2022-09-13","arxiv_id":"2209.06291","n_code_links":0,"syntology":null},{"paper":null,"slug":"robin-a-novel-online-suicidal-text-corpus-of-1","title":"Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale","date":"2022-09-13","arxiv_id":"2209.05707","n_code_links":0,"syntology":null},{"paper":null,"slug":"skin-skimming-intensive-long-text","title":"SkIn: Skimming-Intensive Long-Text Classification Using BERT for Medical Corpus","date":"2022-09-13","arxiv_id":"2209.05741","n_code_links":0,"syntology":null},{"paper":null,"slug":"songdriver-real-time-music-accompaniment","title":"SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias","date":"2022-09-13","arxiv_id":"2209.06054","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-transformers-for-action-recognition-a","title":"Vision Transformers for Action Recognition: A Survey","date":"2022-09-13","arxiv_id":"2209.05700","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-of-hazard-event-via-language","title":"A new hazard event classification model via deep learning and multifractal","date":"2022-09-12","arxiv_id":"2209.05263","n_code_links":0,"syntology":null},{"paper":null,"slug":"deck-behavioral-tests-to-improve-1","title":"DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text","date":"2022-09-12","arxiv_id":"2209.05286","n_code_links":0,"syntology":null},{"paper":"/paper/deep-convolutional-pooling-transformer-for","slug":"deep-convolutional-pooling-transformer-for","title":"Deep Convolutional Pooling Transformer for Deepfake Detection","date":"2022-09-12","arxiv_id":"2209.05299","n_code_links":1,"syntology":null},{"paper":"/paper/perceiver-actor-a-multi-task-transformer-for","slug":"perceiver-actor-a-multi-task-transformer-for","title":"Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation","date":"2022-09-12","arxiv_id":"2209.05451","n_code_links":1,"syntology":null},{"paper":"/paper/tmss-an-end-to-end-transformer-based","slug":"tmss-an-end-to-end-transformer-based","title":"TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction","date":"2022-09-12","arxiv_id":"2209.05036","n_code_links":1,"syntology":null},{"paper":null,"slug":"chain-of-explanation-new-prompting-method-to","title":"Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech","date":"2022-09-11","arxiv_id":"2209.04889","n_code_links":0,"syntology":null},{"paper":"/paper/openmixup-open-mixup-toolbox-and-benchmark","slug":"openmixup-open-mixup-toolbox-and-benchmark","title":"OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning","date":"2022-09-11","arxiv_id":"2209.04851","n_code_links":1,"syntology":null},{"paper":null,"slug":"probing-for-understanding-of-english-verb","title":"Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models","date":"2022-09-11","arxiv_id":"2209.04811","n_code_links":0,"syntology":null},{"paper":null,"slug":"simple-and-effective-gradient-based-tuning-of","title":"Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models","date":"2022-09-10","arxiv_id":"2209.04683","n_code_links":0,"syntology":null},{"paper":null,"slug":"yes-dlgm-a-novel-hierarchical-model-for","title":"Yes, DLGM! A novel hierarchical model for hazard classification","date":"2022-09-10","arxiv_id":"2209.04576","n_code_links":0,"syntology":null},{"paper":"/paper/adapting-to-non-centered-languages-for-zero","slug":"adapting-to-non-centered-languages-for-zero","title":"Adapting to Non-Centered Languages for Zero-shot Multilingual Translation","date":"2022-09-09","arxiv_id":"2209.04138","n_code_links":1,"syntology":null},{"paper":"/paper/echocotr-estimation-of-the-left-ventricular","slug":"echocotr-estimation-of-the-left-ventricular","title":"EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography","date":"2022-09-09","arxiv_id":"2209.04242","n_code_links":1,"syntology":null},{"paper":"/paper/gluformer-transformer-based-personalized","slug":"gluformer-transformer-based-personalized","title":"Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification","date":"2022-09-09","arxiv_id":"2209.04526","n_code_links":1,"syntology":null},{"paper":null,"slug":"protecting-world-leader-using-facial-speaking","title":"Protecting World Leader Using Facial Speaking Pattern Against Deepfakes","date":"2022-09-09","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"trigger-warnings-bootstrapping-a-violence","title":"Trigger Warnings: Bootstrapping a Violence Detector for FanFiction","date":"2022-09-09","arxiv_id":"2209.04409","n_code_links":0,"syntology":null},{"paper":"/paper/claclab-at-socialdisner-using-medical","slug":"claclab-at-socialdisner-using-medical","title":"CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets","date":"2022-09-08","arxiv_id":"2209.03528","n_code_links":1,"syntology":null},{"paper":"/paper/idiapers-causal-news-corpus-2022-extracting","slug":"idiapers-causal-news-corpus-2022-extracting","title":"IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model","date":"2022-09-08","arxiv_id":"2209.03891","n_code_links":1,"syntology":null},{"paper":"/paper/levenshtein-ocr","slug":"levenshtein-ocr","title":"Levenshtein OCR","date":"2022-09-08","arxiv_id":"2209.03594","n_code_links":2,"syntology":null},{"paper":"/paper/multi-granularity-prediction-for-scene-text","slug":"multi-granularity-prediction-for-scene-text","title":"Multi-Granularity Prediction for Scene Text Recognition","date":"2022-09-08","arxiv_id":"2209.03592","n_code_links":3,"syntology":null},{"paper":null,"slug":"multilingual-transformer-language-model-for","title":"Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages","date":"2022-09-08","arxiv_id":"2209.04041","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-a-graph-recurrent-network-for","slug":"pre-training-a-graph-recurrent-network-for","title":"Pre-Training a Graph Recurrent Network for Language Representation","date":"2022-09-08","arxiv_id":"2209.03834","n_code_links":1,"syntology":null},{"paper":"/paper/q-learning-decision-transformer-leveraging","slug":"q-learning-decision-transformer-leveraging","title":"Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL","date":"2022-09-08","arxiv_id":"2209.03993","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-explainable-evaluation-of-language","title":"Towards explainable evaluation of language models on the semantic similarity of visual concepts","date":"2022-09-08","arxiv_id":"2209.03723","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-classification-of-premise","title":"5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19","date":"2022-09-08","arxiv_id":"2209.03851","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-vision-transformers-for-violence","title":"Video Vision Transformers for Violence Detection","date":"2022-09-08","arxiv_id":"2209.03561","n_code_links":0,"syntology":null},{"paper":null,"slug":"adam-mickiewicz-university-at-wmt-2022-ner","title":"Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation","date":"2022-09-07","arxiv_id":"2209.02962","n_code_links":0,"syntology":null},{"paper":null,"slug":"ailab-udine-smm4h-22-limits-of-transformers","title":"AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles","date":"2022-09-07","arxiv_id":"2209.03452","n_code_links":0,"syntology":null},{"paper":null,"slug":"auto-transrl-autonomous-composition-of-vision","title":"Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception","date":"2022-09-07","arxiv_id":"2209.02991","n_code_links":0,"syntology":null},{"paper":null,"slug":"blessing-of-class-diversity-in-pre-training-1","title":"Blessing of Class Diversity in Pre-training","date":"2022-09-07","arxiv_id":"2209.03447","n_code_links":0,"syntology":null},{"paper":null,"slug":"prior-knowledge-guided-attention-in-self","title":"Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers","date":"2022-09-07","arxiv_id":"2209.03745","n_code_links":0,"syntology":null},{"paper":"/paper/spach-transformer-spatial-and-channel-wise","slug":"spach-transformer-spatial-and-channel-wise","title":"Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising","date":"2022-09-07","arxiv_id":"2209.03300","n_code_links":1,"syntology":null},{"paper":null,"slug":"transfer-learning-and-vision-transformer","title":"Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries","date":"2022-09-07","arxiv_id":"2209.05253","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-transformer-for-soil-classification","title":"Visual Transformer for Soil Classification","date":"2022-09-07","arxiv_id":"2209.02950","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-so-toxic-measuring-and-triggering-toxic","title":"Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots","date":"2022-09-07","arxiv_id":"2209.03463","n_code_links":0,"syntology":null},{"paper":"/paper/analyzing-transformers-in-embedding-space","slug":"analyzing-transformers-in-embedding-space","title":"Analyzing Transformers in Embedding Space","date":"2022-09-06","arxiv_id":"2209.02535","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":{"repos":["guyd1995/embedding-space"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/continual-learning-fast-and-slow","slug":"continual-learning-fast-and-slow","title":"Continual Learning, Fast and Slow","date":"2022-09-06","arxiv_id":"2209.02370","n_code_links":1,"syntology":null},{"paper":null,"slug":"fusion-of-satellite-images-and-weather-data","title":"Fusion of Satellite Images and Weather Data with Transformer Networks for Downy Mildew Disease Detection","date":"2022-09-06","arxiv_id":"2209.02797","n_code_links":0,"syntology":null},{"paper":null,"slug":"lrt-an-efficient-low-light-restoration","title":"LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images","date":"2022-09-06","arxiv_id":"2209.02197","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-the-black-box-brighter-interpreting","title":"Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents","date":"2022-09-06","arxiv_id":"2209.02256","n_code_links":0,"syntology":null},{"paper":"/paper/multilingual-bidirectional-unsupervised","slug":"multilingual-bidirectional-unsupervised","title":"Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation","date":"2022-09-06","arxiv_id":"2209.02821","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-image-synthesis-with-semantically","title":"Semantic Image Synthesis with Semantically Coupled VQ-Model","date":"2022-09-06","arxiv_id":"2209.02536","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-outcome-of-the-2022-landslide4sense","title":"The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery","date":"2022-09-06","arxiv_id":"2209.02556","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-cnn-cohort-semi-supervised","title":"Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students","date":"2022-09-06","arxiv_id":"2209.02178","n_code_links":0,"syntology":null},{"paper":null,"slug":"user-recommendation-system-based-on-mind-1","title":"User recommendation system based on MIND dataset","date":"2022-09-06","arxiv_id":"2209.06131","n_code_links":0,"syntology":null},{"paper":"/paper/vitkd-practical-guidelines-for-vit-feature","slug":"vitkd-practical-guidelines-for-vit-feature","title":"ViTKD: Practical Guidelines for ViT feature knowledge distillation","date":"2022-09-06","arxiv_id":"2209.02432","n_code_links":1,"syntology":null},{"paper":"/paper/chemberta-2-towards-chemical-foundation","slug":"chemberta-2-towards-chemical-foundation","title":"ChemBERTa-2: Towards Chemical Foundation Models","date":"2022-09-05","arxiv_id":"2209.01712","n_code_links":2,"syntology":null},{"paper":null,"slug":"distilling-the-knowledge-of-bert-for-ctc","title":"Distilling the Knowledge of BERT for CTC-based ASR","date":"2022-09-05","arxiv_id":"2209.02030","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-susceptibility-of-pre-trained","title":"Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples","date":"2022-09-05","arxiv_id":"2209.02128","n_code_links":0,"syntology":null},{"paper":null,"slug":"features-fusion-framework-for-multimodal","title":"Features Fusion Framework for Multimodal Irregular Time-series Events","date":"2022-09-05","arxiv_id":"2209.01728","n_code_links":0,"syntology":null},{"paper":"/paper/multi-figurative-language-generation","slug":"multi-figurative-language-generation","title":"Multi-Figurative Language Generation","date":"2022-09-05","arxiv_id":"2209.01835","n_code_links":1,"syntology":null},{"paper":null,"slug":"seformer-structure-embedding-transformer-for","title":"SEFormer: Structure Embedding Transformer for 3D Object Detection","date":"2022-09-05","arxiv_id":"2209.01745","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-of-end-to-end-video","slug":"an-empirical-study-of-end-to-end-video","title":"An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling","date":"2022-09-04","arxiv_id":"2209.01540","n_code_links":1,"syntology":null},{"paper":"/paper/do-large-language-models-know-what-humans","slug":"do-large-language-models-know-what-humans","title":"Do Large Language Models know what humans know?","date":"2022-09-04","arxiv_id":"2209.01515","n_code_links":1,"syntology":null},{"paper":null,"slug":"every-picture-tells-a-story-image-grounded","title":"Every picture tells a story: Image-grounded controllable stylistic story generation","date":"2022-09-04","arxiv_id":"2209.01638","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalization-in-neural-networks-a-broad","title":"Generalization in Neural Networks: A Broad Survey","date":"2022-09-04","arxiv_id":"2209.01610","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-transformer-with-spatio-temporal","slug":"hierarchical-transformer-with-spatio-temporal","title":"Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation","date":"2022-09-04","arxiv_id":"2209.01559","n_code_links":1,"syntology":null},{"paper":"/paper/informative-language-representation-learning","slug":"informative-language-representation-learning","title":"Informative Language Representation Learning for Massively Multilingual Neural Machine Translation","date":"2022-09-04","arxiv_id":"2209.01530","n_code_links":1,"syntology":null},{"paper":"/paper/time-distance-vision-transformers-in-lung","slug":"time-distance-vision-transformers-in-lung","title":"Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography","date":"2022-09-04","arxiv_id":"2209.01676","n_code_links":1,"syntology":null},{"paper":"/paper/togethernet-bridging-image-restoration-and","slug":"togethernet-bridging-image-restoration-and","title":"TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning","date":"2022-09-03","arxiv_id":"2209.01373","n_code_links":1,"syntology":null},{"paper":"/paper/transpolymer-a-transformer-based-language","slug":"transpolymer-a-transformer-based-language","title":"TransPolymer: a Transformer-based language model for polymer property predictions","date":"2022-09-03","arxiv_id":"2209.01307","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"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) · 1 unverified","official":{"repos":["ChangwenXu98/TransPolymer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"viecap4h-vlsp-2021-vietnamese-image","title":"vieCap4H-VLSP 2021: Vietnamese Image Captioning for Healthcare Domain using Swin Transformer and Attention-based LSTM","date":"2022-09-03","arxiv_id":"2209.01304","n_code_links":0,"syntology":null},{"paper":null,"slug":"arst-auto-regressive-surgical-transformer-for","title":"ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos","date":"2022-09-02","arxiv_id":"2209.01148","n_code_links":0,"syntology":null},{"paper":null,"slug":"autopet-challenge-combining-nn-unet-with-swin","title":"AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier","date":"2022-09-02","arxiv_id":"2209.01112","n_code_links":0,"syntology":null},{"paper":"/paper/dpit-dual-pipeline-integrated-transformer-for","slug":"dpit-dual-pipeline-integrated-transformer-for","title":"DPIT: Dual-Pipeline Integrated Transformer for Human Pose Estimation","date":"2022-09-02","arxiv_id":"2209.02431","n_code_links":0,"syntology":null}],"record_sha256":"f60af553e0afd65cda315ce7c95d6f0ad9a1788be693a0d686aaa457de5f9236","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}