{"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":"/task/few-shot-learning/papers/9","list_of":"/task/few-shot-learning","task":"Few-Shot Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":9,"pages_in_order":30,"rows_per_page":100,"rows":[801,900],"of":2964,"counts":{"archive_papers_tagged":2964,"with_a_code_link":1297,"where_syntology_ran_a_sample":373,"not_listed_spam_title":0,"listed":2964,"listed_where_code_ran":373,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":306,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":306,"listed_every_run_a_failure_of_syntologys_instrument":67,"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":"/task/few-shot-learning","prev":"/task/few-shot-learning/papers/8","next":"/task/few-shot-learning/papers/10","papers":[{"url":"/paper/metric-based-few-shot-graph-classification","slug":"metric-based-few-shot-graph-classification","title":"Metric Based Few-Shot Graph Classification","date":"2022-06-08","arxiv_id":"2206.03695","repositories_listed":1,"syntology":null},{"url":"/paper/poodle-improving-few-shot-learning-via-1","slug":"poodle-improving-few-shot-learning-via-1","title":"POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples","date":"2022-06-08","arxiv_id":"2206.04679","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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","sample_list":"/paper/poodle-improving-few-shot-learning-via-1#ran","syntology_url":"https://syntology.ai/paper/2206.04679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04679"}},"official":{"repos":["lehduong/poodle"],"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"]}}},{"url":"/paper/few-shot-learning-by-dimensionality-reduction","slug":"few-shot-learning-by-dimensionality-reduction","title":"Few-Shot Learning by Dimensionality Reduction in Gradient Space","date":"2022-06-07","arxiv_id":"2206.03483","repositories_listed":1,"syntology":null},{"url":"/paper/robust-meta-learning-with-sampling-noise-and-1","slug":"robust-meta-learning-with-sampling-noise-and-1","title":"Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile","date":"2022-06-04","arxiv_id":"2206.01944","repositories_listed":1,"syntology":null},{"url":"/paper/the-spike-gating-flow-a-hierarchical","slug":"the-spike-gating-flow-a-hierarchical","title":"The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition","date":"2022-06-04","arxiv_id":"2206.01910","repositories_listed":1,"syntology":null},{"url":"/paper/a-named-entity-recognition-corpus-for","slug":"a-named-entity-recognition-corpus-for","title":"A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-approaches-for-the-detection-of-1","slug":"cross-lingual-approaches-for-the-detection-of-1","title":"Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s Perspective","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-learning-for-argument-aspects-of-the","slug":"few-shot-learning-for-argument-aspects-of-the","title":"Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/metaphor-detection-for-low-resource-languages-1","slug":"metaphor-detection-for-low-resource-languages-1","title":"Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hypermaml-few-shot-adaptation-of-deep-models","slug":"hypermaml-few-shot-adaptation-of-deep-models","title":"HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks","date":"2022-05-31","arxiv_id":"2205.15745","repositories_listed":1,"syntology":null},{"url":"/paper/meta-ticket-finding-optimal-subnetworks-for","slug":"meta-ticket-finding-optimal-subnetworks-for","title":"Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks","date":"2022-05-31","arxiv_id":"2205.15619","repositories_listed":1,"syntology":null},{"url":"/paper/easter2-0-improving-convolutional-models-for","slug":"easter2-0-improving-convolutional-models-for","title":"Easter2.0: Improving convolutional models for handwritten text recognition","date":"2022-05-30","arxiv_id":"2205.14879","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-diffusion-models","slug":"few-shot-diffusion-models","title":"Few-Shot Diffusion Models","date":"2022-05-30","arxiv_id":"2205.15463","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-aligned-gradient-for-prompt-tuning","slug":"prompt-aligned-gradient-for-prompt-tuning","title":"Prompt-aligned Gradient for Prompt Tuning","date":"2022-05-30","arxiv_id":"2205.14865","repositories_listed":1,"syntology":null},{"url":"/paper/prompting-electra-few-shot-learning-with","slug":"prompting-electra-few-shot-learning-with","title":"Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models","date":"2022-05-30","arxiv_id":"2205.15223","repositories_listed":1,"syntology":null},{"url":"/paper/bongard-hoi-benchmarking-few-shot-visual","slug":"bongard-hoi-benchmarking-few-shot-visual","title":"Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions","date":"2022-05-27","arxiv_id":"2205.13803","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-graph-few-shot-learning-with","slug":"spatio-temporal-graph-few-shot-learning-with","title":"Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer","date":"2022-05-27","arxiv_id":"2205.13947","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spatio-temporal-graph-few-shot-learning-with#ran","syntology_url":"https://syntology.ai/paper/2205.13947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13947"}},"official":{"repos":["robinlu1209/st-gfsl"],"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"]}}},{"url":"/paper/learning-dialogue-representations-from","slug":"learning-dialogue-representations-from","title":"Learning Dialogue Representations from Consecutive Utterances","date":"2022-05-26","arxiv_id":"2205.13568","repositories_listed":1,"syntology":null},{"url":"/paper/fleurs-few-shot-learning-evaluation-of","slug":"fleurs-few-shot-learning-evaluation-of","title":"FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech","date":"2022-05-25","arxiv_id":"2205.12446","repositories_listed":1,"syntology":null},{"url":"/paper/attentional-mixtures-of-soft-prompt-tuning","slug":"attentional-mixtures-of-soft-prompt-tuning","title":"ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts","date":"2022-05-24","arxiv_id":"2205.11961","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/attentional-mixtures-of-soft-prompt-tuning#ran","syntology_url":"https://syntology.ai/paper/2205.11961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11961"}},"official":{"repos":["akariasai/attempt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graphq-ir-unifying-semantic-parsing-of-graph","slug":"graphq-ir-unifying-semantic-parsing-of-graph","title":"GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation","date":"2022-05-24","arxiv_id":"2205.12078","repositories_listed":1,"syntology":null},{"url":"/paper/bbtv2-pure-black-box-optimization-can-be","slug":"bbtv2-pure-black-box-optimization-can-be","title":"BBTv2: Towards a Gradient-Free Future with Large Language Models","date":"2022-05-23","arxiv_id":"2205.11200","repositories_listed":1,"syntology":null},{"url":"/paper/prototypical-calibration-for-few-shot","slug":"prototypical-calibration-for-few-shot","title":"Prototypical Calibration for Few-shot Learning of Language Models","date":"2022-05-20","arxiv_id":"2205.10183","repositories_listed":1,"syntology":null},{"url":"/paper/promptda-label-guided-data-augmentation-for","slug":"promptda-label-guided-data-augmentation-for","title":"PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners","date":"2022-05-18","arxiv_id":"2205.09229","repositories_listed":1,"syntology":null},{"url":"/paper/region-aware-metric-learning-for-open-world","slug":"region-aware-metric-learning-for-open-world","title":"Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation","date":"2022-05-17","arxiv_id":"2205.08083","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/region-aware-metric-learning-for-open-world#ran","syntology_url":"https://syntology.ai/paper/2205.08083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08083"}},"official":{"repos":["czifan/raml"],"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"]}}},{"url":"/paper/cross-domain-few-shot-meta-learning-using","slug":"cross-domain-few-shot-meta-learning-using","title":"Feature Extractor Stacking for Cross-domain Few-shot Learning","date":"2022-05-12","arxiv_id":"2205.05831","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unified-prompt-tuning-for-few-shot-1","slug":"towards-unified-prompt-tuning-for-few-shot-1","title":"Towards Unified Prompt Tuning for Few-shot Text Classification","date":"2022-05-11","arxiv_id":"2205.05313","repositories_listed":1,"syntology":null},{"url":"/paper/proqa-structural-prompt-based-pre-training-1","slug":"proqa-structural-prompt-based-pre-training-1","title":"ProQA: Structural Prompt-based Pre-training for Unified Question Answering","date":"2022-05-09","arxiv_id":"2205.04040","repositories_listed":1,"syntology":null},{"url":"/paper/pgada-perturbation-guided-adversarial","slug":"pgada-perturbation-guided-adversarial","title":"PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift","date":"2022-05-08","arxiv_id":"2205.03817","repositories_listed":1,"syntology":null},{"url":"/paper/kecp-knowledge-enhanced-contrastive-prompting","slug":"kecp-knowledge-enhanced-contrastive-prompting","title":"KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering","date":"2022-05-06","arxiv_id":"2205.03071","repositories_listed":1,"syntology":null},{"url":"/paper/faith-few-shot-graph-classification-with","slug":"faith-few-shot-graph-classification-with","title":"FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs","date":"2022-05-05","arxiv_id":"2205.02435","repositories_listed":1,"syntology":null},{"url":"/paper/generating-representative-samples-for-few","slug":"generating-representative-samples-for-few","title":"Generating Representative Samples for Few-Shot Classification","date":"2022-05-05","arxiv_id":"2205.02918","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 0 unverified","sample_list":"/paper/generating-representative-samples-for-few#ran","syntology_url":"https://syntology.ai/paper/2205.02918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.02918"}},"official":{"repos":["cvlab-stonybrook/fsl-rsvae"],"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"]}}},{"url":"/paper/few-shot-document-level-relation-extraction","slug":"few-shot-document-level-relation-extraction","title":"Few-Shot Document-Level Relation Extraction","date":"2022-05-04","arxiv_id":"2205.02048","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-knowledge-distillation-via","slug":"generalized-knowledge-distillation-via","title":"Generalized Knowledge Distillation via Relationship Matching","date":"2022-05-04","arxiv_id":"2205.01915","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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","sample_list":"/paper/generalized-knowledge-distillation-via#ran","syntology_url":"https://syntology.ai/paper/2205.01915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01915"}},"official":{"repos":["njulus/gkd"],"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"]}}},{"url":"/paper/relation-extraction-as-open-book-examination","slug":"relation-extraction-as-open-book-examination","title":"Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning","date":"2022-05-04","arxiv_id":"2205.02355","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-free-and-efficient-few-shot-learning","slug":"prompt-free-and-efficient-few-shot-learning","title":"Prompt-free and Efficient Few-shot Learning with Language Models","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/building-a-role-specified-open-domain-1","slug":"building-a-role-specified-open-domain-1","title":"Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models","date":"2022-04-30","arxiv_id":"2205.00176","repositories_listed":1,"syntology":null},{"url":"/paper/easynlp-a-comprehensive-and-easy-to-use","slug":"easynlp-a-comprehensive-and-easy-to-use","title":"EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing","date":"2022-04-30","arxiv_id":"2205.00258","repositories_listed":1,"syntology":null},{"url":"/paper/look-closer-to-supervise-better-one-shot-font","slug":"look-closer-to-supervise-better-one-shot-font","title":"Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator","date":"2022-04-30","arxiv_id":"2205.00146","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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","sample_list":"/paper/look-closer-to-supervise-better-one-shot-font#ran","syntology_url":"https://syntology.ai/paper/2205.00146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00146"}},"official":{"repos":["kyxscut/CG-GAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/realistic-evaluation-of-transductive-few-shot-1","slug":"realistic-evaluation-of-transductive-few-shot-1","title":"Realistic Evaluation of Transductive Few-Shot Learning","date":"2022-04-24","arxiv_id":"2204.11181","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":2,"n_ran_checked":2,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":10,"phrase":"6 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/realistic-evaluation-of-transductive-few-shot-1#ran","syntology_url":"https://syntology.ai/paper/2204.11181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.11181"}},"official":{"repos":["oveilleux/realistic_transductive_few_shot"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/active-few-shot-learning-with-fasl","slug":"active-few-shot-learning-with-fasl","title":"Active Few-Shot Learning with FASL","date":"2022-04-20","arxiv_id":"2204.09347","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-transfer-learning-to-improve-chest-x","slug":"few-shot-transfer-learning-to-improve-chest-x","title":"Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets","date":"2022-04-16","arxiv_id":"2204.07824","repositories_listed":1,"syntology":null},{"url":"/paper/mgpt-few-shot-learners-go-multilingual","slug":"mgpt-few-shot-learners-go-multilingual","title":"mGPT: Few-Shot Learners Go Multilingual","date":"2022-04-15","arxiv_id":"2204.07580","repositories_listed":1,"syntology":null},{"url":"/paper/pushing-the-limits-of-simple-pipelines-for","slug":"pushing-the-limits-of-simple-pipelines-for","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","date":"2022-04-15","arxiv_id":"2204.07305","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":8,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"14 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; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/pushing-the-limits-of-simple-pipelines-for#ran","syntology_url":"https://syntology.ai/paper/2204.07305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07305"}},"official":{"repos":["hushell/pmf_cvpr22"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-learning-with-noisy-labels","slug":"few-shot-learning-with-noisy-labels","title":"Few-shot Learning with Noisy Labels","date":"2022-04-12","arxiv_id":"2204.05494","repositories_listed":1,"syntology":null},{"url":"/paper/joint-distribution-matters-deep-brownian","slug":"joint-distribution-matters-deep-brownian","title":"Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification","date":"2022-04-09","arxiv_id":"2204.04567","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/joint-distribution-matters-deep-brownian#ran","syntology_url":"https://syntology.ai/paper/2204.04567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04567"}},"official":{"repos":["Fei-Long121/DeepBDC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/banknote-net-open-dataset-for-assistive","slug":"banknote-net-open-dataset-for-assistive","title":"BankNote-Net: Open dataset for assistive universal currency recognition","date":"2022-04-07","arxiv_id":"2204.03738","repositories_listed":1,"syntology":null},{"url":"/paper/interval-bound-propagation-aided-few-shot","slug":"interval-bound-propagation-aided-few-shot","title":"Interval Bound Interpolation for Few-shot Learning with Few Tasks","date":"2022-04-07","arxiv_id":"2204.03511","repositories_listed":1,"syntology":null},{"url":"/paper/metaaudio-a-few-shot-audio-classification","slug":"metaaudio-a-few-shot-audio-classification","title":"MetaAudio: A Few-Shot Audio Classification Benchmark","date":"2022-04-05","arxiv_id":"2204.02121","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/metaaudio-a-few-shot-audio-classification#ran","syntology_url":"https://syntology.ai/paper/2204.02121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02121"}},"official":{"repos":["cheggan/metaaudio-a-few-shot-audio-classification-benchmark"],"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"]}}},{"url":"/paper/too-big-to-fail-active-few-shot-learning","slug":"too-big-to-fail-active-few-shot-learning","title":"Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis","date":"2022-04-05","arxiv_id":"2204.02368","repositories_listed":1,"syntology":null},{"url":"/paper/autoprotonet-interpretability-for","slug":"autoprotonet-interpretability-for","title":"AutoProtoNet: Interpretability for Prototypical Networks","date":"2022-04-02","arxiv_id":"2204.00929","repositories_listed":1,"syntology":null},{"url":"/paper/inverse-is-better-fast-and-accurate-prompt-1","slug":"inverse-is-better-fast-and-accurate-prompt-1","title":"Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging","date":"2022-04-02","arxiv_id":"2204.00885","repositories_listed":1,"syntology":null},{"url":"/paper/k-nn-ner-named-entity-recognition-with","slug":"k-nn-ner-named-entity-recognition-with","title":"$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search","date":"2022-03-31","arxiv_id":"2203.17103","repositories_listed":1,"syntology":null},{"url":"/paper/challenges-in-leveraging-gans-for-few-shot","slug":"challenges-in-leveraging-gans-for-few-shot","title":"Overcoming challenges in leveraging GANs for few-shot data augmentation","date":"2022-03-30","arxiv_id":"2203.16662","repositories_listed":1,"syntology":null},{"url":"/paper/integrative-few-shot-learning-for","slug":"integrative-few-shot-learning-for","title":"Integrative Few-Shot Learning for Classification and Segmentation","date":"2022-03-29","arxiv_id":"2203.15712","repositories_listed":1,"syntology":null},{"url":"/paper/wavprompt-towards-few-shot-spoken-language","slug":"wavprompt-towards-few-shot-spoken-language","title":"WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models","date":"2022-03-29","arxiv_id":"2203.15863","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-learning-with-siamese-networks-and-1","slug":"few-shot-learning-with-siamese-networks-and-1","title":"Few-Shot Learning with Siamese Networks and Label Tuning","date":"2022-03-28","arxiv_id":"2203.14655","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/few-shot-learning-with-siamese-networks-and-1#ran","syntology_url":"https://syntology.ai/paper/2203.14655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14655"}},"official":{"repos":["symanto-research/few-shot-learning-label-tuning"],"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"]}}},{"url":"/paper/a-rationale-centric-framework-for-human-in","slug":"a-rationale-centric-framework-for-human-in","title":"A Rationale-Centric Framework for Human-in-the-loop Machine Learning","date":"2022-03-24","arxiv_id":"2203.12918","repositories_listed":1,"syntology":null},{"url":"/paper/multidimensional-belief-quantification-for","slug":"multidimensional-belief-quantification-for","title":"Multidimensional Belief Quantification for Label-Efficient Meta-Learning","date":"2022-03-23","arxiv_id":"2203.12768","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"6 ran (of which 5 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) · 3 unverified","sample_list":"/paper/multidimensional-belief-quantification-for#ran","syntology_url":"https://syntology.ai/paper/2203.12768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12768"}},"official":{"repos":["pandeydeep9/units-ml-cvpr-22"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hypershot-few-shot-learning-by-kernel","slug":"hypershot-few-shot-learning-by-kernel","title":"HyperShot: Few-Shot Learning by Kernel HyperNetworks","date":"2022-03-21","arxiv_id":"2203.11378","repositories_listed":1,"syntology":null},{"url":"/paper/prototypical-verbalizer-for-prompt-based-few-1","slug":"prototypical-verbalizer-for-prompt-based-few-1","title":"Prototypical Verbalizer for Prompt-based Few-shot Tuning","date":"2022-03-18","arxiv_id":"2203.09770","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 2 unverified","sample_list":"/paper/prototypical-verbalizer-for-prompt-based-few-1#ran","syntology_url":"https://syntology.ai/paper/2203.09770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09770"}},"official":{"repos":["thunlp/OpenPrompt"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/attribute-surrogates-learning-and-spectral","slug":"attribute-surrogates-learning-and-spectral","title":"Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning","date":"2022-03-17","arxiv_id":"2203.09064","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/attribute-surrogates-learning-and-spectral#ran","syntology_url":"https://syntology.ai/paper/2203.09064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09064"}},"official":{"repos":["stomachcold/hctransformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/global-convergence-of-maml-and-theory","slug":"global-convergence-of-maml-and-theory","title":"Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning","date":"2022-03-17","arxiv_id":"2203.09137","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/global-convergence-of-maml-and-theory#ran","syntology_url":"https://syntology.ai/paper/2203.09137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09137"}},"official":{"repos":["yitewang/metantk-nas"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/in-context-learning-for-few-shot-dialogue","slug":"in-context-learning-for-few-shot-dialogue","title":"In-Context Learning for Few-Shot Dialogue State Tracking","date":"2022-03-16","arxiv_id":"2203.08568","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/in-context-learning-for-few-shot-dialogue#ran","syntology_url":"https://syntology.ai/paper/2203.08568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08568"}},"official":{"repos":["yushi-hu/ic-dst"],"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"]}}},{"url":"/paper/label-semantics-for-few-shot-named-entity","slug":"label-semantics-for-few-shot-named-entity","title":"Label Semantics for Few Shot Named Entity Recognition","date":"2022-03-16","arxiv_id":"2203.08985","repositories_listed":1,"syntology":null},{"url":"/paper/wave-san-wavelet-based-style-augmentation","slug":"wave-san-wavelet-based-style-augmentation","title":"Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning","date":"2022-03-15","arxiv_id":"2203.07656","repositories_listed":1,"syntology":null},{"url":"/paper/self-promoted-supervision-for-few-shot","slug":"self-promoted-supervision-for-few-shot","title":"Self-Promoted Supervision for Few-Shot Transformer","date":"2022-03-14","arxiv_id":"2203.07057","repositories_listed":1,"syntology":null},{"url":"/paper/worst-case-matters-for-few-shot-recognition","slug":"worst-case-matters-for-few-shot-recognition","title":"Worst Case Matters for Few-Shot Recognition","date":"2022-03-13","arxiv_id":"2203.06574","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-multitask-fine-tuning-for-few","slug":"model-agnostic-multitask-fine-tuning-for-few","title":"Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning","date":"2022-03-09","arxiv_id":"2203.04904","repositories_listed":1,"syntology":null},{"url":"/paper/instructionner-a-multi-task-instruction-based","slug":"instructionner-a-multi-task-instruction-based","title":"InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER","date":"2022-03-08","arxiv_id":"2203.03903","repositories_listed":1,"syntology":null},{"url":"/paper/pre-trained-token-replaced-detection-model-as","slug":"pre-trained-token-replaced-detection-model-as","title":"Pre-trained Token-replaced Detection Model as Few-shot Learner","date":"2022-03-07","arxiv_id":"2203.03235","repositories_listed":1,"syntology":null},{"url":"/paper/claret-pre-training-a-correlation-aware","slug":"claret-pre-training-a-correlation-aware","title":"ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification","date":"2022-03-04","arxiv_id":"2203.02225","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-inspired-few-shot-medical","slug":"anomaly-detection-inspired-few-shot-medical","title":"Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels","date":"2022-03-03","arxiv_id":"2203.02048","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/anomaly-detection-inspired-few-shot-medical#ran","syntology_url":"https://syntology.ai/paper/2203.02048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02048"}},"official":{"repos":["sha168/ADNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/variational-autoencoder-with-disentanglement","slug":"variational-autoencoder-with-disentanglement","title":"Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation","date":"2022-02-27","arxiv_id":"2202.13363","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-understanding-and-better-1","slug":"towards-better-understanding-and-better-1","title":"Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning","date":"2022-02-18","arxiv_id":"2202.09059","repositories_listed":1,"syntology":null},{"url":"/paper/bias-eliminated-semantic-refinement-for-any","slug":"bias-eliminated-semantic-refinement-for-any","title":"Bias-Eliminated Semantic Refinement for Any-Shot Learning","date":"2022-02-10","arxiv_id":"2202.04827","repositories_listed":1,"syntology":null},{"url":"/paper/generating-training-data-with-language-models","slug":"generating-training-data-with-language-models","title":"Generating Training Data with Language Models: Towards Zero-Shot Language Understanding","date":"2022-02-09","arxiv_id":"2202.04538","repositories_listed":1,"syntology":null},{"url":"/paper/cedille-a-large-autoregressive-french","slug":"cedille-a-large-autoregressive-french","title":"Cedille: A large autoregressive French language model","date":"2022-02-07","arxiv_id":"2202.03371","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-learning-as-cluster-induced-voronoi","slug":"few-shot-learning-as-cluster-induced-voronoi","title":"Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach","date":"2022-02-05","arxiv_id":"2202.02471","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 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; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/few-shot-learning-as-cluster-induced-voronoi#ran","syntology_url":"https://syntology.ai/paper/2202.02471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02471"}},"official":{"repos":["horsepurve/deepvoro"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/smoothed-embeddings-for-certified-few-shot","slug":"smoothed-embeddings-for-certified-few-shot","title":"Smoothed Embeddings for Certified Few-Shot Learning","date":"2022-02-02","arxiv_id":"2202.01186","repositories_listed":1,"syntology":null},{"url":"/paper/similarity-learning-based-few-shot-learning","slug":"similarity-learning-based-few-shot-learning","title":"Similarity Learning based Few Shot Learning for ECG Time Series Classification","date":"2022-01-31","arxiv_id":"2202.00612","repositories_listed":1,"syntology":null},{"url":"/paper/clinical-longformer-and-clinical-bigbird","slug":"clinical-longformer-and-clinical-bigbird","title":"Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences","date":"2022-01-27","arxiv_id":"2201.11838","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-diversity-in-meta-learning-1","slug":"the-effect-of-diversity-in-meta-learning-1","title":"The Effect of Diversity in Meta-Learning","date":"2022-01-27","arxiv_id":"2201.11775","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-effect-of-diversity-in-meta-learning-1#ran","syntology_url":"https://syntology.ai/paper/2201.11775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.11775"}},"official":{"repos":["RamnathKumar181/Task-Diversity-meta-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-aware-prompt-learning-for-language","slug":"instance-aware-prompt-learning-for-language","title":"Instance-aware Prompt Learning for Language Understanding and Generation","date":"2022-01-18","arxiv_id":"2201.07126","repositories_listed":1,"syntology":null},{"url":"/paper/from-examples-to-rules-neural-guided-rule","slug":"from-examples-to-rules-neural-guided-rule","title":"From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction","date":"2022-01-16","arxiv_id":"2202.00475","repositories_listed":1,"syntology":null},{"url":"/paper/rgl-a-simple-yet-effective-relation-graph","slug":"rgl-a-simple-yet-effective-relation-graph","title":"RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning","date":"2022-01-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-sample-efficient-overparameterized-1","slug":"towards-sample-efficient-overparameterized-1","title":"Towards Sample-efficient Overparameterized Meta-learning","date":"2022-01-16","arxiv_id":"2201.06142","repositories_listed":1,"syntology":null},{"url":"/paper/unifiedskg-unifying-and-multi-tasking","slug":"unifiedskg-unifying-and-multi-tasking","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","date":"2022-01-16","arxiv_id":"2201.05966","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 3 unverified","sample_list":"/paper/unifiedskg-unifying-and-multi-tasking#ran","syntology_url":"https://syntology.ai/paper/2201.05966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.05966"}},"official":{"repos":["hkunlp/unifiedskg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-level-second-order-few-shot-learning","slug":"multi-level-second-order-few-shot-learning","title":"Multi-level Second-order Few-shot Learning","date":"2022-01-15","arxiv_id":"2201.05916","repositories_listed":1,"syntology":null},{"url":"/paper/resolving-camera-position-for-a-practical","slug":"resolving-camera-position-for-a-practical","title":"Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices","date":"2022-01-09","arxiv_id":"2201.02946","repositories_listed":1,"syntology":null},{"url":"/paper/semantics-driven-attentive-few-shot-learning","slug":"semantics-driven-attentive-few-shot-learning","title":"Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples","date":"2022-01-09","arxiv_id":"2201.03043","repositories_listed":1,"syntology":null},{"url":"/paper/ease-unsupervised-discriminant-subspace","slug":"ease-unsupervised-discriminant-subspace","title":"EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-few-shot-learning-via-multi","slug":"semi-supervised-few-shot-learning-via-multi","title":"Semi-Supervised Few-Shot Learning via Multi-Factor Clustering","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-neural-network-solves-and-generates","slug":"a-neural-network-solves-and-generates","title":"A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level","date":"2021-12-31","arxiv_id":"2112.15594","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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","sample_list":"/paper/a-neural-network-solves-and-generates#ran","syntology_url":"https://syntology.ai/paper/2112.15594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.15594"}},"official":{"repos":["idrori/mathq"],"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"]}}},{"url":"/paper/surfit-learning-to-fit-surfaces-improves-few","slug":"surfit-learning-to-fit-surfaces-improves-few","title":"PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation","date":"2021-12-27","arxiv_id":"2112.13942","repositories_listed":1,"syntology":null},{"url":"/paper/n-omniglot-a-large-scale-neuromorphic-dataset","slug":"n-omniglot-a-large-scale-neuromorphic-dataset","title":"N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning","date":"2021-12-25","arxiv_id":"2112.13230","repositories_listed":1,"syntology":null},{"url":"/paper/does-maml-only-work-via-feature-re-use-a-data","slug":"does-maml-only-work-via-feature-re-use-a-data","title":"Does MAML Only Work via Feature Re-use? A Data Centric Perspective","date":"2021-12-24","arxiv_id":"2112.13137","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-variational-memory-for-few-shot-1","slug":"hierarchical-variational-memory-for-few-shot-1","title":"Hierarchical Variational Memory for Few-shot Learning Across Domains","date":"2021-12-15","arxiv_id":"2112.08181","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-limits-of-natural-language","slug":"exploring-the-limits-of-natural-language","title":"Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection","date":"2021-12-14","arxiv_id":"2112.07434","repositories_listed":1,"syntology":null},{"url":"/paper/shaping-visual-representations-with","slug":"shaping-visual-representations-with","title":"Shaping Visual Representations with Attributes for Few-Shot Recognition","date":"2021-12-13","arxiv_id":"2112.06398","repositories_listed":1,"syntology":null}],"record_sha256":"cc7f124c76889022785dc0354d2278e403ef18a612297e20fbe05dc98a33cdeb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}