https://github.com/mcgill-nlp/incontext-code-generation
A Corpus for Reasoning About Natural Language Grounded in Photographs
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/mcgill-nlp/llm2vec
https://github.com/mcordier/Papers-Presentation-ML-Reading-Group-
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
https://github.com/mczhuge/Kaleido-BERT
https://github.com/mczhuge/SOCToolbox
https://github.com/mdziardziel/colorize-pictures
https://github.com/mediabrain-sjtu/groupnet
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
https://github.com/mediabrain-sjtu/medicalgpt-zh
https://github.com/mega002/annotator_bias
https://github.com/megagonlabs/llm-longeval
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/megagonlabs/rotom
https://github.com/mehrdadyo/Coherent-Language-Generator
Improving Language Generation with Sentence Coherence Objective
https://github.com/memray/llm_phrase_semantics
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
https://github.com/meszarosanna/rule_extrapolation
https://github.com/metalbubble/moments_models
Moments in Time Dataset: one million videos for event understanding
https://github.com/mevius6/smallgan
Image Generation From Small Datasets via Batch Statistics Adaptation
https://github.com/mgaido91/FBK-fairseq-ST
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/microsoft/AutoML
MiniViT: Compressing Vision Transformers with Weight Multiplexing
https://github.com/microsoft/activemlp
https://github.com/microsoft/guidance
Automatic Chain of Thought Prompting in Large Language Models
https://github.com/microsoft/kid
https://github.com/microsoft/mofdiff
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
https://github.com/microsoft/promptwizard
https://github.com/mindslab-ai/hififace
https://github.com/mindspore-ai/contrib/tree/master/intern/VAE-Creative-Discovery-using-QD-Search
https://github.com/mindspore-ai/contrib/tree/master/papers/UColor
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/mindspore-ai/models/tree/master/research/cv/AVA_cifar
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent Loss
https://github.com/mindspore-ai/models/tree/master/research/cv/augvit
https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
Diversity is All You Need: Learning Skills without a Reward Function
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/EMSA.py
https://github.com/minerllabs/minerl
https://github.com/ming053l/e2d
Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection
https://github.com/mingliiii/ruler
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning
https://github.com/mistralai/mistral-src
https://github.com/mks0601/ExAvatar_RELEASE
https://github.com/ml-research/i2p
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.