repo_url
stringlengths 26
200
| paper_url
stringlengths 36
81
| paper_title
stringlengths 3
229
β | paper_arxiv_id
stringlengths 9
16
| framework
stringclasses 9
values | official_status
stringclasses 2
values | mention_source
stringclasses 3
values |
|---|---|---|---|---|---|---|
https://github.com/Center-for-Diabetes-Technology/TransformerBasedBGPrediction
|
https://paperswithcode.com/paper/a-comparative-study-of-transformer-based-2
|
A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction
|
2505.08821
|
pytorch
|
β
Official
|
β No Mention
|
https://github.com/3x-dev/Comparative-Study-of-Bias-and-Accuracy-in-Multilingual-LLMs-for-Cross-Language-Claim-Verification
|
https://paperswithcode.com/paper/a-comparative-study-of-translation-bias-and
|
A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification
|
2410.10303
|
none
|
β
Official
|
β No Mention
|
https://github.com/Glenj01/Medical-Coding
|
https://paperswithcode.com/paper/a-comparative-study-on-automatic-coding-of
|
A Comparative Study on Automatic Coding of Medical Letters with Explainability
|
2407.13638
|
tf
|
β
Official
|
π In Paper
|
https://github.com/slampai/generative-models-for-highres-solar-images
|
https://paperswithcode.com/paper/a-comparative-study-on-generative-models-for
|
A Comparative Study on Generative Models for High Resolution Solar Observation Imaging
|
2304.07169
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/sen33/end-to-end-dialogue-system
|
https://paperswithcode.com/paper/a-comparative-study-on-language-models-for-1
|
A Comparative Study on Language Models for Task-Oriented Dialogue Systems
|
2201.08687
|
none
|
β
Official
|
π In Paper
|
https://github.com/nii-yamagishilab/antispoofing-watermark
|
https://paperswithcode.com/paper/a-comparative-study-on-proactive-and-passive
|
A Comparative Study on Proactive and Passive Detection of Deepfake Speech
|
2506.14398
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/open-source-o1/o1_reasoning_patterns_study
|
https://paperswithcode.com/paper/a-comparative-study-on-reasoning-patterns-of
|
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
|
2410.13639
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/jantory/gnn-comparison
|
https://paperswithcode.com/paper/a-comparative-study-on-robust-graph-neural
|
A Comparative Study on Robust Graph Neural Networks to Structural Noises
|
2112.06070
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/akalino/semantic-structural-sentences
|
https://paperswithcode.com/paper/a-comparative-study-on-structural-and
|
A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
|
2009.11226
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/minnesotanlp/eyestyliency
|
https://paperswithcode.com/paper/a-comparative-study-on-textual-saliency-of
|
A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models
|
2212.09873
|
none
|
β
Official
|
π In Paper
|
https://github.com/SineZHAN/deepALplus
|
https://paperswithcode.com/paper/a-comparative-survey-of-deep-active-learning
|
A Comparative Survey of Deep Active Learning
|
2203.13450
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/mauriciogtec/bsd-and-rl
|
https://paperswithcode.com/paper/a-comparative-tutorial-of-bayesian-sequential
|
A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning
|
2205.04023
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/vis-sustech/visual-analytics-for-emo-algorithm-comparison
|
https://paperswithcode.com/paper/a-comparative-visual-analytics-framework-for
|
A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization
|
2308.05640
|
none
|
β
Official
|
π In Paper
|
https://github.com/royforestano/2023_gsoc_ml4sci_qmlhep_gnn
|
https://paperswithcode.com/paper/a-comparison-between-invariant-and
|
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
|
2311.18672
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/cair/tsetlin-machine-deep-neural-network-recommendation-system-comparison
|
https://paperswithcode.com/paper/a-comparison-between-tsetlin-machines-and
|
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
|
2212.10136
|
none
|
β
Official
|
π In Paper
|
https://github.com/mingchixu/markov_switching_hurdle_code
|
https://paperswithcode.com/paper/a-comparison-between-markov-switching-zero
|
A Comparison between Markov Switching Zero-inflated and Hurdle Models for Spatio-temporal Infectious Disease Counts
|
2309.04594
|
none
|
β
Official
|
π In Paper
|
https://github.com/ysfoo/sparsefactor
|
https://paperswithcode.com/paper/a-comparison-of-bayesian-inference-techniques
|
A Comparison of Bayesian Inference Techniques for Sparse Factor Analysis
|
2112.11719
|
none
|
β
Official
|
π In Paper
|
https://github.com/ezrafielding/zoobot-arch-comp
|
https://paperswithcode.com/paper/a-comparison-of-deep-learning-architectures
|
A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
|
2111.04353
|
tf
|
β
Official
|
π On GitHub
|
https://github.com/MIDA-group/Cell-Detection
|
https://paperswithcode.com/paper/a-comparison-of-deep-learning-methods-for-1
|
A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology
|
2504.06957
|
pytorch
|
β
Official
|
π On GitHub
|
https://github.com/Oshana/ESWA
|
https://paperswithcode.com/paper/a-comparison-of-deep-learning-and-established
|
A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring
|
2408.13041
|
tf
|
β
Official
|
β No Mention
|
https://github.com/ishwarvenugopal/GCN-ProcessPrediction
|
https://paperswithcode.com/paper/quartile-based-prediction-of-event-types-and
|
A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes
|
2102.07838
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/bshall/soft-vc
|
https://paperswithcode.com/paper/a-comparison-of-discrete-and-soft-speech
|
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
|
2111.02392
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/dandantang0/missing-data-fiml-rf-and-knn
|
https://paperswithcode.com/paper/a-comparison-of-full-information-maximum
|
A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling
|
2312.17363
|
none
|
β
Official
|
π In Paper
|
https://github.com/bohanwu424/rnhanes
|
https://paperswithcode.com/paper/a-comparison-of-functional-principal
|
A Comparison of Functional Principal Component Analysis Methods with Accelerometry Applications
|
2105.14649
|
none
|
β
Official
|
π On GitHub
|
https://github.com/asriniket/file-format-testing
|
https://paperswithcode.com/paper/a-comparison-of-hdf5-zarr-and-netcdf4-in
|
A Comparison of HDF5, Zarr, and netCDF4 in Performing Common I/O Operations
|
2207.09503
|
none
|
β
Official
|
π In Paper
|
https://github.com/ZhaomingKong/Denoising-Comparison
|
https://paperswithcode.com/paper/a-comparison-of-image-denoising-methods
|
A Comparison of Image Denoising Methods
|
2304.08990
|
none
|
β
Official
|
π In Paper
|
https://github.com/helsinki-nlp/lm-vs-mt
|
https://paperswithcode.com/paper/two-stacks-are-better-than-one-a-comparison
|
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives
|
2407.15489
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/paxnea/genAI-rngt
|
https://paperswithcode.com/paper/a-comparison-of-large-language-model-and
|
A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks
|
2408.09656
|
none
|
β
Official
|
π On GitHub
|
https://github.com/cdrovandi/abc-dist-compare
|
https://paperswithcode.com/paper/a-comparison-of-likelihood-free-methods-with
|
A Comparison of Likelihood-Free Methods With and Without Summary Statistics
|
2103.02407
|
none
|
β
Official
|
π In Paper
|
https://github.com/scotthlee/autism_classification
|
https://paperswithcode.com/paper/a-comparison-of-machine-learning-algorithms
|
A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder
|
1804.06223
|
none
|
β
Official
|
π In Paper
|
https://github.com/fabsig/compare_ml_highcardinality_categorical_variables
|
https://paperswithcode.com/paper/a-comparison-of-machine-learning-methods-for
|
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables
|
2307.02071
|
none
|
β
Official
|
π In Paper
|
https://github.com/ddrous/updec
|
https://paperswithcode.com/paper/a-comparison-of-mesh-free-differentiable
|
A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints
|
2310.02286
|
jax
|
β
Official
|
π In Paper
|
https://github.com/sami-horn/adaptive-experimentation
|
https://paperswithcode.com/paper/a-comparison-of-methods-for-adaptive
|
A Comparison of Methods for Adaptive Experimentation
|
2207.00683
|
none
|
β
Official
|
π In Paper
|
https://github.com/narabzad/genir-evaluation
|
https://paperswithcode.com/paper/a-comparison-of-methods-for-evaluating
|
A Comparison of Methods for Evaluating Generative IR
|
2404.04044
|
none
|
β
Official
|
π In Paper
|
https://github.com/CompNet/nerwip
|
https://paperswithcode.com/paper/a-comparison-of-named-entity-recognition
|
A Comparison of Named Entity Recognition Tools Applied to Biographical Texts
|
1308.0661
|
none
|
β
Official
|
β No Mention
|
https://github.com/ehasler/tensorflow
|
https://paperswithcode.com/paper/a-comparison-of-neural-models-for-word
|
A Comparison of Neural Models for Word Ordering
|
1708.01809
|
tf
|
β
Official
|
π In Paper
|
https://github.com/osst3224/channel_prediction_dnn
|
https://paperswithcode.com/paper/a-comparison-of-neural-networks-for-wireless
|
A Comparison of Neural Networks for Wireless Channel Prediction
|
2308.14020
|
none
|
β
Official
|
π In Paper
|
https://github.com/ERMETE-Lab/pDMD
|
https://paperswithcode.com/paper/a-comparison-of-parametric-dynamic-mode
|
A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications
|
2503.24205
|
none
|
β
Official
|
π On GitHub
|
https://github.com/ais-bonn/prompt_engineering
|
https://paperswithcode.com/paper/a-comparison-of-prompt-engineering-techniques
|
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
|
2410.22997
|
none
|
β
Official
|
π In Paper
|
https://github.com/svakulenk0/cast_evaluation
|
https://paperswithcode.com/paper/a-comparison-of-question-rewriting-methods
|
A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
|
2101.07382
|
none
|
β
Official
|
π In Paper
|
https://github.com/npaulinastevia/drl_se
|
https://paperswithcode.com/paper/a-comparison-of-reinforcement-learning-1
|
A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks
|
2208.12136
|
tf
|
β
Official
|
π In Paper
|
https://github.com/amartyamukherjee/ReinforcementLearningCartPosition
|
https://paperswithcode.com/paper/a-comparison-of-reward-functions-in-q
|
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem
|
2105.11617
|
none
|
β
Official
|
π In Paper
|
https://github.com/arezooSarvi/sigir2020-eComWorkshop-LTM-for-product-search
|
https://paperswithcode.com/paper/a-comparison-of-supervised-learning-to-match
|
A Comparison of Supervised Learning to Match Methods for Product Search
|
2007.10296
|
none
|
β
Official
|
π In Paper
|
https://github.com/vincentwi/Anomaly-Detection
|
https://paperswithcode.com/paper/a-comparison-of-supervised-and-unsupervised
|
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images
|
2107.09204
|
tf
|
β
Official
|
π In Paper
|
https://github.com/Tom-Menzies/Code-Menzies-2020
|
https://paperswithcode.com/paper/a-comparison-of-various-aggregation-functions
|
A Comparison of Various Aggregation Functions in Multi-Criteria Decision Analysis for Drug Benefit-Risk Assessment
|
2107.12298
|
none
|
β
Official
|
π In Paper
|
https://github.com/gwu-bmi/opioids-nlp
|
https://paperswithcode.com/paper/a-comparison-of-veterans-with-problematic
|
A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes
|
2401.12996
|
none
|
β
Official
|
π In Paper
|
https://github.com/jatinchowdhury18/KlonCentaur
|
https://paperswithcode.com/paper/a-comparison-of-virtual-analog-modelling
|
A Comparison of Virtual Analog Modelling Techniques for Desktop and Embedded Implementations
|
2009.02833
|
none
|
β
Official
|
π In Paper
|
https://github.com/casilab/phys_ed_2020
|
https://paperswithcode.com/paper/a-compendium-on-general-relativity-for
|
A Compendium on General Relativity for Undergraduate Students
|
2009.05747
|
none
|
β
Official
|
π In Paper
|
https://github.com/sgoodfriend/rl-algo-impls
|
https://paperswithcode.com/paper/a-competition-winning-deep-reinforcement
|
A Competition Winning Deep Reinforcement Learning Agent in microRTS
|
2402.08112
|
jax
|
β
Official
|
π In Paper
|
https://github.com/muzishen/pet-reid-imag
|
https://paperswithcode.com/paper/a-competitive-method-for-dog-nose-print-re
|
A Competitive Method for Dog Nose-print Re-identification
|
2205.15934
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/gaitholabi/klap-cgo22
|
https://paperswithcode.com/paper/a-compiler-framework-for-optimizing-dynamic
|
A Compiler Framework for Optimizing Dynamic Parallelism on GPUs
|
2201.02789
|
none
|
β
Official
|
β No Mention
|
https://github.com/cucapra/calyx
|
https://paperswithcode.com/paper/a-compiler-infrastructure-for-accelerator
|
A Compiler Infrastructure for Accelerator Generators
|
2102.09713
|
none
|
β
Official
|
π In Paper
|
https://github.com/aalkaid/usdenoising
|
https://paperswithcode.com/paper/a-complementary-global-and-local-knowledge
|
A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained Refinement
|
2310.03402
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/gernst/korn
|
https://paperswithcode.com/paper/a-complete-approach-to-loop-verification-with
|
A Complete Approach to Loop Verification with Invariants and Summaries
|
2010.05812
|
none
|
β
Official
|
π In Paper
|
https://github.com/EnouenJ/mode-attributing-hierarchy
|
https://paperswithcode.com/paper/a-complete-decomposition-of-kl-error-using
|
A Complete Decomposition of KL Error using Refined Information and Mode Interaction Selection
|
2410.11964
|
pytorch
|
β
Official
|
β No Mention
|
https://github.com/hongtao-argmin/cqnpcs_mrireco
|
https://paperswithcode.com/paper/a-complex-quasi-newton-proximal-method-for
|
A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI
|
2303.02586
|
none
|
β
Official
|
π In Paper
|
https://gitlab.com/sepid014/composable-coreset-for-k-center
|
https://paperswithcode.com/paper/a-composable-coreset-for-k-center-in-doubling
|
A Composable Coreset for k-Center in Doubling Metrics
|
1902.01896
|
none
|
β
Official
|
β No Mention
|
https://github.com/keyshor/spectrl_tool
|
https://paperswithcode.com/paper/a-composable-specification-language-for-1
|
A Composable Specification Language for Reinforcement Learning Tasks
|
2008.09293
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/algebraicjulia/algebraicoptimization.jl
|
https://paperswithcode.com/paper/a-compositional-framework-for-first-order
|
A Compositional Framework for First-Order Optimization
|
2403.05711
|
none
|
β
Official
|
π In Paper
|
https://github.com/mcgill-nlp/ud-to-meaning
|
https://paperswithcode.com/paper/a-compositional-typed-semantics-for-universal
|
A Compositional Typed Semantics for Universal Dependencies
|
2403.01187
|
none
|
β
Official
|
π In Paper
|
https://github.com/clyons19/CG-Net
|
https://paperswithcode.com/paper/a-compound-gaussian-network-for-solving
|
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems
|
2305.11120
|
tf
|
β
Official
|
β No Mention
|
https://github.com/ghcollin/cpg_likelihood
|
https://paperswithcode.com/paper/a-compound-poisson-generator-approach-to
|
A Compound Poisson Generator approach to Point-Source Inference in Astrophysics
|
2104.04529
|
none
|
β
Official
|
π In Paper
|
https://github.com/AI4Bharat/adapter-efficiency
|
https://paperswithcode.com/paper/a-comprehensive-analysis-of-adapter
|
A Comprehensive Analysis of Adapter Efficiency
|
2305.07491
|
pytorch
|
β
Official
|
π On GitHub
|
https://github.com/turkish-word-embeddings/word-embeddings-repository-for-turkish
|
https://paperswithcode.com/paper/a-comprehensive-analysis-of-static-word
|
A Comprehensive Analysis of Static Word Embeddings for Turkish
|
2405.07778
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/lyndonchan/wsss-analysis
|
https://paperswithcode.com/paper/a-comprehensive-analysis-of-weakly-supervised
|
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
|
1912.11186
|
tf
|
β
Official
|
π In Paper
|
https://github.com/e0397123/comp-analysis
|
https://paperswithcode.com/paper/a-comprehensive-analysis-of-the-effectiveness
|
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
|
2312.15407
|
none
|
β
Official
|
π In Paper
|
https://github.com/mlcaepiee/simuav
|
https://paperswithcode.com/paper/a-comprehensive-approach-for-uav-small-object
|
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion
|
2109.01800
|
none
|
β
Official
|
π In Paper
|
https://github.com/super-AND/super-AND
|
https://paperswithcode.com/paper/a-comprehensive-approach-to-unsupervised
|
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
|
2002.12158
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/exe1023/DialEvalMetrics
|
https://paperswithcode.com/paper/a-comprehensive-assessment-of-dialog
|
A Comprehensive Assessment of Dialog Evaluation Metrics
|
2106.03706
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/yili-hong/elimageclassifictionmlcomparison
|
https://paperswithcode.com/paper/a-comprehensive-case-study-on-the-performance
|
A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images
|
2408.06229
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/sm3a96/ids-machine-learning-techniques-
|
https://paperswithcode.com/paper/a-comprehensive-comparative-study-of
|
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems
|
2410.15597
|
none
|
β
Official
|
π In Paper
|
https://github.com/nahshonmokua/LoRaWAN-Indoor-PathLoss-Dataset-IEEEACCESS
|
https://paperswithcode.com/paper/a-comprehensive-data-description-for-lorawan
|
A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
|
2505.06375
|
none
|
β
Official
|
β No Mention
|
https://github.com/thu-cs-pi-lab/anfc-automated-nailfold-capillary
|
https://paperswithcode.com/paper/a-comprehensive-dataset-and-automated
|
A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
|
2312.05930
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/ncbi-nlp/CovidTermVar
|
https://paperswithcode.com/paper/a-comprehensive-dictionary-and-term-variation
|
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
|
2010.14588
|
none
|
β
Official
|
π In Paper
|
https://github.com/albinsou/ocl_survey
|
https://paperswithcode.com/paper/a-comprehensive-empirical-evaluation-on
|
A Comprehensive Empirical Evaluation on Online Continual Learning
|
2308.10328
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/yisongy/failureclustering
|
https://paperswithcode.com/paper/a-comprehensive-empirical-investigation-on
|
A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging
|
2207.07992
|
none
|
β
Official
|
π In Paper
|
https://github.com/zhixiongz/clip4cmr
|
https://paperswithcode.com/paper/a-comprehensive-empirical-study-of-vision
|
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval
|
2201.02772
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/Lattle-y/AI-recognition-for-lq-ed
|
https://paperswithcode.com/paper/a-comprehensive-end-to-end-computer-vision
|
A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings
|
2312.13620
|
none
|
β
Official
|
β No Mention
|
https://github.com/hanada31/icc-resolution-evaluation
|
https://paperswithcode.com/paper/towards-practical-evaluation-of-android-icc
|
A Comprehensive Evaluation of Android ICC Resolution Techniques
|
2111.05649
|
none
|
β
Official
|
π In Paper
|
https://github.com/simonmalberg/cognitive-biases-in-llms
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-cognitive
|
A Comprehensive Evaluation of Cognitive Biases in LLMs
|
2410.15413
|
none
|
β
Official
|
π In Paper
|
https://github.com/liihwf/testing-end-to-end-ai-autopilots
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-four-end-to-end
|
A Comprehensive Evaluation of Four End-to-End AI Autopilots Using CCTest and the Carla Leaderboard
|
2501.12090
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/scjjb/ovarian_features
|
https://paperswithcode.com/paper/histopathology-foundation-models-enable
|
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
|
2405.09990
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/tahmedge/llm-eval-biomed
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-large-language
|
A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks
|
2310.04270
|
none
|
β
Official
|
π In Paper
|
https://github.com/srhthu/lm-compeval-legal
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-large-language-1
|
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction
|
2310.11761
|
none
|
β
Official
|
π In Paper
|
https://github.com/mmcdermott/comprehensive_MTL_EHR
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-multi-task
|
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
|
2007.10185
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/mabelqi/peft4csd
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-parameter-2
|
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
|
2412.13801
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/zwtnju/peft
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-parameter
|
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
|
2312.15614
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/cordercorder/quant_eval
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-of-quantization
|
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
|
2402.16775
|
pytorch
|
β
Official
|
β No Mention
|
https://github.com/tzwwww/ev2
|
https://paperswithcode.com/paper/a-comprehensive-evaluation-on-event-reasoning
|
A Comprehensive Evaluation on Event Reasoning of Large Language Models
|
2404.17513
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/fcgrolleau/itreval
|
https://paperswithcode.com/paper/a-comprehensive-framework-for-the-evaluation
|
A Comprehensive Framework for the Evaluation of Individual Treatment Rules From Observational Data
|
2207.06275
|
none
|
β
Official
|
π In Paper
|
https://github.com/goose315/graph_pooling_benchmark
|
https://paperswithcode.com/paper/a-comprehensive-graph-pooling-benchmark
|
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
|
2406.09031
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs
|
https://paperswithcode.com/paper/a-comprehensive-guide-to-explainable-ai-from
|
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
|
2412.00800
|
tf
|
β
Official
|
π In Paper
|
https://github.com/anik801/bdl_data_1
|
https://paperswithcode.com/paper/a-comprehensive-indoor-environment-dataset
|
A Comprehensive Indoor Environment Dataset from Single-family Houses in the US
|
2310.03771
|
none
|
β
Official
|
π In Paper
|
https://github.com/trystanscottlambert/pyfof
|
https://paperswithcode.com/paper/a-comprehensive-investigation-of
|
A Comprehensive Investigation of Environmental Influences on Galaxies in Group Environments
|
2410.15548
|
none
|
β
Official
|
π In Paper
|
https://github.com/yzhenggit/zheng_dwarfcgm_survey
|
https://paperswithcode.com/paper/a-comprehensive-investigation-of-metals-in
|
A Comprehensive Investigation of Metals in the Circumgalactic Medium of Nearby Dwarf Galaxies
|
2301.12233
|
none
|
β
Official
|
π In Paper
|
https://github.com/louisbearing/hmo-audio
|
https://paperswithcode.com/paper/a-comprehensive-multi-scale-approach-for
|
A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation
|
2307.03270
|
pytorch
|
β
Official
|
π In Paper
|
https://github.com/humza909/llm_survey
|
https://paperswithcode.com/paper/a-comprehensive-overview-of-large-language
|
A Comprehensive Overview of Large Language Models
|
2307.06435
|
none
|
β
Official
|
π In Paper
|
https://github.com/grigorisg9gr/deformable_tracking_review_ijcv2016
|
https://paperswithcode.com/paper/a-comprehensive-performance-evaluation-of
|
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
|
1603.06015
|
tf
|
β
Official
|
β No Mention
|
https://github.com/menouarazib/informationretrievalinnlp
|
https://paperswithcode.com/paper/a-comprehensive-python-library-for-deep
|
A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP
|
2310.16485
|
none
|
β
Official
|
π In Paper
|
https://github.com/sonyresearch/raw_bench
|
https://paperswithcode.com/paper/a-comprehensive-real-world-assessment-of
|
A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?
|
2505.19663
|
tf
|
β
Official
|
π In Paper
|
Subsets and Splits
Unique ArXiv IDs in Train Data
Identifies and retrieves records of papers that appear only once in the dataset, helping to understand unique entries.