https://github.com/ink-usc/predicting-big-bench
공식 코드How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
Papers with Code 아카이브
Diversity
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Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ink-usc/rationalemultirewarddistillation
공식 코드https://github.com/inouye-lab/feddg_benchmark
공식 코드https://github.com/inouye-lab/ncm
공식 코드https://github.com/inria-thoth/t3sc
공식 코드A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
https://github.com/instadeepai/qd-skill-discovery-benchmark
공식 코드Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery
https://github.com/intellabs/matsciml
공식 코드MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
https://github.com/intellabs/neurocounterfactuals
공식 코드NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation
https://github.com/intelligent-control-lab/guard
공식 코드https://github.com/internlm/alchemistcoder
공식 코드AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
https://github.com/internlm/internlm-law
공식 코드InternLM-Law: An Open Source Chinese Legal Large Language Model
https://github.com/iranroman/spatialscaper
공식 코드https://github.com/iremeyiokur/COVID-19-Preventions-Control-System
공식 코드https://github.com/irislucent/motif-encoder
공식 코드https://github.com/irmvlab/difflow3d
공식 코드DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Model
https://github.com/ironbeliever/car
공식 코드https://github.com/isabella-gomes/globecom2023
공식 코드Multi-Static ISAC in Cell-Free Massive MIMO: Precoder Design and Privacy Assessment
https://github.com/isadrtdinov/ens-for-transfer
공식 코드To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
https://github.com/ishaanb92/generalizedprobabilisticunet
공식 코드Generalized Probabilistic U-Net for medical image segementation
https://github.com/isitnavajo/NavajoDetector
공식 코드Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages
https://github.com/islab-sophia/odigen-mlpmixer
공식 코드https://github.com/isvy08/l2c
공식 코드Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
https://github.com/itayeilat/performative-recommendation
공식 코드Performative Recommendation: Diversifying Content via Strategic Incentives
https://github.com/ivalab/kgn
공식 코드Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input
https://github.com/ivan-chai/hotpp-benchmark
공식 코드DeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction
https://github.com/ivcylc/openmusic
공식 코드Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
https://github.com/ivcylc/qa-mdt
공식 코드Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
https://github.com/ivrl/augsal
공식 코드Data Augmentation via Latent Diffusion for Saliency Prediction
https://github.com/j-f-cheng/score-pa_score-based-3d-part-assembly
공식 코드https://github.com/jCarrilloDeAlbornoz/RBU
공식 코드An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric
https://github.com/jackory/rpbt
공식 코드Learning Diverse Risk Preferences in Population-based Self-play
https://github.com/jackyue1994/FreEformer
공식 코드FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting
https://github.com/jacobzhaoziyuan/SAAL
공식 코드Self-supervised Assisted Active Learning for Skin Lesion Segmentation
https://github.com/jakob-schloer/latentgmm
공식 코드https://github.com/jakobbossek/GECCO2021-knapsack-diversity
공식 코드https://github.com/jakpra/l2-prepositions
공식 코드Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.