{"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/set/papers/74","list_of":"/method/set","method":"SET","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":74,"pages_in_order":135,"rows_per_page":100,"rows":[7301,7400],"of":13419,"counts":{"archive_papers_tagged":13419,"with_a_code_link":4158,"where_syntology_ran_a_sample":1186,"not_listed_spam_title":0,"listed":13419,"listed_where_code_ran":1186,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":148,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":148,"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/set","prev":"/method/set/papers/73","next":"/method/set/papers/75","papers":[{"paper":null,"slug":"a-complete-set-of-quadratic-constraints-for","title":"A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizations","date":"2024-07-09","arxiv_id":"2407.06888","n_code_links":0,"syntology":null},{"paper":null,"slug":"adapting-llms-to-hebrew-unveiling-dictalm-2-0","title":"Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities","date":"2024-07-09","arxiv_id":"2407.07080","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-aesthetics-cultural-competence-in-text","slug":"beyond-aesthetics-cultural-competence-in-text","title":"Beyond Aesthetics: Cultural Competence in Text-to-Image Models","date":"2024-07-09","arxiv_id":"2407.06863","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["google-deepmind/cube","google-research-datasets/cube"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"can-virtual-staining-for-high-throughput","title":"Can virtual staining for high-throughput screening generalize?","date":"2024-07-09","arxiv_id":"2407.06979","n_code_links":0,"syntology":null},{"paper":null,"slug":"cardinality-aware-set-prediction-and-top-k","title":"Cardinality-Aware Set Prediction and Top-$k$ Classification","date":"2024-07-09","arxiv_id":"2407.07140","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-optimizers-for-fault-isolation","title":"Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives","date":"2024-07-09","arxiv_id":"2407.06557","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-triggered-recursive-impact","title":"Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids","date":"2024-07-09","arxiv_id":"2407.06948","n_code_links":0,"syntology":null},{"paper":"/paper/distributionally-robust-risk-evaluation-with-1","slug":"distributionally-robust-risk-evaluation-with-1","title":"Distributionally robust risk evaluation with an isotonic constraint","date":"2024-07-09","arxiv_id":"2407.06867","n_code_links":1,"syntology":null},{"paper":null,"slug":"divine-llamas-bias-stereotypes-stigmatization","title":"Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models","date":"2024-07-09","arxiv_id":"2407.06908","n_code_links":0,"syntology":null},{"paper":null,"slug":"empirical-analysis-of-biding-precedent","title":"Empirical analysis of Binding Precedent efficiency in the Brazilian Supreme Court via Similar Case Retrieval","date":"2024-07-09","arxiv_id":"2407.07004","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-hyperdimensional-computing-for","slug":"explainable-hyperdimensional-computing-for","title":"Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring","date":"2024-07-09","arxiv_id":"2407.07066","n_code_links":1,"syntology":null},{"paper":null,"slug":"funcevalgmn-evaluating-functional-correctness","title":"FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network","date":"2024-07-09","arxiv_id":"2407.14530","n_code_links":0,"syntology":null},{"paper":null,"slug":"fuzzy-color-model-and-clustering-algorithm","title":"Fuzzy color model and clustering algorithm for color clustering problem","date":"2024-07-09","arxiv_id":"2407.06782","n_code_links":0,"syntology":null},{"paper":null,"slug":"games-played-by-exponential-weights","title":"Games played by Exponential Weights Algorithms","date":"2024-07-09","arxiv_id":"2407.06676","n_code_links":0,"syntology":null},{"paper":null,"slug":"historical-review-of-variants-of-informal","title":"Historical Review of Variants of Informal Semantics for Logic Programs under Answer Set Semantics: GL'88, GL'91, GK'14, D-V'12","date":"2024-07-09","arxiv_id":"2407.06814","n_code_links":0,"syntology":null},{"paper":null,"slug":"iclguard-controlling-in-context-learning","title":"ICLGuard: Controlling In-Context Learning Behavior for Applicability Authorization","date":"2024-07-09","arxiv_id":"2407.06955","n_code_links":0,"syntology":null},{"paper":"/paper/ittakestwo-leveraging-peer-representations","slug":"ittakestwo-leveraging-peer-representations","title":"ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation","date":"2024-07-09","arxiv_id":"2407.07171","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["yyliu01/it2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"joint-prototype-and-coefficient-prediction","title":"Joint prototype and coefficient prediction for 3D instance segmentation","date":"2024-07-09","arxiv_id":"2407.06958","n_code_links":0,"syntology":null},{"paper":null,"slug":"learn-and-don-t-forget-adding-a-new-language","title":"Learn and Don't Forget: Adding a New Language to ASR Foundation Models","date":"2024-07-09","arxiv_id":"2407.06800","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixture-of-modules-reinventing-transformers","title":"Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules","date":"2024-07-09","arxiv_id":"2407.06677","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-decision-making-through-scenario","title":"Optimal Decision Making Through Scenario Simulations Using Large Language Models","date":"2024-07-09","arxiv_id":"2407.06486","n_code_links":0,"syntology":null},{"paper":"/paper/powerful-and-flexible-personalized-text-to","slug":"powerful-and-flexible-personalized-text-to","title":"Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning","date":"2024-07-09","arxiv_id":"2407.06642","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":2,"n_instrument":1,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["wfanyue/dpg-t2i-personalization"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"rethinking-image-to-video-adaptation-an","title":"Rethinking Image-to-Video Adaptation: An Object-centric Perspective","date":"2024-07-09","arxiv_id":"2407.06871","n_code_links":0,"syntology":null},{"paper":null,"slug":"reuse-don-t-retrain-a-recipe-for-continued","title":"Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models","date":"2024-07-09","arxiv_id":"2407.07263","n_code_links":0,"syntology":null},{"paper":"/paper/self-recognition-in-language-models","slug":"self-recognition-in-language-models","title":"Self-Recognition in Language Models","date":"2024-07-09","arxiv_id":"2407.06946","n_code_links":1,"syntology":null},{"paper":null,"slug":"solving-general-natural-language-description","title":"Solving General Natural-Language-Description Optimization Problems with Large Language Models","date":"2024-07-09","arxiv_id":"2407.07924","n_code_links":0,"syntology":null},{"paper":null,"slug":"spinex-clustering-similarity-based","title":"SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems","date":"2024-07-09","arxiv_id":"2407.07222","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-convolution-derived-multi-layered","title":"Temporal Convolution Derived Multi-Layered Reservoir Computing","date":"2024-07-09","arxiv_id":"2407.06771","n_code_links":0,"syntology":null},{"paper":"/paper/who-is-better-at-math-jenny-or-jingzhen","slug":"who-is-better-at-math-jenny-or-jingzhen","title":"Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models","date":"2024-07-09","arxiv_id":"2407.06917","n_code_links":1,"syntology":null},{"paper":null,"slug":"ada-adapter-fast-few-shot-style","title":"Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder","date":"2024-07-08","arxiv_id":"2407.05552","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-learning-of-preferences-with-a","title":"Contrastive Learning of Preferences with a Contextual InfoNCE Loss","date":"2024-07-08","arxiv_id":"2407.05898","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-data-everywhere-a-guide-for-pretraining","title":"Data, Data Everywhere: A Guide for Pretraining Dataset Construction","date":"2024-07-08","arxiv_id":"2407.06380","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-multi-modal-learning-model","title":"Data-Driven Multi-Modal Learning Model Predictive Control","date":"2024-07-08","arxiv_id":"2407.06313","n_code_links":0,"syntology":null},{"paper":null,"slug":"dirichlet-process-mixture-model-based-on","title":"Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations","date":"2024-07-08","arxiv_id":"2407.05760","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficiently-training-neural-networks-for","title":"Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets","date":"2024-07-08","arxiv_id":"2407.05876","n_code_links":0,"syntology":null},{"paper":null,"slug":"fostering-trust-and-quantifying-value-of-ai","title":"Fostering Trust and Quantifying Value of AI and ML","date":"2024-07-08","arxiv_id":"2407.05919","n_code_links":0,"syntology":null},{"paper":"/paper/generation-and-de-identification-of-indian","slug":"generation-and-de-identification-of-indian","title":"Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs","date":"2024-07-08","arxiv_id":"2407.05887","n_code_links":1,"syntology":null},{"paper":"/paper/historical-ink-semantic-shift-detection-for","slug":"historical-ink-semantic-shift-detection-for","title":"Historical Ink: Semantic Shift Detection for 19th Century Spanish","date":"2024-07-08","arxiv_id":"2407.12852","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-alphaflow-for-efficient-protein","title":"Improving AlphaFlow for Efficient Protein Ensembles Generation","date":"2024-07-08","arxiv_id":"2407.12053","n_code_links":0,"syntology":null},{"paper":"/paper/insightbench-evaluating-business-analytics","slug":"insightbench-evaluating-business-analytics","title":"InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation","date":"2024-07-08","arxiv_id":"2407.06423","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"7 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; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["servicenow/insight-bench"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-to-adapt-category-consistent-meta","title":"Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification","date":"2024-07-08","arxiv_id":"2407.05647","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-data-driven-weather-models-for","title":"Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging","date":"2024-07-08","arxiv_id":"2407.06100","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-image-captions-for-selective-whole","slug":"leveraging-image-captions-for-selective-whole","title":"Leveraging image captions for selective whole slide image annotation","date":"2024-07-08","arxiv_id":"2407.06363","n_code_links":1,"syntology":null},{"paper":"/paper/lgrnet-local-global-reciprocal-network-for","slug":"lgrnet-local-global-reciprocal-network-for","title":"LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos","date":"2024-07-08","arxiv_id":"2407.05703","n_code_links":1,"syntology":null},{"paper":"/paper/multi-label-learning-with-random-circular","slug":"multi-label-learning-with-random-circular","title":"Multi-label Learning with Random Circular Vectors","date":"2024-07-08","arxiv_id":"2407.05656","n_code_links":1,"syntology":null},{"paper":"/paper/multi-label-plant-species-classification-with","slug":"multi-label-plant-species-classification-with","title":"Multi-Label Plant Species Classification with Self-Supervised Vision Transformers","date":"2024-07-08","arxiv_id":"2407.06298","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-based-information-set","title":"Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess","date":"2024-07-08","arxiv_id":"2407.05864","n_code_links":0,"syntology":null},{"paper":"/paper/nonrigid-reconstruction-of-freehand","slug":"nonrigid-reconstruction-of-freehand","title":"Nonrigid Reconstruction of Freehand Ultrasound without a Tracker","date":"2024-07-08","arxiv_id":"2407.05767","n_code_links":1,"syntology":null},{"paper":"/paper/on-speeding-up-language-model-evaluation","slug":"on-speeding-up-language-model-evaluation","title":"On Speeding Up Language Model Evaluation","date":"2024-07-08","arxiv_id":"2407.06172","n_code_links":0,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"random-features-hopfield-networks-generalize","title":"Random Features Hopfield Networks generalize retrieval to previously unseen examples","date":"2024-07-08","arxiv_id":"2407.05658","n_code_links":0,"syntology":null},{"paper":null,"slug":"regret-analysis-of-multi-task-representation","title":"Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control","date":"2024-07-08","arxiv_id":"2407.05781","n_code_links":0,"syntology":null},{"paper":"/paper/scaling-exponents-across-parameterizations","slug":"scaling-exponents-across-parameterizations","title":"Scaling Exponents Across Parameterizations and Optimizers","date":"2024-07-08","arxiv_id":"2407.05872","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":null}},{"paper":null,"slug":"synthetic-data-for-discriminating","title":"Synthetic Data for Discriminating Serotonergic Neurons using Convolutional Neural Networks","date":"2024-07-08","arxiv_id":"2407.05701","n_code_links":0,"syntology":null},{"paper":null,"slug":"t2vsafetybench-evaluating-the-safety-of-text","title":"T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models","date":"2024-07-08","arxiv_id":"2407.05965","n_code_links":0,"syntology":null},{"paper":null,"slug":"targo-benchmarking-target-driven-object","title":"TARGO: Benchmarking Target-driven Object Grasping under Occlusions","date":"2024-07-08","arxiv_id":"2407.06168","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-braille-an-end-to-end-tool-for-chinese","title":"Vision-Braille: An End-to-End Tool for Chinese Braille Image-to-Text Translation","date":"2024-07-08","arxiv_id":"2407.06048","n_code_links":0,"syntology":null},{"paper":null,"slug":"biomedical-nested-ner-with-large-language","title":"Biomedical Nested NER with Large Language Model and UMLS Heuristics","date":"2024-07-07","arxiv_id":"2407.05480","n_code_links":0,"syntology":null},{"paper":null,"slug":"disciplined-geodesically-convex-programming","title":"Disciplined Geodesically Convex Programming","date":"2024-07-07","arxiv_id":"2407.05261","n_code_links":0,"syntology":null},{"paper":null,"slug":"einstein-from-noise-statistical-analysis","title":"Einstein from Noise: Statistical Analysis","date":"2024-07-07","arxiv_id":"2407.05277","n_code_links":0,"syntology":null},{"paper":"/paper/elecbench-a-power-dispatch-evaluation","slug":"elecbench-a-power-dispatch-evaluation","title":"ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models","date":"2024-07-07","arxiv_id":"2407.05365","n_code_links":1,"syntology":null},{"paper":"/paper/just-read-twice-closing-the-recall-gap-for","slug":"just-read-twice-closing-the-recall-gap-for","title":"Just read twice: closing the recall gap for recurrent language models","date":"2024-07-07","arxiv_id":"2407.05483","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["HazyResearch/prefix-linear-attention"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"model-agnostic-meta-learners-for-estimating","title":"Model-agnostic meta-learners for estimating heterogeneous treatment effects over time","date":"2024-07-07","arxiv_id":"2407.05287","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-power-of-data-augmentation-for-head","slug":"on-the-power-of-data-augmentation-for-head","title":"On the power of data augmentation for head pose estimation","date":"2024-07-07","arxiv_id":"2407.05357","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-switch-the-ladder-and-the-matrix-models","title":"The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems","date":"2024-07-07","arxiv_id":"2407.05341","n_code_links":0,"syntology":null},{"paper":"/paper/ultraedit-instruction-based-fine-grained","slug":"ultraedit-instruction-based-fine-grained","title":"UltraEdit: Instruction-based Fine-Grained Image Editing at Scale","date":"2024-07-07","arxiv_id":"2407.05282","n_code_links":1,"syntology":{"ran":10,"of":17,"n_ran_checked":9,"n_instrument":1,"unverified":7,"pointer_only":17,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["pkunlp-icler/ultraedit"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-marginal-distributionally-robust-kalman","title":"A Marginal Distributionally Robust Kalman Filter for Centralized Fusion","date":"2024-07-06","arxiv_id":"2407.05052","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-of-test-time-contrastive-concepts-for","title":"Test-time Contrastive Concepts for Open-world Semantic Segmentation","date":"2024-07-06","arxiv_id":"2407.05061","n_code_links":0,"syntology":null},{"paper":null,"slug":"advancing-algorithmic-approaches-to","title":"Advancing Algorithmic Approaches to Probabilistic Argumentation under the Constellation Approach","date":"2024-07-06","arxiv_id":"2407.05058","n_code_links":0,"syntology":null},{"paper":null,"slug":"collaborative-estimation-of-real-valued","title":"Collaborative Estimation of Real Valued Function by Two Agents and a Fusion Center with Knowledge Exchange","date":"2024-07-06","arxiv_id":"2407.05136","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-lingual-word-alignment-for-asean","title":"Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning","date":"2024-07-06","arxiv_id":"2407.05054","n_code_links":0,"syntology":null},{"paper":"/paper/dmtg-one-shot-differentiable-multi-task","slug":"dmtg-one-shot-differentiable-multi-task","title":"DMTG: One-Shot Differentiable Multi-Task Grouping","date":"2024-07-06","arxiv_id":"2407.05082","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":6,"n_instrument":1,"unverified":2,"pointer_only":9,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ethanygao/dmtg"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/flowlearn-evaluating-large-vision-language","slug":"flowlearn-evaluating-large-vision-language","title":"FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding","date":"2024-07-06","arxiv_id":"2407.05183","n_code_links":1,"syntology":null},{"paper":"/paper/granular-privacy-control-for-geolocation-with","slug":"granular-privacy-control-for-geolocation-with","title":"Granular Privacy Control for Geolocation with Vision Language Models","date":"2024-07-06","arxiv_id":"2407.04952","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ethanm88/GPTGeoChat"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"identifying-intensity-of-the-structure-and","title":"Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines","date":"2024-07-06","arxiv_id":"2407.05154","n_code_links":0,"syntology":null},{"paper":null,"slug":"nash-incentive-compatible-online-mechanism","title":"Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning","date":"2024-07-06","arxiv_id":"2407.04898","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-scientific-method-the-role-of","title":"On the Scientific Method: The Role of Hypotheses and Involved Mathematics","date":"2024-07-06","arxiv_id":"2407.06225","n_code_links":0,"syntology":null},{"paper":"/paper/preference-distillation-for-personalized","slug":"preference-distillation-for-personalized","title":"Preference Distillation for Personalized Generative Recommendation","date":"2024-07-06","arxiv_id":"2407.05033","n_code_links":1,"syntology":null},{"paper":null,"slug":"releasing-malevolence-from-benevolence-the","title":"Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning","date":"2024-07-06","arxiv_id":"2407.05112","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-reachability-problem-for-neural-network","title":"The Reachability Problem for Neural-Network Control Systems","date":"2024-07-06","arxiv_id":"2407.04988","n_code_links":0,"syntology":null},{"paper":null,"slug":"trace-transformer-based-attribution-using","title":"TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs","date":"2024-07-06","arxiv_id":"2407.04981","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-structure-based-three","slug":"benchmarking-structure-based-three","title":"Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters","date":"2024-07-05","arxiv_id":"2407.04424","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":["bbaillif/genbench3d"],"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/brain-age-estimation-with-a-greedy-dual","slug":"brain-age-estimation-with-a-greedy-dual","title":"Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets","date":"2024-07-05","arxiv_id":"2407.04808","n_code_links":1,"syntology":null},{"paper":null,"slug":"consistent-conjectures-in-dynamic-matching","title":"Consistent Conjectures in Dynamic Matching Markets","date":"2024-07-05","arxiv_id":"2407.04857","n_code_links":0,"syntology":null},{"paper":"/paper/controlling-whisper-universal-acoustic","slug":"controlling-whisper-universal-acoustic","title":"Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models","date":"2024-07-05","arxiv_id":"2407.04482","n_code_links":1,"syntology":null},{"paper":null,"slug":"fair-submodular-cover","title":"Fair Submodular Cover","date":"2024-07-05","arxiv_id":"2407.04804","n_code_links":0,"syntology":null},{"paper":"/paper/neufair-neural-network-fairness-repair-with","slug":"neufair-neural-network-fairness-repair-with","title":"NeuFair: Neural Network Fairness Repair with Dropout","date":"2024-07-05","arxiv_id":"2407.04268","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimal-estimators-of-cross-partial","title":"Optimal estimators of cross-partial derivatives and surrogates of functions","date":"2024-07-05","arxiv_id":"2407.11035","n_code_links":0,"syntology":null},{"paper":"/paper/poprero-a-new-dataset-for-popularity","slug":"poprero-a-new-dataset-for-popularity","title":"PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts","date":"2024-07-05","arxiv_id":"2407.04541","n_code_links":1,"syntology":null},{"paper":"/paper/robust-q-learning-for-finite-ambiguity-sets","slug":"robust-q-learning-for-finite-ambiguity-sets","title":"Robust Q-Learning for finite ambiguity sets","date":"2024-07-05","arxiv_id":"2407.04259","n_code_links":1,"syntology":null},{"paper":null,"slug":"sam-fewshot-finetuning-for-anatomical","title":"SAM Fewshot Finetuning for Anatomical Segmentation in Medical Images","date":"2024-07-05","arxiv_id":"2407.04651","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-segmentation-via-embedding","title":"Semi-Supervised Segmentation via Embedding Matching","date":"2024-07-05","arxiv_id":"2407.04638","n_code_links":0,"syntology":null},{"paper":null,"slug":"spinex-similarity-based-predictions-with","title":"SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection","date":"2024-07-05","arxiv_id":"2407.04760","n_code_links":0,"syntology":null},{"paper":null,"slug":"ssp-gnn-learning-to-track-via-bilevel","title":"SSP-GNN: Learning to Track via Bilevel Optimization","date":"2024-07-05","arxiv_id":"2407.04308","n_code_links":0,"syntology":null},{"paper":null,"slug":"success-or-failure-analyzing-segmentation","title":"Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process","date":"2024-07-05","arxiv_id":"2407.04519","n_code_links":0,"syntology":null},{"paper":"/paper/trustworthy-classification-through-rank-based","slug":"trustworthy-classification-through-rank-based","title":"Trustworthy Classification through Rank-Based Conformal Prediction Sets","date":"2024-07-05","arxiv_id":"2407.04407","n_code_links":1,"syntology":null},{"paper":null,"slug":"unraveling-radiomics-complexity-strategies","title":"Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling","date":"2024-07-05","arxiv_id":"2407.04888","n_code_links":0,"syntology":null},{"paper":null,"slug":"vcd-texture-variance-alignment-based-3d-2d-co","title":"VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing","date":"2024-07-05","arxiv_id":"2407.04461","n_code_links":0,"syntology":null},{"paper":null,"slug":"vrsd-rethinking-similarity-and-diversity-for","title":"VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models","date":"2024-07-05","arxiv_id":"2407.04573","n_code_links":0,"syntology":null},{"paper":null,"slug":"10-years-of-fair-representations-challenges","title":"10 Years of Fair Representations: Challenges and Opportunities","date":"2024-07-04","arxiv_id":"2407.03834","n_code_links":0,"syntology":null},{"paper":"/paper/adapt-multimodal-learning-for-detecting","slug":"adapt-multimodal-learning-for-detecting","title":"ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities","date":"2024-07-04","arxiv_id":"2407.03836","n_code_links":1,"syntology":null}],"record_sha256":"d44d1d85207e87f2568fc2c10b9f7a20e09f5afefcc2ebaa3e036092b0dc127d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}