https://github.com/cstankonrad/long_llama
Landmark Attention: Random-Access Infinite Context Length for Transformers
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 11
- 보존된 평가 기록
- 19
A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cvg/Hierarchical-Localization
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/cvlab-columbia/globetrotter
https://github.com/cwida/pdx
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval
https://github.com/cwitkowitz/guitar-transcription-continuous
https://github.com/cwitkowitz/lhvqt
https://github.com/cxa-unique/Simplified-TinyBERT
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/cybermetric/cybermetric
https://github.com/d223302/merging-facts-crafting-fallacies
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/daltonj/treccastweb
TREC CAsT 2019: The Conversational Assistance Track Overview
https://github.com/danieljf24/hybrid_space
https://github.com/daod/coca
https://github.com/daod/dcl
https://github.com/darthbatman/simplified-ECON
https://github.com/datastax/jvector
Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
https://github.com/davidalami/convert
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/davidpaulius/foon_api
Functional Object-Oriented Network: Construction & Expansion
https://github.com/dddzeng/CMR_audiovisual
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
https://github.com/deepakks1995/DeepHashing
https://github.com/deepampatel/TwinBert
TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval
https://github.com/deeplearning-wisc/hypo
https://github.com/deepmind/narrativeqa
https://github.com/deepmind/open_spiel
Fast deep reinforcement learning using online adjustments from the past
https://github.com/deepset-ai/haystack
https://github.com/deperiaskerre/qkg
Retrieval-Augmented Generation for Large Language Models: A Survey
https://github.com/dglai/wsdm2022-challenge
https://github.com/dhesenkamp/attentive-lstm
https://github.com/dholzmueller/pytabkit
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
https://github.com/dibschat/tempAgg
https://github.com/dikshantsagar/Triplet-GAN
https://github.com/dladustn95/ENMODEL
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/dladustn95/dialogGenerator_transformer
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/dladustn95/enLanguageModel
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/dlsun/ds-seminar
Deep Learning based Recommender System: A Survey and New Perspectives
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/DDAG_mindspore
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.