Papers with Code 아카이브
Retrieval
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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/tanveer81/revisionllm
RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
https://github.com/tanyakoh/CBIR
https://github.com/taroogura/Person_reID_baseline_pytorch
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/tatsu-lab/linguistic_calibration
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/technicolor-research/sodeep
https://github.com/tencentarc/opencompatible
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
https://github.com/tengdahan/temporalalignnet
https://github.com/tensorflow/models/tree/master/research/deeplab
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
https://github.com/tensorflow/models/tree/master/research/delf
Deep Image Retrieval: Learning global representations for image search
https://github.com/tensorflow/models/tree/master/research/global_objectives
https://github.com/tensorflow/neural-structured-learning
https://github.com/tensorflow/tensor2tensor
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
https://github.com/terrierteam/pyterrier_ance
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
https://github.com/terrierteam/pyterrier_deepct
Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval
https://github.com/terrierteam/pyterrier_dr
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
https://github.com/terrierteam/pyterrier_t5
Declarative Experimentation in Information Retrieval using PyTerrier
https://github.com/texsmv/point_cloud_reconstruction
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
https://github.com/texttron/tevatron/tree/main/examples/repllama
https://github.com/textvec/textvec
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
https://github.com/tginart/sglang
SGLang: Efficient Execution of Structured Language Model Programs
https://github.com/thadikari/differentiable-boundary-trees
Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees
https://github.com/thakur-nandan/beir-colbert
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/the-pythoncoder/councelChatbot2
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/the-pythoncoder/counsel-chat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/theamaya/3dlatnav
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
https://github.com/thinklab-sjtu/linsatnet
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
https://github.com/thirdairesearch/bolt_benchmarks
https://github.com/thiviyanT/torch-rgcn
https://github.com/thompsonb/prism_bitext_filter
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/thswodnjs3/CSTA
Video Joint Modelling Based on Hierarchical Transformer for Co-summarization
https://github.com/thu-coai/CDial-GPT
https://github.com/thu-coai/OpenMEVA
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
https://github.com/thu-coai/ecm
https://github.com/thu-keg/korc
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/thulab/DeepHash
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.