https://github.com/pku-icst-mipl/posterlayout-cvpr2023
공식 코드PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout
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/pkunlp-icler/ultraedit
공식 코드UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
https://github.com/pliumath/dependencies
공식 코드Quantifying syntax similarity with a polynomial representation of dependency trees
https://github.com/pluslabnlp/flashback_gen
공식 코드Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts
https://github.com/pluslabnlp/narrative-discourse
공식 코드Are Large Language Models Capable of Generating Human-Level Narratives?
https://github.com/plusmultiply/taps3d
공식 코드TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervision
https://github.com/pmh9960/regft-for-gen
공식 코드Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models
https://github.com/pokaxpoka/sunrise
공식 코드SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
https://github.com/poloclub/diffusiondb
공식 코드DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
https://github.com/poloclub/people-map
공식 코드PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
https://github.com/polyagent/from-bytes-to-borsch
공식 코드From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation
https://github.com/poolio/unrolled_gan
공식 코드https://github.com/poruna-byte/gumbelsoft
공식 코드GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick
https://github.com/ppartha03/Semantic-Loss-Dialogue-Generation
공식 코드A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
https://github.com/pprp/pruner-zero
공식 코드Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
https://github.com/ppsantos/rl-data-distribution-public
공식 코드The Impact of Data Distribution on Q-learning with Function Approximation
https://github.com/pqy000/PMT-IQA
공식 코드PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment
https://github.com/prakharguptaz/multirefeval
공식 코드Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
https://github.com/prateekmunjal/torchal
공식 코드Towards Robust and Reproducible Active Learning Using Neural Networks
https://github.com/praveensonu/Multi-perspective-Stance-Detection
공식 코드https://github.com/princetonvisualai/HIVE
공식 코드HIVE: Evaluating the Human Interpretability of Visual Explanations
https://github.com/princetonvisualai/SPICE-U
공식 코드https://github.com/principledhiring/SWAP
공식 코드https://github.com/priyamtejaswin/howwelldoyouknow
공식 코드https://github.com/probcomp/pclean
공식 코드PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
https://github.com/probe2/kg_text
공식 코드Evaluating and Improving Graph to Text Generation with Large Language Models
https://github.com/proroklab/controllingbehavioraldiversity
공식 코드Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning
https://github.com/proroklab/hetgppo
공식 코드System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning
https://github.com/proteus1991/GridDehazeNet
공식 코드GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
https://github.com/ptrckhmmr/human-ai-teams
공식 코드https://github.com/ptschandl/HAM10000_dataset
공식 코드https://github.com/pubgeo/cmmh_segmentation
공식 코드Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
https://github.com/publisher-promptcd/promptcd
공식 코드Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis
https://github.com/qbouniot/sim_kernel_mixup
공식 코드https://github.com/qiang-ma/graph-pointer-network
공식 코드https://github.com/qianghuangwhu/benchtemp
공식 코드BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.