Teacher train files (ids only), based on the MSMARCO-Passage collection, for the paper: Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan and Allan Hanbury (https://arxiv.org/abs/2010.02666) for the documentation please see: https://github.com/sebastian-hofstaetter/neural-ranking-k
Schenck W. Ranking Methods for Neural Gas and NGPCA. Bielefeld: Computer Engineering Group, Faculty ...
Published ArticleThe human brain has about 100 billion neurons. These neural networks can be simulat...
Perhaps the applied nature of information retrieval research goes some way to explain the community'...
Large pretrained language models have achieved state-of-the-art results on a variety of downstream t...
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in m...
One of the main problems in the field of Artificial Intelligence is the efficiency of neural network...
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, bu...
Palm G, Goser K, Rückert U, Ultsch A. Knowledge Processing in Neural Architecture. In: Delgado-Frias...
We present a novel framework of knowledge distillation that is capable of learning powerful and effi...
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine...
Building a Neural Language Model from scratch involves a big number of different design decisions. Y...
Knowledge distillation is considered as a training and compression strategy in which two neural netw...
Can we perform Neural Architecture Search (NAS) with a smaller subset of target dataset and still fa...
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petř...
In recent years, deep neural networks have been successful in both industry and academia, especially...
Schenck W. Ranking Methods for Neural Gas and NGPCA. Bielefeld: Computer Engineering Group, Faculty ...
Published ArticleThe human brain has about 100 billion neurons. These neural networks can be simulat...
Perhaps the applied nature of information retrieval research goes some way to explain the community'...
Large pretrained language models have achieved state-of-the-art results on a variety of downstream t...
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in m...
One of the main problems in the field of Artificial Intelligence is the efficiency of neural network...
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, bu...
Palm G, Goser K, Rückert U, Ultsch A. Knowledge Processing in Neural Architecture. In: Delgado-Frias...
We present a novel framework of knowledge distillation that is capable of learning powerful and effi...
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine...
Building a Neural Language Model from scratch involves a big number of different design decisions. Y...
Knowledge distillation is considered as a training and compression strategy in which two neural netw...
Can we perform Neural Architecture Search (NAS) with a smaller subset of target dataset and still fa...
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petř...
In recent years, deep neural networks have been successful in both industry and academia, especially...
Schenck W. Ranking Methods for Neural Gas and NGPCA. Bielefeld: Computer Engineering Group, Faculty ...
Published ArticleThe human brain has about 100 billion neurons. These neural networks can be simulat...
Perhaps the applied nature of information retrieval research goes some way to explain the community'...