{"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/test/papers/38","list_of":"/method/test","method":"Test","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":38,"pages_in_order":65,"rows_per_page":100,"rows":[3701,3800],"of":6434,"counts":{"archive_papers_tagged":6434,"with_a_code_link":2339,"where_syntology_ran_a_sample":517,"not_listed_spam_title":0,"listed":6434,"listed_where_code_ran":517,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":428,"every_run_a_failure_of_syntologys_instrument":89,"listed_with_a_run_with_no_instrument_failure":428,"listed_every_run_a_failure_of_syntologys_instrument":89,"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/test","prev":"/method/test/papers/37","next":"/method/test/papers/39","papers":[{"paper":null,"slug":"the-effect-of-modeling-human-rationality","title":"The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types","date":"2022-08-23","arxiv_id":"2208.10687","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-deep-learning-s-performance-data-are","title":"Why Deep Learning's Performance Data Are Misleading","date":"2022-08-23","arxiv_id":"2208.11228","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-diverse-large-scale-building-dataset-and-a","title":"A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction","date":"2022-08-22","arxiv_id":"2208.10004","n_code_links":0,"syntology":null},{"paper":"/paper/an-anomaly-detection-approach-for-backdoored","slug":"an-anomaly-detection-approach-for-backdoored","title":"An anomaly detection approach for backdoored neural networks: face recognition as a case study","date":"2022-08-22","arxiv_id":"2208.10231","n_code_links":1,"syntology":null},{"paper":null,"slug":"ebsnor-event-based-snow-removal-by-optimal","title":"EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding","date":"2022-08-22","arxiv_id":"2208.10581","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-auxiliary-or-adversarial-tasks","title":"Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding","date":"2022-08-22","arxiv_id":"2208.10077","n_code_links":0,"syntology":null},{"paper":"/paper/individual-tree-detection-in-large-scale","slug":"individual-tree-detection-in-large-scale","title":"Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery","date":"2022-08-22","arxiv_id":"2208.10607","n_code_links":2,"syntology":null},{"paper":null,"slug":"learning-invariant-representations-under","title":"Learning Invariant Representations under General Interventions on the Response","date":"2022-08-22","arxiv_id":"2208.10027","n_code_links":0,"syntology":null},{"paper":"/paper/one-model-any-csp-graph-neural-networks-as","slug":"one-model-any-csp-graph-neural-networks-as","title":"One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction","date":"2022-08-22","arxiv_id":"2208.10227","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["toenshoff/runcsp-pytorch"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/posebert-a-generic-transformer-module-for","slug":"posebert-a-generic-transformer-module-for","title":"PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling","date":"2022-08-22","arxiv_id":"2208.10211","n_code_links":1,"syntology":null},{"paper":"/paper/ribac-towards-robust-and-imperceptible","slug":"ribac-towards-robust-and-imperceptible","title":"RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN","date":"2022-08-22","arxiv_id":"2208.10608","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":0,"n_instrument":1,"unverified":3,"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) · 3 unverified","official":{"repos":["huyvnphan/eccv2022-ribac"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scalable-hybrid-classification-regression","title":"Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring","date":"2022-08-22","arxiv_id":"2208.10638","n_code_links":0,"syntology":null},{"paper":"/paper/shapelet-based-counterfactual-explanations","slug":"shapelet-based-counterfactual-explanations","title":"Shapelet-Based Counterfactual Explanations for Multivariate Time Series","date":"2022-08-22","arxiv_id":"2208.10462","n_code_links":1,"syntology":null},{"paper":null,"slug":"byzantines-can-also-learn-from-history-fall","title":"Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning","date":"2022-08-21","arxiv_id":"2208.09894","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-tests-in-online-decision-making","title":"Robust Tests in Online Decision-Making","date":"2022-08-21","arxiv_id":"2208.09819","n_code_links":0,"syntology":null},{"paper":"/paper/a-multi-head-model-for-continual-learning-via","slug":"a-multi-head-model-for-continual-learning-via","title":"A Multi-Head Model for Continual Learning via Out-of-Distribution Replay","date":"2022-08-20","arxiv_id":"2208.09734","n_code_links":3,"syntology":null},{"paper":"/paper/an-ensemble-meta-estimator-to-predict-source","slug":"an-ensemble-meta-estimator-to-predict-source","title":"An ensemble meta-estimator to predict source code testability","date":"2022-08-20","arxiv_id":"2208.09614","n_code_links":1,"syntology":null},{"paper":"/paper/artifact-based-domain-generalization-of-skin","slug":"artifact-based-domain-generalization-of-skin","title":"Artifact-Based Domain Generalization of Skin Lesion Models","date":"2022-08-20","arxiv_id":"2208.09756","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-predict-test-effectiveness","title":"Learning to predict test effectiveness","date":"2022-08-20","arxiv_id":"2208.09623","n_code_links":0,"syntology":null},{"paper":"/paper/net2brain-a-toolbox-to-compare-artificial","slug":"net2brain-a-toolbox-to-compare-artificial","title":"Net2Brain: A Toolbox to compare artificial vision models with human brain responses","date":"2022-08-20","arxiv_id":"2208.09677","n_code_links":1,"syntology":null},{"paper":null,"slug":"offline-handwritten-mathematical-recognition","title":"Offline Handwritten Mathematical Recognition using Adversarial Learning and Transformers","date":"2022-08-20","arxiv_id":"2208.09662","n_code_links":0,"syntology":null},{"paper":"/paper/persuasion-strategies-in-advertisements","slug":"persuasion-strategies-in-advertisements","title":"Persuasion Strategies in Advertisements","date":"2022-08-20","arxiv_id":"2208.09626","n_code_links":1,"syntology":null},{"paper":"/paper/quo-vadis-hybrid-machine-learning-meta-model","slug":"quo-vadis-hybrid-machine-learning-meta-model","title":"Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations","date":"2022-08-20","arxiv_id":"2208.12248","n_code_links":1,"syntology":null},{"paper":"/paper/representing-knowledge-by-spans-a-knowledge","slug":"representing-knowledge-by-spans-a-knowledge","title":"SPOT: Knowledge-Enhanced Language Representations for Information Extraction","date":"2022-08-20","arxiv_id":"2208.09625","n_code_links":0,"syntology":null},{"paper":null,"slug":"unit-selection-with-nonbinary-treatment-and","title":"Unit Selection with Nonbinary Treatment and Effect","date":"2022-08-20","arxiv_id":"2208.09569","n_code_links":0,"syntology":null},{"paper":"/paper/a-risk-sensitive-approach-to-policy","slug":"a-risk-sensitive-approach-to-policy","title":"A Risk-Sensitive Approach to Policy Optimization","date":"2022-08-19","arxiv_id":"2208.09106","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-investigation-into-neuromorphic-ics-using","title":"An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits","date":"2022-08-19","arxiv_id":"2210.15593","n_code_links":0,"syntology":null},{"paper":"/paper/cross-domain-evaluation-of-a-deep-learning","slug":"cross-domain-evaluation-of-a-deep-learning","title":"Cross-Domain Evaluation of a Deep Learning-Based Type Inference System","date":"2022-08-19","arxiv_id":"2208.09189","n_code_links":1,"syntology":null},{"paper":null,"slug":"daft-distilling-adversarially-fine-tuned","title":"DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization","date":"2022-08-19","arxiv_id":"2208.09139","n_code_links":0,"syntology":null},{"paper":null,"slug":"discourse-cohesion-evaluation-for-document","title":"Discourse Cohesion Evaluation for Document-Level Neural Machine Translation","date":"2022-08-19","arxiv_id":"2208.09118","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-clinical-event-extraction-from","title":"End-to-end Clinical Event Extraction from Chinese Electronic Health Record","date":"2022-08-19","arxiv_id":"2208.09354","n_code_links":0,"syntology":null},{"paper":null,"slug":"graphtta-test-time-adaptation-on-graph-neural","title":"GraphTTA: Test Time Adaptation on Graph Neural Networks","date":"2022-08-19","arxiv_id":"2208.09126","n_code_links":0,"syntology":null},{"paper":"/paper/ttt-ucdr-test-time-training-for-universal","slug":"ttt-ucdr-test-time-training-for-universal","title":"Test-time Training for Data-efficient UCDR","date":"2022-08-19","arxiv_id":"2208.09198","n_code_links":1,"syntology":null},{"paper":null,"slug":"ultron-an-ultimate-retriever-on-corpus-with-a","title":"Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer","date":"2022-08-19","arxiv_id":"2208.09257","n_code_links":0,"syntology":null},{"paper":null,"slug":"cope-end-to-end-trainable-constant-runtime","title":"COPE: End-to-end trainable Constant Runtime Object Pose Estimation","date":"2022-08-18","arxiv_id":"2208.08807","n_code_links":0,"syntology":null},{"paper":null,"slug":"diet-conditional-independence-testing-with","title":"DIET: Conditional independence testing with marginal dependence measures of residual information","date":"2022-08-18","arxiv_id":"2208.08579","n_code_links":0,"syntology":null},{"paper":"/paper/differentiable-architecture-search-with-1","slug":"differentiable-architecture-search-with-1","title":"Differentiable Architecture Search with Random Features","date":"2022-08-18","arxiv_id":"2208.08835","n_code_links":0,"syntology":null},{"paper":"/paper/domain-specific-risk-minimization","slug":"domain-specific-risk-minimization","title":"Domain-Specific Risk Minimization for Out-of-Distribution Generalization","date":"2022-08-18","arxiv_id":"2208.08661","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-continual-test-time-adaptation-for","slug":"evaluating-continual-test-time-adaptation-for","title":"Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts","date":"2022-08-18","arxiv_id":"2208.08767","n_code_links":1,"syntology":null},{"paper":"/paper/mere-contrastive-learning-for-cross-domain","slug":"mere-contrastive-learning-for-cross-domain","title":"Mere Contrastive Learning for Cross-Domain Sentiment Analysis","date":"2022-08-18","arxiv_id":"2208.08678","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimal-energy-management-in-autonomous-power","title":"Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control","date":"2022-08-18","arxiv_id":"2208.08953","n_code_links":0,"syntology":null},{"paper":"/paper/prompt-vision-transformer-for-domain","slug":"prompt-vision-transformer-for-domain","title":"Prompt Vision Transformer for Domain Generalization","date":"2022-08-18","arxiv_id":"2208.08914","n_code_links":1,"syntology":{"ran":11,"of":15,"n_ran_checked":7,"n_instrument":4,"unverified":4,"pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["zhengzangw/DoPrompt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/shadows-aren-t-so-dangerous-after-all-a-fast","slug":"shadows-aren-t-so-dangerous-after-all-a-fast","title":"Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks","date":"2022-08-18","arxiv_id":"2208.09285","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-active-distribution-network-flexibility","title":"Using Active Distribution Network Flexibility to Increase Transmission System Voltage Stability Margins","date":"2022-08-18","arxiv_id":"2208.08920","n_code_links":0,"syntology":null},{"paper":"/paper/using-large-language-models-to-simulate","slug":"using-large-language-models-to-simulate","title":"Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies","date":"2022-08-18","arxiv_id":"2208.10264","n_code_links":2,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["gatiaher/using-large-language-models-to-replicate-human-subject-studies"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"attackar-attack-of-the-evolutionary-adversary","title":"An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks","date":"2022-08-17","arxiv_id":"2208.08297","n_code_links":0,"syntology":null},{"paper":"/paper/conformal-inference-for-online-prediction","slug":"conformal-inference-for-online-prediction","title":"Conformal Inference for Online Prediction with Arbitrary Distribution Shifts","date":"2022-08-17","arxiv_id":"2208.08401","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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) · 0 unverified","official":{"repos":["isgibbs/DtACI"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"constrained-few-shot-learning-human-like-low","title":"Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification","date":"2022-08-17","arxiv_id":"2208.08089","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-unlabeled-data-for-target-oriented","slug":"exploiting-unlabeled-data-for-target-oriented","title":"Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction","date":"2022-08-17","arxiv_id":"2208.08280","n_code_links":1,"syntology":null},{"paper":null,"slug":"label-flipping-data-poisoning-attack-against","title":"Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System","date":"2022-08-17","arxiv_id":"2208.08433","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-observability-of-gaussian-models-using","title":"On the Observability of Gaussian Models using Discrete Density Approximations","date":"2022-08-17","arxiv_id":"2208.08870","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-long-tailed-recognition-in-a-dynamic","title":"Open Long-Tailed Recognition in a Dynamic World","date":"2022-08-17","arxiv_id":"2208.08349","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-anomaly-detection-based-on","title":"Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation","date":"2022-08-17","arxiv_id":"2208.08265","n_code_links":0,"syntology":null},{"paper":null,"slug":"superior-generalization-of-smaller-models-in","title":"Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise","date":"2022-08-17","arxiv_id":"2208.08003","n_code_links":0,"syntology":null},{"paper":"/paper/the-conversational-short-phrase-speaker","slug":"the-conversational-short-phrase-speaker","title":"The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines","date":"2022-08-17","arxiv_id":"2208.08042","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-artificial-neural-networks-can-tell-us","title":"What Artificial Neural Networks Can Tell Us About Human Language Acquisition","date":"2022-08-17","arxiv_id":"2208.07998","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-deep-feature-based-deformable-image","title":"A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images","date":"2022-08-16","arxiv_id":"2208.07655","n_code_links":0,"syntology":null},{"paper":null,"slug":"bertifying-sinhala-a-comprehensive-analysis","title":"BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification","date":"2022-08-16","arxiv_id":"2208.07864","n_code_links":0,"syntology":null},{"paper":null,"slug":"blind-users-accessing-their-training-images","title":"Blind Users Accessing Their Training Images in Teachable Object Recognizers","date":"2022-08-16","arxiv_id":"2208.07968","n_code_links":0,"syntology":null},{"paper":null,"slug":"casual-indoor-hdr-radiance-capture-from","title":"Casual Indoor HDR Radiance Capture from Omnidirectional Images","date":"2022-08-16","arxiv_id":"2208.07903","n_code_links":0,"syntology":null},{"paper":"/paper/context-aware-streaming-perception-in-dynamic","slug":"context-aware-streaming-perception-in-dynamic","title":"Context-Aware Streaming Perception in Dynamic Environments","date":"2022-08-16","arxiv_id":"2208.07479","n_code_links":1,"syntology":null},{"paper":null,"slug":"error-parity-fairness-testing-for-group","title":"Error Parity Fairness: Testing for Group Fairness in Regression Tasks","date":"2022-08-16","arxiv_id":"2208.08279","n_code_links":0,"syntology":null},{"paper":"/paper/gradual-test-time-adaptation-by-self-training","slug":"gradual-test-time-adaptation-by-self-training","title":"Introducing Intermediate Domains for Effective Self-Training during Test-Time","date":"2022-08-16","arxiv_id":"2208.07736","n_code_links":1,"syntology":null},{"paper":"/paper/machine-learning-based-test-smell-detection","slug":"machine-learning-based-test-smell-detection","title":"Machine Learning-Based Test Smell Detection","date":"2022-08-16","arxiv_id":"2208.07574","n_code_links":1,"syntology":null},{"paper":null,"slug":"parallel-hierarchical-transformer-with","title":"Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization","date":"2022-08-16","arxiv_id":"2208.07845","n_code_links":0,"syntology":null},{"paper":null,"slug":"sierra-a-modular-framework-for-research-1","title":"SIERRA: A Modular Framework for Research Automation and Reproducibility","date":"2022-08-16","arxiv_id":"2208.07805","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-the-diffusion-of-responsibility","title":"A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning","date":"2022-08-16","arxiv_id":"2208.07753","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-of-impact-of-emotions-on-target","slug":"analysis-of-impact-of-emotions-on-target","title":"Analysis of impact of emotions on target speech extraction and speech separation","date":"2022-08-15","arxiv_id":"2208.07091","n_code_links":1,"syntology":null},{"paper":"/paper/bow3d-bag-of-words-for-real-time-loop-closing","slug":"bow3d-bag-of-words-for-real-time-loop-closing","title":"BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM","date":"2022-08-15","arxiv_id":"2208.07473","n_code_links":2,"syntology":null},{"paper":null,"slug":"c3-dino-joint-contrastive-and-non-contrastive","title":"C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification","date":"2022-08-15","arxiv_id":"2208.07446","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-quantum-natural-gradient-descent","title":"Federated Quantum Natural Gradient Descent for Quantum Federated Learning","date":"2022-08-15","arxiv_id":"2209.00564","n_code_links":0,"syntology":null},{"paper":"/paper/online-pole-segmentation-on-range-images-for","slug":"online-pole-segmentation-on-range-images-for","title":"Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments","date":"2022-08-15","arxiv_id":"2208.07364","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PRBonn/pole-localization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"predictive-data-calibration-for-linear","title":"Predictive Data Calibration for Linear Correlation Significance Testing","date":"2022-08-15","arxiv_id":"2208.07081","n_code_links":0,"syntology":null},{"paper":null,"slug":"preventing-deterioration-of-classification","title":"Preventing Deterioration of Classification Accuracy in Predictive Coding Networks","date":"2022-08-15","arxiv_id":"2208.07114","n_code_links":0,"syntology":null},{"paper":"/paper/reward-design-for-an-online-reinforcement","slug":"reward-design-for-an-online-reinforcement","title":"Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care","date":"2022-08-15","arxiv_id":"2208.07406","n_code_links":1,"syntology":null},{"paper":null,"slug":"signed-graph-neural-networks-a-frequency","title":"Signed Graph Neural Networks: A Frequency Perspective","date":"2022-08-15","arxiv_id":"2208.07323","n_code_links":0,"syntology":null},{"paper":"/paper/wifi-based-distance-estimation-using","slug":"wifi-based-distance-estimation-using","title":"WiFi Based Distance Estimation Using Supervised Machine Learning","date":"2022-08-15","arxiv_id":"2208.07190","n_code_links":1,"syntology":null},{"paper":"/paper/z-bert-a-a-zero-shot-pipeline-for-unknown","slug":"z-bert-a-a-zero-shot-pipeline-for-unknown","title":"Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection","date":"2022-08-15","arxiv_id":"2208.07084","n_code_links":2,"syntology":null},{"paper":null,"slug":"limits-of-an-ai-program-for-solving-college","title":"Limits of an AI program for solving college math problems","date":"2022-08-14","arxiv_id":"2208.06906","n_code_links":0,"syntology":null},{"paper":"/paper/mtcsnn-multi-task-clinical-siamese-neural","slug":"mtcsnn-multi-task-clinical-siamese-neural","title":"MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction","date":"2022-08-14","arxiv_id":"2208.06917","n_code_links":1,"syntology":null},{"paper":"/paper/multinomial-logistic-regression-algorithms","slug":"multinomial-logistic-regression-algorithms","title":"Multinomial Logistic Regression Algorithms via Quadratic Gradient","date":"2022-08-14","arxiv_id":"2208.06828","n_code_links":1,"syntology":null},{"paper":null,"slug":"tl-dw-summarizing-instructional-videos-with","title":"TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency","date":"2022-08-14","arxiv_id":"2208.06773","n_code_links":0,"syntology":null},{"paper":"/paper/tripjudge-a-relevance-judgement-test","slug":"tripjudge-a-relevance-judgement-test","title":"TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval","date":"2022-08-14","arxiv_id":"2208.06936","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":0,"n_instrument":4,"unverified":1,"pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["sophiaalthammer/tripjudge"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-study-of-demographic-bias-in-cnn-based","title":"A Study of Demographic Bias in CNN-based Brain MR Segmentation","date":"2022-08-13","arxiv_id":"2208.06613","n_code_links":0,"syntology":null},{"paper":null,"slug":"binbert-binary-code-understanding-with-a-fine","title":"BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer","date":"2022-08-13","arxiv_id":"2208.06692","n_code_links":0,"syntology":null},{"paper":"/paper/demo-rhythmedge-enabling-contactless-heart","slug":"demo-rhythmedge-enabling-contactless-heart","title":"Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge","date":"2022-08-13","arxiv_id":"2208.06572","n_code_links":1,"syntology":null},{"paper":null,"slug":"entropy-induced-pruning-framework-for","title":"Entropy Induced Pruning Framework for Convolutional Neural Networks","date":"2022-08-13","arxiv_id":"2208.06660","n_code_links":0,"syntology":null},{"paper":"/paper/expansionnet-v2-block-static-expansion-in","slug":"expansionnet-v2-block-static-expansion-in","title":"Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning","date":"2022-08-13","arxiv_id":"2208.06551","n_code_links":1,"syntology":null},{"paper":null,"slug":"feasibility-layer-aided-machine-learning","title":"Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations","date":"2022-08-13","arxiv_id":"2208.06742","n_code_links":0,"syntology":null},{"paper":null,"slug":"gedi-a-graph-based-end-to-end-data-imputation","title":"GEDI: A Graph-based End-to-end Data Imputation Framework","date":"2022-08-13","arxiv_id":"2208.06573","n_code_links":0,"syntology":null},{"paper":null,"slug":"metricbert-text-representation-learning-via","title":"MetricBERT: Text Representation Learning via Self-Supervised Triplet Training","date":"2022-08-13","arxiv_id":"2208.06610","n_code_links":0,"syntology":null},{"paper":null,"slug":"opinion-market-model-stemming-far-right","title":"Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions","date":"2022-08-13","arxiv_id":"2208.06620","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-knowledge-distillation-based-backdoor","title":"A Knowledge Distillation-Based Backdoor Attack in Federated Learning","date":"2022-08-12","arxiv_id":"2208.06176","n_code_links":0,"syntology":null},{"paper":null,"slug":"grid-impact-analysis-and-mitigation-of-en","title":"Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles","date":"2022-08-12","arxiv_id":"2208.06518","n_code_links":0,"syntology":null},{"paper":null,"slug":"omnivoxel-a-fast-and-precise-reconstruction","title":"OmniVoxel: A Fast and Precise Reconstruction Method of Omnidirectional Neural Radiance Field","date":"2022-08-12","arxiv_id":"2208.06335","n_code_links":0,"syntology":null},{"paper":"/paper/a-probabilistic-framework-for-mutation","slug":"a-probabilistic-framework-for-mutation","title":"A Probabilistic Framework for Mutation Testing in Deep Neural Networks","date":"2022-08-11","arxiv_id":"2208.06018","n_code_links":1,"syntology":null},{"paper":null,"slug":"dbias-detecting-biases-and-ensuring-fairness","title":"Dbias: Detecting biases and ensuring Fairness in news articles","date":"2022-08-11","arxiv_id":"2208.05777","n_code_links":0,"syntology":null},{"paper":null,"slug":"interactive-code-generation-via-test-driven","title":"Interactive Code Generation via Test-Driven User-Intent Formalization","date":"2022-08-11","arxiv_id":"2208.05950","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-agent-reinforcement-learning-with-graph-1","title":"Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets","date":"2022-08-11","arxiv_id":"2208.06242","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimatch-multi-task-learning-for-semi","title":"MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization","date":"2022-08-11","arxiv_id":"2208.05853","n_code_links":0,"syntology":null}],"record_sha256":"df6b97b43e3ed512a14de02f19b8d0f29eed8826aece1f7e3d09220b0770dd34","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}