| Nome |
# |
|
null, file e0d6c92c-5d5c-fcf8-e053-d805fe0aa794
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324
|
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Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees, file e0d6c92c-ca10-fcf8-e053-d805fe0aa794
|
278
|
|
Efficient query processing for scalable web search, file e0d6c931-3b36-fcf8-e053-d805fe0aa794
|
208
|
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Quality versus efficiency in document scoring with learning-to-rank models, file e0d6c930-4c9f-fcf8-e053-d805fe0aa794
|
182
|
|
Parallel Traversal of Large Ensembles of Decision Tree, file e0d6c92c-80de-fcf8-e053-d805fe0aa794
|
104
|
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Energy-Efficient Query Processing in Web Search Engines, file e0d6c930-6078-fcf8-e053-d805fe0aa794
|
49
|
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null, file e0d6c92d-f5b5-fcf8-e053-d805fe0aa794
|
29
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|
A Systematic Mapping Study of Italian Research on Workflows, file b2e561c5-ef07-4901-9a3e-bbabdf073ebc
|
25
|
|
Neural network quantization in federated learning at the edge, file e0d6c931-79a9-fcf8-e053-d805fe0aa794
|
19
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|
QuickScorer: Efficient Traversal of Large Ensembles of Decision Trees, file e0d6c92f-69da-fcf8-e053-d805fe0aa794
|
18
|
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Learning Passage Impacts for Inverted Indexes, file e0d6c931-55ab-fcf8-e053-d805fe0aa794
|
15
|
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On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval, file e0d6c931-5b42-fcf8-e053-d805fe0aa794
|
13
|
|
PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval, file e0d6c931-5b44-fcf8-e053-d805fe0aa794
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13
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GPU-based parallelization of QuickScorer to speed-up document ranking with tree ensembles, file e0d6c930-16dc-fcf8-e053-d805fe0aa794
|
10
|
|
Energy-Efficient Query Processing in Web Search Engines, file e0d6c92e-21cf-fcf8-e053-d805fe0aa794
|
9
|
|
Efficient & Effective Selective Query Rewriting with Efficiency Predictions, file e0d6c930-a536-fcf8-e053-d805fe0aa794
|
9
|
|
Training Curricula for Open Domain Answer Re-Ranking, file e0d6c92f-681b-fcf8-e053-d805fe0aa794
|
8
|
|
Exploiting green energy to reduce the operational costs of multi-center web search engines, file e0d6c92e-14be-fcf8-e053-d805fe0aa794
|
7
|
|
Weighting Passages Enhances Accuracy, file e0d6c931-a312-fcf8-e053-d805fe0aa794
|
7
|
|
Hybrid Query Scheduling for a Replicated Search Engine, file e0d6c92d-cf4c-fcf8-e053-d805fe0aa794
|
6
|
|
Design of Fault Tolerant Distributed Cyber-Physical Systems for Smart Environments, file e0d6c931-6749-fcf8-e053-d805fe0aa794
|
6
|
|
Adaptive Re-Ranking with a Corpus Graph, file 6714e58b-d131-4639-894b-85513fea3ace
|
5
|
|
QuickScorer: a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e0d6c92f-f76d-fcf8-e053-d805fe0aa794
|
5
|
|
Speeding-up document scoring with tree ensembles using CPU SIMD extensions, file e0d6c92f-f998-fcf8-e053-d805fe0aa794
|
5
|
|
Optimal space-time tradeoffs for inverted indexes, file e0d6c930-a6c3-fcf8-e053-d805fe0aa794
|
5
|
|
Load-sensitive CPU power management for Web search engines, file e0d6c92e-14c0-fcf8-e053-d805fe0aa794
|
4
|
|
Faster blockmaxwand with variable-sized blocks, file e0d6c930-57b8-fcf8-e053-d805fe0aa794
|
4
|
|
Quality versus efficiency in document scoring with learning-to-rank models, file e0d6c930-7c99-fcf8-e053-d805fe0aa794
|
4
|
|
Efficient & Effective Selective Query Rewriting with Efficiency Predictions, file e0d6c930-a537-fcf8-e053-d805fe0aa794
|
4
|
|
Parallel Traversal of Large Ensembles of Decision Tree, file e0d6c930-ab15-fcf8-e053-d805fe0aa794
|
4
|
|
A Federated Channel Modeling System using Generative Neural Networks, file 17792252-3e41-46af-9786-6cc63608242f
|
3
|
|
A Dependency-Aware Utterances Permutation Strategy to Improve Conversational Evaluation, file 3d601f02-9fc2-4ad0-af36-3cce6c4cf1f7
|
3
|
|
A Static Pruning Study on Sparse Neural Retrievers, file 59da8ae7-0ab9-43ed-96c2-3e96d2b2e0ba
|
3
|
|
Artificial intelligence of things at the edge: Scalable and efficient distributed learning for massive scenarios, file d15259e8-ac94-4456-965a-261189dd351d
|
3
|
|
On Cloud-Based Multisource Reliable Multicast Transport in Broadband Multimedia Satellite Networks, file e0d6c92d-bad2-fcf8-e053-d805fe0aa794
|
3
|
|
A self-adapting latency/power tradeoff model for replicated search engines, file e0d6c92d-ec98-fcf8-e053-d805fe0aa794
|
3
|
|
Topic Propagation in Conversational Search, file e0d6c92f-6815-fcf8-e053-d805fe0aa794
|
3
|
|
Exploiting green energy to reduce the operational costs of multi-center web search engines, file e0d6c930-a6c6-fcf8-e053-d805fe0aa794
|
3
|
|
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval, file e0d6c931-55a9-fcf8-e053-d805fe0aa794
|
3
|
|
Hierarchical Dependence-aware Evaluation Measures for Conversational Search, file e0d6c931-9499-fcf8-e053-d805fe0aa794
|
3
|
|
Reproducibility, Replicability, and Insights into Dense Multi-Representation Retrieval Models: from ColBERT to Col, file f8298897-2cfc-4e0a-baa6-16515a777d96
|
3
|
|
Improving Conversational Evaluation via a Dependency-Aware Permutation Strategy, file 28b60985-4c6f-46ab-b29a-b5dff7988b06
|
2
|
|
KafkaFed: Two-Tier Federated Learning Communication Architecture for Internet of Vehicles, file 4c9dd131-c862-48bf-bdc8-4e8bfea1bbbc
|
2
|
|
A Spatial Approach to Predict Performance of Conversational Search Systems, file 50b64775-6468-49ca-b07a-b783115ffca5
|
2
|
|
ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval, file 53d2ee30-2ecc-4918-84c9-e741b7275ede
|
2
|
|
An Intelligent system for the categorization of question time official documents of the Italian Chamber of Deputies, file 63a3e82c-4199-455e-88cc-d0362177d6ba
|
2
|
|
Faster Learned Sparse Retrieval with Guided Traversal, file 6d832a62-207b-4228-8277-d08c132c00b4
|
2
|
|
Static Pruning for Multi-Representation Dense Retrieval, file 7d6b9599-63a1-441c-b8ed-bb98cf1ca212
|
2
|
|
Latency-Energy Tradeoffs in Federated Learning on Resource Constrained Edge Computing Systems, file b64f223e-c5f4-4816-8151-524c9e6f26f5
|
2
|
|
Declarative Experimentation in Information Retrieval using PyTerrier, file e0d6c92f-ed14-fcf8-e053-d805fe0aa794
|
2
|
|
Cache optimization via topics in web search engines, file e0d6c930-5206-fcf8-e053-d805fe0aa794
|
2
|
|
QuickScorer: a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e0d6c930-a4b4-fcf8-e053-d805fe0aa794
|
2
|
|
Optimal space-time tradeoffs for inverted indexes, file e0d6c930-a6c4-fcf8-e053-d805fe0aa794
|
2
|
|
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees, file e0d6c930-a6c8-fcf8-e053-d805fe0aa794
|
2
|
|
Faster blockmaxwand with variable-sized blocks, file e0d6c930-c1ad-fcf8-e053-d805fe0aa794
|
2
|
|
Query Embedding Pruning for Dense Retrieval, file e0d6c931-4677-fcf8-e053-d805fe0aa794
|
2
|
|
Cache Optimization Via Topics in Web Search Engines, file e0d6c931-62cd-fcf8-e053-d805fe0aa794
|
2
|
|
On the Role of Relevance in Natural Language Processing Tasks, file eb985b87-15f5-47d2-814e-1573ff0add23
|
2
|
|
Drivers Stress Identification in Real-World Driving Tasks, file 4e5d3eb7-7d1b-4afd-8d53-35acc07c47f6
|
1
|
|
FedTCS: Federated Learning with Time-based Client Selection to Optimize Edge Resources, file 65a902f9-a89a-4e78-8904-0da57cd44659
|
1
|
|
Speeding up document ranking with rank-based features, file e0d6c92d-c8ae-fcf8-e053-d805fe0aa794
|
1
|
|
Dataset Popularity Prediction for Caching of CMS Big Data, file e0d6c92d-df66-fcf8-e053-d805fe0aa794
|
1
|
|
Multiple query processing via logic function factoring, file e0d6c92d-e10a-fcf8-e053-d805fe0aa794
|
1
|
|
Enhanced news retrieval: Passages lead the way!, file e0d6c92d-e10c-fcf8-e053-d805fe0aa794
|
1
|
|
Performance Analysis of WebRTC-Based Video Streaming Over Power Constrained Platforms, file e0d6c92e-1232-fcf8-e053-d805fe0aa794
|
1
|
|
Upper bound approximations for BlockMaxWand, file e0d6c92e-1234-fcf8-e053-d805fe0aa794
|
1
|
|
The MIDAS cloud platform for testing SOA applications, file e0d6c92e-14c7-fcf8-e053-d805fe0aa794
|
1
|
|
Queuing theory-based latency/Power tradeoff models for replicated search engines, file e0d6c92e-21c3-fcf8-e053-d805fe0aa794
|
1
|
|
Midas: A cloud platform for SOA testing as a service, file e0d6c92e-21c5-fcf8-e053-d805fe0aa794
|
1
|
|
Topical result caching in web search engines, file e0d6c92e-36f5-fcf8-e053-d805fe0aa794
|
1
|
|
Using chemical reactions to model service composition, file e0d6c92e-826e-fcf8-e053-d805fe0aa794
|
1
|
|
Weighting Passages Enhances Accuracy, file e0d6c92f-b8b2-fcf8-e053-d805fe0aa794
|
1
|
|
null, file e0d6c930-ee81-fcf8-e053-d805fe0aa794
|
1
|
|
On single and multiple representations in dense passage retrieval, file e0d6c931-79ad-fcf8-e053-d805fe0aa794
|
1
|
| Totale |
1.462 |