Retrieval
3928 papers with code • 4 benchmarks • 25 datasets
Libraries
Use these libraries to find Retrieval models and implementations
OK-VQA
Polyvore
PopQA
ARO
ALCE
InfoSeek
QAMPARI
xCodeEval
MVK
DIOR-RSVG
Most implemented papers
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
facebookresearch/LASER
•
•
ACL 2019
Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
huggingface/transformers
•
•
NeurIPS 2020
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Declarative Experimentation in Information Retrieval using PyTerrier
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures.
Linear Transformers Are Secretly Fast Weight Programmers
ischlag/fast-weight-transformers
•
•
We show the formal equivalence of linearised self-attention mechanisms and fast weight controllers from the early '90s, where a ``slow" neural net learns by gradient descent to program the ``fast weights" of another net through sequences of elementary programming instructions which are additive outer products of self-invented activation patterns (today called keys and values).
Deep Learning based Recommender System: A Survey and New Perspectives
This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
Atomu2014/product-nets-distributed
•
•
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
HUSE: Hierarchical Universal Semantic Embeddings
The works in the domain of visual semantic embeddings address this problem by first constructing a semantic embedding space based on some external knowledge and projecting image embeddings onto this fixed semantic embedding space.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
stanford-futuredata/ColBERT
•
•
ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
garythung/torch-lrcn
•
•
CVPR 2015
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.
Deep Neural Networks for YouTube Recommendations
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence.
