Disentangling Locality and Entropy in Ranking Distillation | Paper Citation Record

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This paper cites Attention Is All You Need.

Disentangling Locality and Entropy in Ranking Distillation Attention Is All You Need

Reference 1

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This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

Disentangling Locality and Entropy in Ranking Distillation BERT: pre-training of deep bidirectional transformers for language understanding

Reference 2

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This paper cites Pretrained Transformers for Text Ranking: BERT and Beyond.

Disentangling Locality and Entropy in Ranking Distillation Pretrained Transformers for Text Ranking: BERT and Beyond

Reference 3

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This paper cites MS MARCO: A human generated machine reading comprehension dataset.

Disentangling Locality and Entropy in Ranking Distillation MS MARCO: A human generated machine reading comprehension dataset

Reference 4

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This paper cites Passage Re-ranking with BERT.

Disentangling Locality and Entropy in Ranking Distillation Passage Re-ranking with BERT

Reference 5

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This paper cites Dense passage retrieval for open-domain question answering.

Disentangling Locality and Entropy in Ranking Distillation Dense passage retrieval for open-domain question answering

Reference 6

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This paper cites Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering.

Disentangling Locality and Entropy in Ranking Distillation Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering

Reference 7

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This paper cites Distilling the Knowledge in a Neural Network.

Disentangling Locality and Entropy in Ranking Distillation Distilling the Knowledge in a Neural Network

Reference 8

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This paper cites Distilling Dense Representations for Ranking using Tightly-Coupled Teachers.

Disentangling Locality and Entropy in Ranking Distillation Distilling Dense Representations for Ranking using Tightly-Coupled Teachers

Reference 9

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This paper cites Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation.

Disentangling Locality and Entropy in Ranking Distillation Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

Reference 10

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Disentangling Locality and Entropy in Ranking Distillation Generalization bounds via distillation

Reference 11

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This paper cites Retromae: Pre-training retrieval-oriented language models via masked auto-encoder.

Disentangling Locality and Entropy in Ranking Distillation Retromae: Pre-training retrieval-oriented language models via masked auto-encoder

Reference 12

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This paper cites Rocketqav2: A joint training method for dense passage retrieval and passage re-ranking.

Disentangling Locality and Entropy in Ranking Distillation Rocketqav2: A joint training method for dense passage retrieval and passage re-ranking

Reference 13

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This paper cites Doermann, and Junsong Yuan.

Disentangling Locality and Entropy in Ranking Distillation Doermann, and Junsong Yuan

Reference 14

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This paper cites FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge.

Disentangling Locality and Entropy in Ranking Distillation FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge

Reference 15

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This paper cites Rethink training of BERT rerankers in multi-stage retrieval pipeline.

Disentangling Locality and Entropy in Ranking Distillation Rethink training of BERT rerankers in multi-stage retrieval pipeline

Reference 16

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Disentangling Locality and Entropy in Ranking Distillation Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 17

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Disentangling Locality and Entropy in Ranking Distillation Unresolved cited work

Reference 18

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This paper cites Model compression.

Disentangling Locality and Entropy in Ranking Distillation Model compression

Reference 19

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This paper cites Do deep nets really need to be deep? In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D.

Disentangling Locality and Entropy in Ranking Distillation Do deep nets really need to be deep? In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D

Reference 20

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This paper cites Squeezing water from a stone: A bag of tricks for further improving cross-encoder effectiveness for reranking.

Disentangling Locality and Entropy in Ranking Distillation Squeezing water from a stone: A bag of tricks for further improving cross-encoder effectiveness for reranking

Reference 21

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This paper cites RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!.

Disentangling Locality and Entropy in Ranking Distillation RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

Reference 22

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Disentangling Locality and Entropy in Ranking Distillation Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 23

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This paper cites An inspection of the reproducibility and replicability of tct-colbert.

Disentangling Locality and Entropy in Ranking Distillation An inspection of the reproducibility and replicability of tct-colbert

Reference 25

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Disentangling Locality and Entropy in Ranking Distillation Bennett, Junaid Ahmed, and Arnold Overwijk

Reference 26

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Disentangling Locality and Entropy in Ranking Distillation Optimizing dense retrieval model training with hard negatives

Reference 27

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Disentangling Locality and Entropy in Ranking Distillation Christiano, Jan Leike, Tom B

Reference 28

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Disentangling Locality and Entropy in Ranking Distillation Manning, Stefano Ermon, and Chelsea Finn

Reference 29

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This paper cites Annotating data for fine-tuning a neural ranker? current active learning strategies are not better than random selection.

Disentangling Locality and Entropy in Ranking Distillation Annotating data for fine-tuning a neural ranker? current active learning strategies are not better than random selection

Reference 30

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Disentangling Locality and Entropy in Ranking Distillation Bruce Croft

Reference 31

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Disentangling Locality and Entropy in Ranking Distillation Rank-distillm: Closing the effectiveness gap between cross-encoders and llms for passage re-ranking

Reference 32

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Disentangling Locality and Entropy in Ranking Distillation Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

Reference 33

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Disentangling Locality and Entropy in Ranking Distillation Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents

Reference 34

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Disentangling Locality and Entropy in Ranking Distillation Knowledge distillation in wide neural networks: Risk bound, data efficiency and imperfect teacher

Reference 35

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Disentangling Locality and Entropy in Ranking Distillation Zico Kolter

Reference 36

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Disentangling Locality and Entropy in Ranking Distillation A Generalization Theory of Cross-Modality Distillation with Contrastive Learning

Reference 37

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Disentangling Locality and Entropy in Ranking Distillation Robertson, Steve Walker, Micheline Hancock - Beaulieu, Mike Gatford, and A

Reference 38

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Disentangling Locality and Entropy in Ranking Distillation Document ranking with a pretrained sequence-to-sequence model

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Disentangling Locality and Entropy in Ranking Distillation Voorhees

Reference 40

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Disentangling Locality and Entropy in Ranking Distillation Variations in Relevance Judgments and the Shelf Life of Test Collections

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Disentangling Locality and Entropy in Ranking Distillation Unresolved cited work

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Disentangling Locality and Entropy in Ranking Distillation Unresolved cited work

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Disentangling Locality and Entropy in Ranking Distillation Overview of the TREC 2019 deep learning track

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Disentangling Locality and Entropy in Ranking Distillation Overview of the TREC 2020 deep learning track

Reference 46

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Disentangling Locality and Entropy in Ranking Distillation BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models

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Disentangling Locality and Entropy in Ranking Distillation a rvelin and Jaana Kek \

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Disentangling Locality and Entropy in Ranking Distillation Le, and Christopher D

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Disentangling Locality and Entropy in Ranking Distillation Auto-Encoding Variational Bayes

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Disentangling Locality and Entropy in Ranking Distillation Decoupled weight decay regularization

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Disentangling Locality and Entropy in Ranking Distillation HuggingFace's Transformers: State-of-the-art Natural Language Processing

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Disentangling Locality and Entropy in Ranking Distillation Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 53

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Disentangling Locality and Entropy in Ranking Distillation Declarative experimentation ininformation retrieval using pyterrier

Reference 54

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Disentangling Locality and Entropy in Ranking Distillation Unresolved cited work

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Disentangling Locality and Entropy in Ranking Distillation On the optimality of conditional expectation as a bregman predictor

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Disentangling Locality and Entropy in Ranking Distillation Unresolved cited work

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Disentangling Locality and Entropy in Ranking Distillation Probability inequalities for sums of bounded random variables

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Disentangling Locality and Entropy in Ranking Distillation On the density of families of sets

Reference 59

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