https://github.com/prijatelj/bayesian_eval_ground_truth-free
공식 코드A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
Papers with Code 아카이브
Open-Ended Question Answering
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Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/psclklnk/spdl
공식 코드https://github.com/qiyuw/peercl
공식 코드PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
https://github.com/reamdc1/gene_block_evolution
공식 코드An Event-Driven Approach for Studying Gene Block Evolution in Bacteria
https://github.com/robin-karlsson0/predictive-world-models
공식 코드Predictive World Models from Real-World Partial Observations
https://github.com/rochesterxugroup/DSGPM
공식 코드Graph Neural Network Based Coarse-Grained Mapping Prediction
https://github.com/rochesterxugroup/HAM_dataset
공식 코드Graph Neural Network Based Coarse-Grained Mapping Prediction
https://github.com/rpartsey/pointgoal-navigation
공식 코드https://github.com/ruoxi-jia-group/2dshapley
공식 코드https://github.com/sarthmit/BRIMs
공식 코드https://github.com/sarwanpasha/pwm2vec
공식 코드https://github.com/sen-uni-kn/specrepair
공식 코드A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
https://github.com/shaily99/eecc
공식 코드Extrinsic Evaluation of Cultural Competence in Large Language Models
https://github.com/sijingtu/kdd-23-adversaries-with-limited-information
공식 코드Adversaries with Limited Information in the Friedkin--Johnsen Model
https://github.com/sjoshi804/sas-data-efficient-contrastive-learning
공식 코드https://github.com/skokec/detectron-traffic-signs
공식 코드Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
https://github.com/snap-stanford/greaselm
공식 코드GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
https://github.com/snap-stanford/pretrain-gnns/
공식 코드https://github.com/songtaoliu0823/lagnn
공식 코드https://github.com/spandan-madan/in_distribution_adversarial_examples
공식 코드In-distribution adversarial attacks on object recognition models using gradient-free search
https://github.com/speechbrain/speechbrain/tree/develop/recipes/REAL-M/sisnr-estimation
공식 코드https://github.com/stephanlorenzen/majorityvotebounds
공식 코드Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
https://github.com/stwisdom/urnn
공식 코드https://github.com/suvodeep90/semi_supervised_methods
공식 코드When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
https://github.com/swj0419/knn_prompt
공식 코드https://github.com/szu-tera/rankedvotingsc
공식 코드Ranked Voting based Self-Consistency of Large Language Models
https://github.com/taejoo-ahn/fdr_bayes_figures
공식 코드Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
https://github.com/tpfi22/spectral-and-combinatorial
공식 코드Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
https://github.com/transmuteai/data-efficient-transformers
공식 코드Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
https://github.com/twytock/Distinguishing_Cell_Types
공식 코드https://github.com/uma-pi1/OPIEC
공식 코드https://github.com/utahnlp/layer_augmentation
공식 코드https://github.com/uuujf/IterAvg
공식 코드Obtaining Adjustable Regularization for Free via Iterate Averaging
https://github.com/valvoda/neuraltransducer
공식 코드https://github.com/vik0/LangevinDQN
공식 코드https://github.com/vis-nlp/opencqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.