https://github.com/codedotal/gpt-code-clippy
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
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/codeguardplus/codeguardplus
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
https://github.com/codelion/optillm/blob/main/optillm/plansearch.py
Planning In Natural Language Improves LLM Search For Code Generation
https://github.com/coderpiaobozhe/classifier-free-diffusion-guidance-Pytorch
https://github.com/cogsys-tuebingen/uninas
https://github.com/colorfulscoop/tfdlg
https://github.com/commissarsilver/cvt
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
https://github.com/conan7882/tf-gans
https://github.com/conceptofmind/LaMDA-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/consequencesunintended/BEGAN
https://github.com/cool21th/ai_drug_discovery
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
https://github.com/craftjarvis/mc-planner
https://github.com/crockwell/cap3d
https://github.com/crosszamirski/guided-i2i
https://github.com/crowsonkb/guided-diffusion
https://github.com/cs-giung/course-dl-TP
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
https://github.com/cshaib/diversity
Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores
https://github.com/cshizhe/asg2cap
Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs
https://github.com/ctgatecci/Clinical-trial-eligibility-criteria-NER
Information Extraction of Clinical Trial Eligibility Criteria
https://github.com/ctrnh/LinearContextualBandits
https://github.com/ctuning/ck-tvm
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
https://github.com/cycloneboy/pdf_table
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
https://github.com/cynricfu/MAGNN
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
https://github.com/dacostaHugo/AC-GANs
https://github.com/daerduoCarey/o2oafford
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
https://github.com/damo-nlp-sg/seallms
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
https://github.com/dannigt/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/dashayushman/TAC-GAN
https://github.com/david-stojanovski/echo_from_noise
https://github.com/davidismael/BEGAN
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dazhangyu123/acmil
https://github.com/dcaled/mind
https://github.com/dcaled/mint
https://github.com/decemberzhou/TF_TAS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.