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06/13/2023
Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
BEIR is a benchmark dataset for zero-shot evaluation of information retr...
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06/05/2023
Benchmarking Middle-Trained Language Models for Neural Search
Middle training methods aim to bridge the gap between the Masked Languag...
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04/25/2023
The tale of two MS MARCO – and their unfair comparisons
The MS MARCO-passage dataset has been the main large-scale dataset open ...
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04/25/2023
A Static Pruning Study on Sparse Neural Retrievers
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have b...
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04/04/2023
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
This paper presents the AToMiC (Authoring Tools for Multimedia Content) ...
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04/03/2023
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
The advent of multilingual language models has generated a resurgence of...
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03/23/2023
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
Parameter-Efficient transfer learning with Adapters have been studied in...
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03/10/2023
Naver Labs Europe (SPLADE) @ TREC NeuCLIR 2022
This paper describes our participation in the 2022 TREC NeuCLIR challeng...
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02/28/2023
Extending English IR methods to multi-lingual IR
This paper describes our participation in the 2023 WSDM CUP - MIRACL cha...
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02/24/2023
Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022
This paper describes our participation to the 2022 TREC Deep Learning ch...
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01/25/2023
An Experimental Study on Pretraining Transformers from Scratch for IR
Finetuning Pretrained Language Models (PLM) for IR has been de facto the...
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07/08/2022
An Efficiency Study for SPLADE Models
Latency and efficiency issues are often overlooked when evaluating IR mo...
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05/10/2022
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Neural retrievers based on dense representations combined with Approxima...
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04/14/2022
Composite Code Sparse Autoencoders for first stage retrieval
We propose a Composite Code Sparse Autoencoder (CCSA) approach for Appro...
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12/13/2021
A Study on Token Pruning for ColBERT
The ColBERT model has recently been proposed as an effective BERT based ...
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10/18/2021
TLDR: Twin Learning for Dimensionality Reduction
Dimensionality reduction methods are unsupervised approaches which learn...
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10/08/2021
Graphs as Tools to Improve Deep Learning Methods
In recent years, deep neural networks (DNNs) have known an important ris...
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09/21/2021
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
In neural Information Retrieval (IR), ongoing research is directed towar...
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01/12/2021
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in t...
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12/14/2020
Graphs for deep learning representations
In recent years, Deep Learning methods have achieved state of the art pe...
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12/02/2020
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Learning deep representations to solve complex machine learning tasks ha...
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11/25/2020
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Measuring the generalization performance of a Deep Neural Network (DNN) ...
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11/14/2020
Representing Deep Neural Networks Latent Space Geometries with Graphs
Deep Learning (DL) has attracted a lot of attention for its ability to r...
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07/16/2020
Graph topology inference benchmarks for machine learning
Graphs are nowadays ubiquitous in the fields of signal processing and ma...
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11/08/2019
Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
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11/07/2019
Improved Visual Localization via Graph Smoothing
Vision based localization is the problem of inferring the pose of the ca...
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09/11/2019
Structural Robustness for Deep Learning Architectures
Deep Networks have been shown to provide state-of-the-art performance in...
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08/19/2019
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Predicting the future of Graph-supported Time Series (GTS) is a key chal...
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05/29/2019
Attention Based Pruning for Shift Networks
In many application domains such as computer vision, Convolutional Layer...
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05/01/2019
A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals d...
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05/01/2019
