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CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | yoo2019learning | \cite{yoo2019learning} | Learning Loss for Active Learning | http://arxiv.org/abs/1905.03677v1 | The performance of deep neural networks improves with more annotated data.
The problem is that the budget for annotation is limited. One solution to this
is active learning, where a model asks human to annotate data that it perceived
as uncertain. A variety of recent methods have been proposed to apply active
learning ... | true | true | Yoo, Donggeun and Kweon, In So | 2,019 | null | null | null | null | Learning Loss for Active Learning | Learning Loss for Active Learning | http://arxiv.org/pdf/1905.03677v1 | The performance of deep neural networks improves with more annotated data.
The problem is that the budget for annotation is limited. One solution to this
is active learning, where a model asks human to annotate data that it perceived
as uncertain. A variety of recent methods have been proposed to apply active
learning ... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | yuan2021multiple | \cite{yuan2021multiple} | Multiple instance active learning for object detection | http://arxiv.org/abs/2104.02324v1 | Despite the substantial progress of active learning for image recognition,
there still lacks an instance-level active learning method specified for object
detection. In this paper, we propose Multiple Instance Active Object Detection
(MI-AOD), to select the most informative images for detector training by
observing ins... | true | true | Yuan, Tianning and Wan, Fang and Fu, Mengying and Liu, Jianzhuang and Xu, Songcen and Ji, Xiangyang and Ye, Qixiang | 2,021 | null | null | null | null | Multiple instance active learning for object detection | Multiple instance active learning for object detection | http://arxiv.org/pdf/2104.02324v1 | Despite the substantial progress of active learning for image recognition,
there still lacks an instance-level active learning method specified for object
detection. In this paper, we propose Multiple Instance Active Object Detection
(MI-AOD), to select the most informative images for detector training by
observing ins... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | fu2021agreement | \cite{fu2021agreement} | Agreement-Discrepancy-Selection: Active learning with progressive distribution alignment | null | null | true | false | Fu, Mengying and Yuan, Tianning and Wan, Fang and Xu, Songcen and Ye, Qixiang | 2,021 | null | null | null | null | Agreement-Discrepancy-Selection: Active learning with progressive distribution alignment | [PDF] Selection: Active Learning with Progressive Distribution Alignment | https://cdn.aaai.org/ojs/16915/16915-13-20409-1-2-20210518.pdf | In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignment with sample selection by. |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | konevcny2015federated | \cite{konevcny2015federated} | Federated Optimization:Distributed Optimization Beyond the Datacenter | http://arxiv.org/abs/1511.03575v1 | We introduce a new and increasingly relevant setting for distributed
optimization in machine learning, where the data defining the optimization are
distributed (unevenly) over an extremely large number of \nodes, but the goal
remains to train a high-quality centralized model. We refer to this setting as
Federated Optim... | true | true | Kone{\v{c}}n{\`y}, Jakub and McMahan, Brendan and Ramage, Daniel | 2,015 | null | null | null | null | Federated Optimization:Distributed Optimization Beyond the Datacenter | Federated Optimization:Distributed Optimization Beyond the Datacenter | http://arxiv.org/pdf/1511.03575v1 | We introduce a new and increasingly relevant setting for distributed
optimization in machine learning, where the data defining the optimization are
distributed (unevenly) over an extremely large number of \nodes, but the goal
remains to train a high-quality centralized model. We refer to this setting as
Federated Optim... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | mcmahan2017communication | \cite{mcmahan2017communication} | Communication-Efficient Learning of Deep Networks from Decentralized
Data | http://arxiv.org/abs/1602.05629v4 | Modern mobile devices have access to a wealth of data suitable for learning
models, which in turn can greatly improve the user experience on the device.
For example, language models can improve speech recognition and text entry, and
image models can automatically select good photos. However, this rich data is
often pri... | true | true | McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera | 2,017 | null | null | null | null | Communication-Efficient Learning of Deep Networks from Decentralized
Data | [1602.05629] Communication-Efficient Learning of Deep Networks ... | https://arxiv.org/abs/1602.05629 | Communication-Efficient Learning of Deep Networks from Decentralized Data. Authors:H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | ahmed2020active | \cite{ahmed2020active} | Active learning based federated learning for waste and natural disaster image classification | null | null | true | false | Ahmed, Lulwa and Ahmad, Kashif and Said, Naina and Qolomany, Basheer and Qadir, Junaid and Al-Fuqaha, Ala | 2,020 | null | null | null | IEEE Access | Active learning based federated learning for waste and natural disaster image classification | Active Learning Based Federated Learning for Waste and ... | https://ieeexplore.ieee.org/document/9261337/ | by L Ahmed · 2020 · Cited by 96 — Active Learning (AL) provides an alternative solution allowing a Machine Learning (ML) model to automatically choose and label the data from |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | mohammad21flare | \cite{mohammad21flare} | {FLARE:} Federated active learning assisted by naming for responding
to emergencies | null | null | true | false | Mittal, Viyom and Jahanian, Mohammad and Ramakrishnan, K. K. | 2,021 | null | null | null | null | {FLARE:} Federated active learning assisted by naming for responding
to emergencies | FLARE: Federated Active Learning Assisted by Naming for ... | https://ieeexplore.ieee.org/document/9651978 | DEMO: FLARE: Federated Active Learning Assisted by Naming for Responding to Emergencies | IEEE Conference Publication | IEEE Xplore * IEEE.org * IEEE _Xplore_ * IEEE SA * IEEE Spectrum Image 1: IEEE Xplore logo - Link to home Image 2: IEEE logo - Link to IEEE main site homepage Conferences>2021 IEEE 29th Intern... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | jia2019active | \cite{jia2019active} | Active Learning Solution on Distributed Edge Computing | http://arxiv.org/abs/1906.10718v1 | Industry 4.0 becomes possible through the convergence between Operational and
Information Technologies. All the requirements to realize the convergence is
integrated on the Fog Platform. Fog Platform is introduced between the cloud
server and edge devices when the unprecedented generation of data causes the
burden of t... | true | true | Jia Qian and Sayantan Sengupta and Lars Kai Hansen | 2,019 | null | null | null | null | Active Learning Solution on Distributed Edge Computing | Active Learning Solution on Distributed Edge Computing | http://arxiv.org/pdf/1906.10718v1 | Industry 4.0 becomes possible through the convergence between Operational and
Information Technologies. All the requirements to realize the convergence is
integrated on the Fog Platform. Fog Platform is introduced between the cloud
server and edge devices when the unprecedented generation of data causes the
burden of t... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | nicolas2020combine | \cite{nicolas2020combine} | Combining Federated and Active Learning for Communication-efficient
Distributed Failure Prediction in Aeronautics | http://arxiv.org/abs/2001.07504v1 | Machine Learning has proven useful in the recent years as a way to achieve
failure prediction for industrial systems. However, the high computational
resources necessary to run learning algorithms are an obstacle to its
widespread application. The sub-field of Distributed Learning offers a solution
to this problem by e... | true | true | Nicolas Aussel and Sophie Chabridon and Yohan Petetin | 2,020 | null | null | null | null | Combining Federated and Active Learning for Communication-efficient
Distributed Failure Prediction in Aeronautics | Combining Federated and Active Learning for Communication ... | https://www.researchgate.net/publication/338737955_Combining_Federated_and_Active_Learning_for_Communication-efficient_Distributed_Failure_Prediction_in_Aeronautics | In this paper, we propose a distributed learning approach able to optimize the use of computational and communication resources to achieve |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | jin2022federated | \cite{jin2022federated} | Federated Active Learning (F-AL): an Efficient Annotation Strategy for
Federated Learning | http://arxiv.org/abs/2202.00195v2 | Federated learning (FL) has been intensively investigated in terms of
communication efficiency, privacy, and fairness. However, efficient annotation,
which is a pain point in real-world FL applications, is less studied. In this
project, we propose to apply active learning (AL) and sampling strategy into
the FL framewor... | true | true | Jin{-}Hyun Ahn and Kyung Sang Kim and Jeongwan Koh and Quanzheng Li | 2,022 | null | null | null | null | Federated Active Learning (F-AL): an Efficient Annotation Strategy for
Federated Learning | Federated Active Learning (F-AL): an Efficient Annotation Strategy ... | https://arxiv.org/abs/2202.00195 | In this project, we propose to apply active learning (AL) and sampling strategy into the FL framework to reduce the annotation workload. |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | 10184650 | \cite{10184650} | Distribution-Regularized Federated Learning on Non-IID Data | null | null | true | false | Wang, Yansheng and Tong, Yongxin and Zhou, Zimu and Zhang, Ruisheng and Pan, Sinno Jialin and Fan, Lixin and Yang, Qiang | 2,023 | null | null | null | null | Distribution-Regularized Federated Learning on Non-IID Data | Distribution-Regularized Federated Learning on Non-IID Data | https://ieeexplore.ieee.org/document/10184650 | We propose a distribution regularization for FL on non-IID data such that the discrepancy of data distributions between clients is reduced. |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | jia2020Robust | \cite{jia2020Robust} | Robustness analytics to data heterogeneity in edge computing | http://arxiv.org/abs/2002.05038v2 | Federated Learning is a framework that jointly trains a model \textit{with}
complete knowledge on a remotely placed centralized server, but
\textit{without} the requirement of accessing the data stored in distributed
machines. Some work assumes that the data generated from edge devices are
identically and independently... | true | true | Jia Qian and Lars Kai Hansen and Xenofon Fafoutis and Prayag Tiwari and Hari Mohan Pandey | 2,020 | null | null | null | Comput. Commun. | Robustness analytics to data heterogeneity in edge computing | Robustness analytics to data heterogeneity in edge computing | http://arxiv.org/pdf/2002.05038v2 | Federated Learning is a framework that jointly trains a model \textit{with}
complete knowledge on a remotely placed centralized server, but
\textit{without} the requirement of accessing the data stored in distributed
machines. Some work assumes that the data generated from edge devices are
identically and independently... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | cao2022knowledgeaware | \cite{cao2022knowledgeaware} | Knowledge-Aware Federated Active Learning with Non-IID Data | http://arxiv.org/abs/2211.13579v3 | Federated learning enables multiple decentralized clients to learn
collaboratively without sharing the local training data. However, the expensive
annotation cost to acquire data labels on local clients remains an obstacle in
utilizing local data. In this paper, we propose a federated active learning
paradigm to effici... | true | true | Yu-Tong Cao and Jingya Wang and Ye Shi and Baosheng Yu and Dacheng Tao | 2,023 | null | null | null | null | Knowledge-Aware Federated Active Learning with Non-IID Data | [PDF] Knowledge-Aware Federated Active Learning with Non-IID Data | https://openaccess.thecvf.com/content/ICCV2023/papers/Cao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.pdf | This paper devised a Knowledge-Aware Federated Active Learning (KAFAL) method for federated active learning with non-IID data. KAFAL computes the |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | kim2023rethinking | \cite{kim2023rethinking} | Re-thinking Federated Active Learning based on Inter-class Diversity | http://arxiv.org/abs/2303.12317v1 | Although federated learning has made awe-inspiring advances, most studies
have assumed that the client's data are fully labeled. However, in a real-world
scenario, every client may have a significant amount of unlabeled instances.
Among the various approaches to utilizing unlabeled data, a federated active
learning fra... | true | true | SangMook Kim and Sangmin Bae and Hwanjun Song and Se-Young Yun | 2,023 | null | null | null | null | Re-thinking Federated Active Learning based on Inter-class Diversity | [PDF] Re-Thinking Federated Active Learning Based on Inter-Class Diversity | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.pdf | Hence, in the FAL framework, the active selection algo- rithm has to ensure inter-class diversity from both local and global perspectives. Second, there are two |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | goetz2019active | \cite{goetz2019active} | Active Federated Learning | http://arxiv.org/abs/1909.12641v1 | Federated Learning allows for population level models to be trained without
centralizing client data by transmitting the global model to clients,
calculating gradients locally, then averaging the gradients. Downloading models
and uploading gradients uses the client's bandwidth, so minimizing these
transmission costs is... | true | true | Goetz, Jack and Malik, Kshitiz and Bui, Duc and Moon, Seungwhan and Liu, Honglei and Kumar, Anuj | 2,019 | null | null | null | null | Active Federated Learning | Active Federated Learning | http://arxiv.org/pdf/1909.12641v1 | Federated Learning allows for population level models to be trained without
centralizing client data by transmitting the global model to clients,
calculating gradients locally, then averaging the gradients. Downloading models
and uploading gradients uses the client's bandwidth, so minimizing these
transmission costs is... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | li2021sample | \cite{li2021sample} | Sample-level data selection for federated learning | null | null | true | false | Li, Anran and Zhang, Lan and Tan, Juntao and Qin, Yaxuan and Wang, Junhao and Li, Xiang-Yang | 2,021 | null | null | null | null | Sample-level data selection for federated learning | Sample-level Data Selection for Federated Learning - IEEE Xplore | https://ieeexplore.ieee.org/iel7/9488422/9488423/09488723.pdf | In FL systems, the selection of training samples has a significant impact on model performances, e.g., selecting participants whose datasets have erroneous |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | shin2022sample | \cite{shin2022sample} | Sample selection with deadline control for efficient federated learning on heterogeneous clients | null | null | true | false | Shin, Jaemin and Li, Yuanchun and Liu, Yunxin and Lee, Sung-Ju | 2,022 | null | null | null | null | Sample selection with deadline control for efficient federated learning on heterogeneous clients | Sample Selection with Deadline Control for Efficient Federated ... - dblp | https://dblp.org/rec/journals/corr/abs-2201-01601 | Bibliographic details on Sample Selection with Deadline Control for Efficient Federated Learning on Heterogeneous Clients. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | wang2024must | \cite{wang2024must} | MUST: An Effective and Scalable Framework for Multimodal Search of
Target Modality | http://arxiv.org/abs/2312.06397v1 | We investigate the problem of multimodal search of target modality, where the
task involves enhancing a query in a specific target modality by integrating
information from auxiliary modalities. The goal is to retrieve relevant objects
whose contents in the target modality match the specified multimodal query. The
paper... | true | true | Wang, Mengzhao and Ke, Xiangyu and Xu, Xiaoliang and Chen, Lu and Gao, Yunjun and Huang, Pinpin and Zhu, Runkai | 2,024 | null | null | null | null | MUST: An Effective and Scalable Framework for Multimodal Search of
Target Modality | An Effective and Scalable Framework for Multimodal ... | https://ieeexplore.ieee.org/iel8/10597630/10597390/10597872.pdf | by M Wang · 2024 · Cited by 12 — MUST is a framework for multimodal search of target modality, enhancing a query by integrating information from auxiliary modalities. It uses a hybrid fusion |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | yu2014large | \cite{yu2014large} | Large-Scale Multi-Label Learning with Incomplete Label Assignments | http://arxiv.org/abs/1407.1538v1 | Multi-label learning deals with the classification problems where each
instance can be assigned with multiple labels simultaneously. Conventional
multi-label learning approaches mainly focus on exploiting label correlations.
It is usually assumed, explicitly or implicitly, that the label sets for
training instances are... | true | true | Yu, Hsiang-Fu and Jain, Prateek and Kar, Purushottam and Dhillon, Inderjit | 2,014 | null | null | null | null | Large-Scale Multi-Label Learning with Incomplete Label Assignments | Large-Scale Multi-Label Learning with Incomplete Label Assignments | https://arxiv.org/abs/1407.1538 | In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | xu2020product | \cite{xu2020product} | Product Knowledge Graph Embedding for E-commerce | http://arxiv.org/abs/1911.12481v1 | In this paper, we propose a new product knowledge graph (PKG) embedding
approach for learning the intrinsic product relations as product knowledge for
e-commerce. We define the key entities and summarize the pivotal product
relations that are critical for general e-commerce applications including
marketing, advertiseme... | true | true | Xu, Da and Ruan, Chuanwei and Korpeoglu, Evren and Kumar, Sushant and Achan, Kannan | 2,020 | null | null | null | null | Product Knowledge Graph Embedding for E-commerce | Product Knowledge Graph Embedding for E-commerce | http://arxiv.org/pdf/1911.12481v1 | In this paper, we propose a new product knowledge graph (PKG) embedding
approach for learning the intrinsic product relations as product knowledge for
e-commerce. We define the key entities and summarize the pivotal product
relations that are critical for general e-commerce applications including
marketing, advertiseme... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | asai2023retrieval | \cite{asai2023retrieval} | Retrieval-based language models and applications | null | null | true | false | Asai, Akari and Min, Sewon and Zhong, Zexuan and Chen, Danqi | 2,023 | null | null | null | null | Retrieval-based language models and applications | ACL 2023 Tutorial:Retrieval-based Language Models ... - YouTube | https://www.youtube.com/watch?v=BsxxjMPu-YM | This content isn't available. ACL 2023 Tutorial:Retrieval-based Language Models and Applications. 2.1K views · 1 year ago ...more. 哈哈大笑和哈. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | huang2020embedding | \cite{huang2020embedding} | Embedding-based Retrieval in Facebook Search | http://arxiv.org/abs/2006.11632v2 | Search in social networks such as Facebook poses different challenges than in
classical web search: besides the query text, it is important to take into
account the searcher's context to provide relevant results. Their social graph
is an integral part of this context and is a unique aspect of Facebook search.
While emb... | true | true | Huang, Jui-Ting and Sharma, Ashish and Sun, Shuying and Xia, Li and Zhang, David and Pronin, Philip and Padmanabhan, Janani and Ottaviano, Giuseppe and Yang, Linjun | 2,020 | null | null | null | null | Embedding-based Retrieval in Facebook Search | Embedding-based Retrieval in Facebook Search | https://dl.acm.org/doi/10.1145/3394486.3403305 | In this paper, we discuss the techniques for applying EBR to a Facebook Search system. We introduce the unified embedding framework developed to model semantic |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | radford2021learning | \cite{radford2021learning} | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/abs/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... | true | true | Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others | 2,021 | null | null | null | null | Learning Transferable Visual Models From Natural Language Supervision | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/pdf/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | wang2017survey | \cite{wang2017survey} | A Survey on Learning to Hash | http://arxiv.org/abs/1606.00185v2 | Nearest neighbor search is a problem of finding the data points from the
database such that the distances from them to the query point are the smallest.
Learning to hash is one of the major solutions to this problem and has been
widely studied recently. In this paper, we present a comprehensive survey of
the learning t... | true | true | Wang, Jingdong and Zhang, Ting and Sebe, Nicu and Shen, Heng Tao and others | 2,017 | null | null | null | TPAMI | A Survey on Learning to Hash | A Survey on Learning to Hash | http://arxiv.org/pdf/1606.00185v2 | Nearest neighbor search is a problem of finding the data points from the
database such that the distances from them to the query point are the smallest.
Learning to hash is one of the major solutions to this problem and has been
widely studied recently. In this paper, we present a comprehensive survey of
the learning t... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | wei2024det | \cite{wei2024det} | Det-lsh: a locality-sensitive hashing scheme with dynamic encoding tree for approximate nearest neighbor search | null | null | true | false | Wei, Jiuqi and Peng, Botao and Lee, Xiaodong and Palpanas, Themis | 2,024 | null | null | null | arXiv preprint arXiv:2406.10938 | Det-lsh: a locality-sensitive hashing scheme with dynamic encoding tree for approximate nearest neighbor search | DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic ... | https://www.researchgate.net/publication/382927854_DET-LSH_A_Locality-Sensitive_Hashing_Scheme_with_Dynamic_Encoding_Tree_for_Approximate_Nearest_Neighbor_Search | Based on DE-Tree, we propose a novel LSH scheme called DET-LSH. DET-LSH adopts a novel query strategy, which performs range queries in multiple independent |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | shrivastava2014asymmetric | \cite{shrivastava2014asymmetric} | Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search
(MIPS) | http://arxiv.org/abs/1405.5869v1 | We present the first provably sublinear time algorithm for approximate
\emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first
hashing algorithm for searching with (un-normalized) inner product as the
underlying similarity measure. Finding hashing schemes for MIPS was considered
hard. We formally sho... | true | true | Shrivastava, Anshumali and Li, Ping | 2,014 | null | null | null | null | Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search
(MIPS) | [1405.5869] Asymmetric LSH (ALSH) for Sublinear Time ... | https://arxiv.org/abs/1405.5869 | by A Shrivastava · 2014 · Cited by 612 — Abstract:We present the first provably sublinear time algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | shrivastava2015improved | \cite{shrivastava2015improved} | Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner
Product Search (MIPS) | http://arxiv.org/abs/1410.5410v2 | Recently it was shown that the problem of Maximum Inner Product Search (MIPS)
is efficient and it admits provably sub-linear hashing algorithms. Asymmetric
transformations before hashing were the key in solving MIPS which was otherwise
hard. In the prior work, the authors use asymmetric transformations which
convert th... | true | true | Shrivastava, Anshumali and Li, Ping | 2,015 | null | null | null | null | Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner
Product Search (MIPS) | [1410.5410] Improved Asymmetric Locality Sensitive Hashing (ALSH ... | https://arxiv.org/abs/1410.5410 | Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | bachrach2014speeding | \cite{bachrach2014speeding} | Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces | null | null | true | false | Bachrach, Yoram and Finkelstein, Yehuda and Gilad-Bachrach, Ran and Katzir, Liran and Koenigstein, Noam and Nice, Nir and Paquet, Ulrich | 2,014 | null | null | null | null | Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces | [PDF] Speeding Up the Xbox Recommender System Using a Euclidean ... | https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/XboxInnerProduct.pdf | The paper speeds up Xbox recommendations by transforming the inner product problem to a Euclidean space, using a PCA-Tree data structure and neighborhood |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | yan2018norm | \cite{yan2018norm} | Norm-Ranging LSH for Maximum Inner Product Search | http://arxiv.org/abs/1809.08782v2 | Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art
hashing method for maximum inner product search (MIPS) with performance
guarantee. We found that the performance of Simple-LSH, in both theory and
practice, suffers from long tails in the 2-norm distribution of real datasets.
We propose Norm-rangin... | true | true | Yan, Xiao and Li, Jinfeng and Dai, Xinyan and Chen, Hongzhi and Cheng, James | 2,018 | null | null | null | null | Norm-Ranging LSH for Maximum Inner Product Search | Norm-Ranging LSH for Maximum Inner Product Search | https://arxiv.org/abs/1809.08782 | by X Yan · 2018 · Cited by 70 — We propose Norm-ranging LSH, which addresses the excessive normalization problem caused by long tails in Simple-LSH by partitioning a dataset |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | neyshabur2015symmetric | \cite{neyshabur2015symmetric} | On Symmetric and Asymmetric LSHs for Inner Product Search | http://arxiv.org/abs/1410.5518v3 | We consider the problem of designing locality sensitive hashes (LSH) for
inner product similarity, and of the power of asymmetric hashes in this
context. Shrivastava and Li argue that there is no symmetric LSH for the
problem and propose an asymmetric LSH based on different mappings for query and
database points. Howev... | true | true | Neyshabur, Behnam and Srebro, Nathan | 2,015 | null | null | null | null | On Symmetric and Asymmetric LSHs for Inner Product Search | On Symmetric and Asymmetric LSHs for Inner Product Search | http://arxiv.org/pdf/1410.5518v3 | We consider the problem of designing locality sensitive hashes (LSH) for
inner product similarity, and of the power of asymmetric hashes in this
context. Shrivastava and Li argue that there is no symmetric LSH for the
problem and propose an asymmetric LSH based on different mappings for query and
database points. Howev... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | zhao2023fargo | \cite{zhao2023fargo} | FARGO: Fast maximum inner product search via global multi-probing | null | null | true | false | Zhao, Xi and Zheng, Bolong and Yi, Xiaomeng and Luan, Xiaofan and Xie, Charles and Zhou, Xiaofang and Jensen, Christian S | 2,023 | null | null | null | PVLDB | FARGO: Fast maximum inner product search via global multi-probing | [PDF] FARGO: Fast Maximum Inner Product Search via Global Multi-Probing | https://www.vldb.org/pvldb/vol16/p1100-zheng.pdf | FARGO is a fast search framework for MIPS using global multi-probing (GMP) to examine high-quality candidates, unlike Multi-Probe. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | song2021promips | \cite{song2021promips} | ProMIPS: Efficient high-dimensional C-approximate maximum inner product search with a lightweight index | null | null | true | false | Song, Yang and Gu, Yu and Zhang, Rui and Yu, Ge | 2,021 | null | null | null | null | ProMIPS: Efficient high-dimensional C-approximate maximum inner product search with a lightweight index | ProMIPS: Efficient High-Dimensional c-Approximate Maximum Inner ... | https://arxiv.org/abs/2104.04406 | In this paper, we relax the guarantee of accuracy for efficiency and propose an efficient method for c-Approximate Maximum Inner Product (c-AMIP) search with a |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | ma2024reconsidering | \cite{ma2024reconsidering} | Reconsidering Tree based Methods for k-Maximum Inner-Product Search: The LRUS-CoverTree | null | null | true | false | Ma, Hengzhao and Li, Jianzhong and Zhang, Yong | 2,024 | null | null | null | null | Reconsidering Tree based Methods for k-Maximum Inner-Product Search: The LRUS-CoverTree | Reconsidering Tree based Methods for k-Maximum Inner- ... | https://ieeexplore.ieee.org/document/10598031/ | by H Ma · 2024 · Cited by 4 — The new k- Maximum Inner-Product Search algorithm based on LRUS-CoverTree outperforms the state-of-the-art locality sensitive hashing based methods. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | dai2020norm | \cite{dai2020norm} | Norm-Explicit Quantization: Improving Vector Quantization for Maximum
Inner Product Search | http://arxiv.org/abs/1911.04654v2 | Vector quantization (VQ) techniques are widely used in similarity search for
data compression, fast metric computation and etc. Originally designed for
Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly or
implicitly minimize the quantization error. In this paper, we present a new
angle to analyze the... | true | true | Dai, Xinyan and Yan, Xiao and Ng, Kelvin KW and Liu, Jiu and Cheng, James | 2,020 | null | null | null | null | Norm-Explicit Quantization: Improving Vector Quantization for Maximum
Inner Product Search | Improving Vector Quantization for Maximum Inner Product Search | https://arxiv.org/abs/1911.04654 | We propose norm-explicit quantization (NEQ) --- a general paradigm that improves existing VQ techniques for MIPS. |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | guo2020accelerating | \cite{guo2020accelerating} | Accelerating Large-Scale Inference with Anisotropic Vector Quantization | http://arxiv.org/abs/1908.10396v5 | Quantization based techniques are the current state-of-the-art for scaling
maximum inner product search to massive databases. Traditional approaches to
quantization aim to minimize the reconstruction error of the database points.
Based on the observation that for a given query, the database points that have
the largest... | true | true | Guo, Ruiqi and Sun, Philip and Lindgren, Erik and Geng, Quan and Simcha, David and Chern, Felix and Kumar, Sanjiv | 2,020 | null | null | null | null | Accelerating Large-Scale Inference with Anisotropic Vector Quantization | Accelerating Large-Scale Inference with Anisotropic Vector ... | https://arxiv.org/abs/1908.10396 | > cs > arXiv:1908.10396 arXiv:1908.10396 (cs) Authors:Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar View a PDF of the paper titled Accelerating Large-Scale Inference with Anisotropic Vector Quantization, by Ruiqi Guo and 6 other authors Subjects:Machine Learning (cs.LG); Machi... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | sun2024soar | \cite{sun2024soar} | SOAR: Improved Indexing for Approximate Nearest Neighbor Search | http://arxiv.org/abs/2404.00774v1 | This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals,
a novel data indexing technique for approximate nearest neighbor (ANN) search.
SOAR extends upon previous approaches to ANN search, such as spill trees, that
utilize multiple redundant representations while partitioning the data to
reduce the ... | true | true | Sun, Philip and Simcha, David and Dopson, Dave and Guo, Ruiqi and Kumar, Sanjiv | 2,023 | null | null | null | null | SOAR: Improved Indexing for Approximate Nearest Neighbor Search | SOAR: Improved Indexing for Approximate Nearest Neighbor Search | http://arxiv.org/pdf/2404.00774v1 | This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals,
a novel data indexing technique for approximate nearest neighbor (ANN) search.
SOAR extends upon previous approaches to ANN search, such as spill trees, that
utilize multiple redundant representations while partitioning the data to
reduce the ... |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | morozov2018non | \cite{morozov2018non} | Non-metric similarity graphs for maximum inner product search | null | null | true | false | Morozov, Stanislav and Babenko, Artem | 2,018 | null | null | null | null | Non-metric similarity graphs for maximum inner product search | Reviews: Non-metric Similarity Graphs for Maximum Inner ... | https://proceedings.neurips.cc/paper/2018/file/229754d7799160502a143a72f6789927-Reviews.html | This paper addresses the Maximum Inner Product Search (MIPS) problem by using the popular Approximate Nearest Neighbor Search (ANN Search) technique: Navigable |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | liu2020understanding | \cite{liu2020understanding} | Understanding and Improving Proximity Graph based Maximum Inner Product
Search | http://arxiv.org/abs/1909.13459v2 | The inner-product navigable small world graph (ip-NSW) represents the
state-of-the-art method for approximate maximum inner product search (MIPS) and
it can achieve an order of magnitude speedup over the fastest baseline.
However, to date it is still unclear where its exceptional performance comes
from. In this paper, ... | true | true | Liu, Jie and Yan, Xiao and Dai, Xinyan and Li, Zhirong and Cheng, James and Yang, Ming-Chang | 2,020 | null | null | null | null | Understanding and Improving Proximity Graph based Maximum Inner Product
Search | Understanding and Improving Proximity Graph based ... | https://arxiv.org/abs/1909.13459 | by J Liu · 2019 · Cited by 39 — The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | zhou2019mobius | \cite{zhou2019mobius} | M{\"o}bius transformation for fast inner product search on graph | null | null | true | false | Zhou, Zhixin and Tan, Shulong and Xu, Zhaozhuo and Li, Ping | 2,019 | null | null | null | null | M{\"o}bius transformation for fast inner product search on graph | Möbius transformation for fast inner product search on graph | https://dl.acm.org/doi/10.5555/3454287.3455025 | by Z Zhou · 2019 · Cited by 68 — Our proposed method is based on the property that Möbius transformation introduces an isomorphism between a subgraph of ℓ2-Delaunay graph and Delaunay graph for |
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
Inner Product Search | 2504.14861v1 | tan2021norm | \cite{tan2021norm} | Norm adjusted proximity graph for fast inner product retrieval | null | null | true | false | Tan, Shulong and Xu, Zhaozhuo and Zhao, Weijie and Fei, Hongliang and Zhou, Zhixin and Li, Ping | 2,021 | null | null | null | null | Norm adjusted proximity graph for fast inner product retrieval | Norm Adjusted Proximity Graph for Fast Inner Product Retrieval | https://oa.mg/work/10.1145/3447548.3467412 | “Norm Adjusted Proximity Graph for Fast Inner Product Retrieval” is a paper by Shulong Tan Zhaozhuo Xu Weijie Zhao Hongliang Fei Zhixin Zhou Ping Li published |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Terry1994a | \cite{Terry1994a} | Session Guarantees for Weakly Consistent Replicated Data | null | null | true | false | Terry, D.B. and Demers, A.J. and Petersen, K. and Spreitzer, M.J. and Theimer, M.M. and Welch, B.B. | 1,994 | null | https://ieeexplore.ieee.org/document/331722 | 10.1109/PDIS.1994.331722 | null | Session Guarantees for Weakly Consistent Replicated Data | Session Guarantees for Weakly Consistent Replicated Data | https://www.cs.cornell.edu/courses/cs734/2000FA/cached%20papers/SessionGuaranteesPDIS_1.html | Four per-session guarantees are proposed to aid users and applications of weakly consistent replicated data: Read Your Writes, Monotonic Reads, Writes Follow |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Berenson1995 | \cite{Berenson1995} | A Critique of ANSI SQL Isolation Levels | http://arxiv.org/abs/cs/0701157v1 | ANSI SQL-92 defines Isolation Levels in terms of phenomena: Dirty Reads,
Non-Repeatable Reads, and Phantoms. This paper shows that these phenomena and
the ANSI SQL definitions fail to characterize several popular isolation levels,
including the standard locking implementations of the levels. Investigating the
ambiguiti... | true | true | Berenson, Hal and Bernstein, Phil and Gray, Jim and Melton, Jim and O'Neil, Elizabeth and O'Neil, Patrick | 1,995 | null | null | 10.1145/568271.223785 | SIGMOD Rec. | A Critique of ANSI SQL Isolation Levels | A Critique of ANSI SQL Isolation Levels | http://arxiv.org/pdf/cs/0701157v1 | ANSI SQL-92 defines Isolation Levels in terms of phenomena: Dirty Reads,
Non-Repeatable Reads, and Phantoms. This paper shows that these phenomena and
the ANSI SQL definitions fail to characterize several popular isolation levels,
including the standard locking implementations of the levels. Investigating the
ambiguiti... |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Adya2000 | \cite{Adya2000} | Generalized Isolation Level Definitions | null | null | true | false | Adya, A. and Liskov, B. and O'Neil, P. | 2,000 | null | null | 10.1109/ICDE.2000.839388 | null | Generalized Isolation Level Definitions | Reviews for Paper 8-Generalized Isolation Level Definitions | https://web.eecs.umich.edu/~mozafari/fall2018/eecs584/reviews/summaries/summary8.html | The author proposes the generalized isolation level definitions which are precise and implementation-independent (locking, optimism, serialization). It adopts |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Crooks2017 | \cite{Crooks2017} | Seeing Is {{Believing}}: {{A Client-Centric Specification}} of {{Database Isolation}} | null | null | true | false | Crooks, Natacha and Pu, Youer and Alvisi, Lorenzo and Clement, Allen | 2,017 | null | null | 10.1145/3087801.3087802 | null | Seeing Is {{Believing}}: {{A Client-Centric Specification}} of {{Database Isolation}} | [PDF] A Client-Centric Specification of Database Isolation | https://www.cs.cornell.edu/lorenzo/papers/Crooks17Seeing.pdf | Seeing is Believing: A Client-Centric Specification of Database. Isolation. Natacha Crooks. The University of Texas at Austin and Cornell University. Youer Pu. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Burckhardt2014 | \cite{Burckhardt2014} | Replicated data types: specification, verification, optimality | null | null | true | false | Burckhardt, Sebastian and Gotsman, Alexey and Yang, Hongseok and Zawirski, Marek | 2,014 | null | https://doi.org/10.1145/2535838.2535848 | 10.1145/2535838.2535848 | null | Replicated data types: specification, verification, optimality | Replicated data types: specification, verification, optimality | https://dl.acm.org/doi/10.1145/2578855.2535848 | We propose a framework for specifying replicated data types using relations over events and verifying their implementations using replication-aware simulations. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Cerone2015 | \cite{Cerone2015} | A {{Framework}} for {{Transactional Consistency Models}} with {{Atomic Visibility}} | null | null | true | false | Cerone, Andrea and Bernardi, Giovanni and Gotsman, Alexey | 2,015 | null | null | 10.4230/LIPIcs.CONCUR.2015.58 | null | A {{Framework}} for {{Transactional Consistency Models}} with {{Atomic Visibility}} | A Framework for Transactional Consistency Models with ... | https://drops.dagstuhl.de/storage/00lipics/lipics-vol042-concur2015/LIPIcs.CONCUR.2015.58/LIPIcs.CONCUR.2015.58.pdf | by A Cerone · 2015 · Cited by 134 — A Framework for Transactional Consistency Models with Atomic Visibility. Our work systematises the knowledge about consistency models of replicated databases. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Biswas2019 | \cite{Biswas2019} | On the Complexity of Checking Transactional Consistency | http://arxiv.org/abs/1908.04509v1 | Transactions simplify concurrent programming by enabling computations on
shared data that are isolated from other concurrent computations and are
resilient to failures. Modern databases provide different consistency models
for transactions corresponding to different tradeoffs between consistency and
availability. In th... | true | true | Biswas, Ranadeep and Enea, Constantin | 2,019 | null | null | 10.1145/3360591 | Proceedings of the ACM on Programming Languages | On the Complexity of Checking Transactional Consistency | On the Complexity of Checking Transactional Consistency | http://arxiv.org/pdf/1908.04509v1 | Transactions simplify concurrent programming by enabling computations on
shared data that are isolated from other concurrent computations and are
resilient to failures. Modern databases provide different consistency models
for transactions corresponding to different tradeoffs between consistency and
availability. In th... |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Liu2024a | \cite{Liu2024a} | Plume: Efficient and Complete Black-Box Checking of Weak Isolation
Levels | null | null | true | false | Si Liu and
Long Gu and
Hengfeng Wei and
David A. Basin | 2,024 | null | https://doi.org/10.1145/3689742 | 10.1145/3689742 | Proc. {ACM} Program. Lang. | Plume: Efficient and Complete Black-Box Checking of Weak Isolation
Levels | Efficient and Complete Black-box Checking of Weak Isolation ... | https://2024.splashcon.org/details/splash-2024-oopsla/85/Plume-Efficient-and-Complete-Black-box-Checking-of-Weak-Isolation-Levels | In this paper we present Plume, the first efficient, complete, black-box checker for weak isolation levels. Plume builds on modular, fine-grained, transactional |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Tan2020 | \cite{Tan2020} | Cobra: Making Transactional Key-Value Stores Verifiably Serializable | null | null | true | false | Cheng Tan and
Changgeng Zhao and
Shuai Mu and
Michael Walfish | 2,020 | null | https://www.usenix.org/conference/osdi20/presentation/tan | null | null | Cobra: Making Transactional Key-Value Stores Verifiably Serializable | Making transactional key-value stores verifiably serializable | https://dl.acm.org/doi/abs/10.5555/3488766.3488770 | by C Tan · 2020 · Cited by 61 — COBRA tames that problem by starting with a suitable SMT solver. COBRA then introduces several new techniques, including a new encoding of the |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Geng2024 | \cite{Geng2024} | IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable
Behaviors in Weakly Isolated Data Store Applications | http://arxiv.org/abs/2404.04621v1 | This paper presents the first dynamic predictive analysis for data store
applications under weak isolation levels, called Isopredict. Given an observed
serializable execution of a data store application, Isopredict generates and
solves SMT constraints to find an unserializable execution that is a feasible
execution of ... | true | true | Geng, Chujun and Blanas, Spyros and Bond, Michael D. and Wang, Yang | 2,024 | null | null | 10.1145/3656391 | Reproduction Package for 'IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications' | IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable
Behaviors in Weakly Isolated Data Store Applications | Chujun Geng - - researchr.org | https://conf.researchr.org/profile/conf/chujungeng | Author of IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications within the PLDI Research |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Zhang2023a | \cite{Zhang2023a} | Viper: {{A Fast Snapshot Isolation Checker}} | null | null | true | false | Zhang, Jian and Ji, Ye and Mu, Shuai and Tan, Cheng | 2,023 | null | null | 10.1145/3552326.3567492 | null | Viper: {{A Fast Snapshot Isolation Checker}} | Viper: A Fast Snapshot Isolation Checker - ACM Digital Library | https://dl.acm.org/doi/10.1145/3552326.3567492 | We present viper, an SI checker that is sound, complete, and fast. Viper checks black-box databases and hence is transparent to both users and databases. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Huang2023b | \cite{Huang2023b} | Efficient Black-box Checking of Snapshot Isolation in Databases | http://arxiv.org/abs/2301.07313v2 | Snapshot isolation (SI) is a prevalent weak isolation level that avoids the
performance penalty imposed by serializability and simultaneously prevents
various undesired data anomalies. Nevertheless, SI anomalies have recently been
found in production cloud databases that claim to provide the SI guarantee.
Given the com... | true | true | Huang, Kaile and Liu, Si and Chen, Zhenge and Wei, Hengfeng and Basin, David and Li, Haixiang and Pan, Anqun | 2,023 | null | null | 10.14778/3583140.3583145 | Proc. VLDB Endow. | Efficient Black-box Checking of Snapshot Isolation in Databases | Efficient Black-Box Checking of Snapshot Isolation in Databases | https://dl.acm.org/doi/abs/10.14778/3583140.3583145 | by K Huang · 2023 · Cited by 19 — In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Papadimitriou1979a | \cite{Papadimitriou1979a} | The Serializability of Concurrent Database Updates | null | null | true | false | Papadimitriou, Christos H. | 1,979 | null | https://dl.acm.org/doi/10.1145/322154.322158 | 10.1145/322154.322158 | Journal of the ACM | The Serializability of Concurrent Database Updates | MIT/LCS/TR-210 - Serializability of - CSAIL Publications | https://publications.csail.mit.edu/lcs/pubs/pdf/MIT-LCS-TR-210.pdf | The Serializability of Concurrent Database Updates* by. Christos H. Papadimitriou. Massachusetts Institute of Technology. Abstract. A sequence of interleaved |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Furbach2015 | \cite{Furbach2015} | Memory-Model-Aware Testing: A Unified Complexity Analysis | null | null | true | false | Furbach, Florian and Meyer, Roland and Schneider, Klaus and Senftleben, Maximilian | 2,015 | null | https://doi.org/10.1145/2753761 | 10.1145/2753761 | ACM Trans. Embed. Comput. Syst. | Memory-Model-Aware Testing: A Unified Complexity Analysis | Memory-Model-Aware Testing: A Unified Complexity Analysis | https://dl.acm.org/doi/10.1145/2753761 | We determine the complexity of the testing problem for most of the known memory models. Moreover, we study the impact on the complexity of parameters, such as |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Gibbons1997 | \cite{Gibbons1997} | Testing {{Shared Memories}} | null | null | true | false | Gibbons, Phillip B. and Korach, Ephraim | 1,997 | null | http://epubs.siam.org/doi/10.1137/S0097539794279614 | 10.1137/S0097539794279614 | SIAM Journal on Computing | Testing {{Shared Memories}} | Testing Shared Memories | SIAM Journal on Computing | https://epubs.siam.org/doi/10.1137/S0097539794279614 | A series of results are presented for testing an execution of a shared memory under various scenarios, comparing sequential consistency with linearizability, |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Gibbons1994 | \cite{Gibbons1994} | On testing cache-coherent shared memories | null | null | true | false | Gibbons, Phillip B and Korach, Ephraim | 1,994 | null | null | null | null | On testing cache-coherent shared memories | On testing cache-coherent shared memories - ACM Digital Library | https://dl.acm.org/doi/pdf/10.1145/181014.181328 | We present a series of re- sults for testing an execution of a shared memory under scenarios that exploit the cache-coherence protocol. In ad- dition to reads |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Abdulla2019b | \cite{Abdulla2019b} | {Optimal stateless model checking for reads-from equivalence under sequential consistency} | null | null | true | false | Parosh Aziz Abdulla and Mohamed Faouzi Atig and Bengt Jonsson and Magnus L{\aa}ng and Tuan Phong Ngo and Konstantinos Sagonas | null | null | null | 10.1145/3360576 | Proc. {ACM} Program. Lang. | {Optimal stateless model checking for reads-from equivalence under sequential consistency} | Optimal stateless model checking for reads-from equivalence ... | https://dl.acm.org/doi/10.1145/3360576 | We present a new approach for stateless model checking (SMC) of multithreaded programs under Sequential Consistency (SC) semantics. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Chalupa2018 | \cite{Chalupa2018} | Data-centric Dynamic Partial Order Reduction | http://arxiv.org/abs/1610.01188v6 | We present a new dynamic partial-order reduction method for stateless model
checking of concurrent programs. A common approach for exploring program
behaviors relies on enumerating the traces of the program, without storing the
visited states (aka stateless exploration). As the number of distinct traces
grows exponenti... | true | true | Chalupa, Marek and Chatterjee, Krishnendu and Pavlogiannis, Andreas and Sinha, Nishant and Vaidya, Kapil | 2,018 | null | null | 10.1145/3158119 | Proceedings of the ACM on Programming Languages | Data-centric Dynamic Partial Order Reduction | [1610.01188] Data-centric Dynamic Partial Order Reduction - arXiv | https://arxiv.org/abs/1610.01188 | Abstract:We present a new dynamic partial-order reduction method for stateless model checking of concurrent programs. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Mathur2020 | \cite{Mathur2020} | The Complexity of Dynamic Data Race Prediction | http://arxiv.org/abs/2004.14931v2 | Writing concurrent programs is notoriously hard due to scheduling
non-determinism. The most common concurrency bugs are data races, which are
accesses to a shared resource that can be executed concurrently. Dynamic
data-race prediction is the most standard technique for detecting data races:
given an observed, data-rac... | true | true | Mathur, Umang and Pavlogiannis, Andreas and Viswanathan, Mahesh | 2,020 | null | https://dl.acm.org/doi/10.1145/3373718.3394783 | 10.1145/3373718.3394783 | null | The Complexity of Dynamic Data Race Prediction | The Complexity of Dynamic Data Race Prediction | http://arxiv.org/pdf/2004.14931v2 | Writing concurrent programs is notoriously hard due to scheduling
non-determinism. The most common concurrency bugs are data races, which are
accesses to a shared resource that can be executed concurrently. Dynamic
data-race prediction is the most standard technique for detecting data races:
given an observed, data-rac... |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Bui2021 | \cite{Bui2021} | The Reads-From Equivalence for the TSO and PSO Memory Models | http://arxiv.org/abs/2011.11763v3 | The verification of concurrent programs remains an open challenge due to the
non-determinism in inter-process communication. One algorithmic problem in this
challenge is the consistency verification of concurrent executions. Consistency
verification under a reads-from map allows to compute the reads-from (RF)
equivalen... | true | true | Bui, Truc Lam and Chatterjee, Krishnendu and Gautam, Tushar and Pavlogiannis, Andreas and Toman, Viktor | 2,021 | null | https://dl.acm.org/doi/10.1145/3485541 | 10.1145/3485541 | Proceedings of the ACM on Programming Languages | The Reads-From Equivalence for the TSO and PSO Memory Models | The reads-from equivalence for the TSO and PSO memory ... | https://dl.acm.org/doi/10.1145/3485541 | In this work we solve the algorithmic problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Baty2011 | \cite{Baty2011} | Mathematizing C++ concurrency | null | null | true | false | Batty, Mark and Owens, Scott and Sarkar, Susmit and Sewell, Peter and Weber, Tjark | 2,011 | null | https://doi.org/10.1145/1926385.1926394 | 10.1145/1926385.1926394 | null | Mathematizing C++ concurrency | [PDF] Mathematizing C++ Concurrency - University of Cambridge | https://www.cl.cam.ac.uk/~pes20/cpp/popl085ap-sewell.pdf | Here we describe C++ concurrency incrementally, starting with single-threaded programs and then adding threads and locks, SC atomics, and low-level atomics ( |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Lahav2015 | \cite{Lahav2015} | Owicki-{{Gries Reasoning}} for {{Weak Memory Models}} | null | null | true | false | Lahav, Ori and Vafeiadis, Viktor | 2,015 | null | https://link.springer.com/10.1007/978-3-662-47666-6_25 | 10.1007/978-3-662-47666-6_25 | null | Owicki-{{Gries Reasoning}} for {{Weak Memory Models}} | Owicki-Gries Reasoning for Weak Memory Models | https://plv.mpi-sws.org/ogra/ | We show that even in the absence of auxiliary variables, the well-known Owicki-Gries method for verifying concurrent programs is unsound for weak memory models. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Bouajjani2017a | \cite{Bouajjani2017a} | On Verifying Causal Consistency | http://arxiv.org/abs/1611.00580v2 | Causal consistency is one of the most adopted consistency criteria for
distributed implementations of data structures. It ensures that operations are
executed at all sites according to their causal precedence. We address the
issue of verifying automatically whether the executions of an implementation of
a data structur... | true | true | Bouajjani, Ahmed and Enea, Constantin and Guerraoui, Rachid and Hamza, Jad | 2,017 | null | https://dl.acm.org/doi/10.1145/3093333.3009888 | 10.1145/3093333.3009888 | SIGPLAN Not. | On Verifying Causal Consistency | On Verifying Causal Consistency | http://arxiv.org/pdf/1611.00580v2 | Causal consistency is one of the most adopted consistency criteria for
distributed implementations of data structures. It ensures that operations are
executed at all sites according to their causal precedence. We address the
issue of verifying automatically whether the executions of an implementation of
a data structur... |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Chakraborty2024a | \cite{Chakraborty2024a} | How Hard is Weak-Memory Testing? | http://arxiv.org/abs/2311.04302v2 | Weak-memory models are standard formal specifications of concurrency across
hardware, programming languages, and distributed systems. A fundamental
computational problem is consistency testing: is the observed execution of a
concurrent program in alignment with the specification of the underlying
system? The problem ha... | true | true | Chakraborty, Soham and Krishna, Shankara Narayanan and Mathur, Umang and Pavlogiannis, Andreas | 2,024 | null | https://dl.acm.org/doi/10.1145/3632908 | 10.1145/3632908 | Proceedings of the ACM on Programming Languages | How Hard is Weak-Memory Testing? | [2311.04302] How Hard is Weak-Memory Testing? - arXiv | https://arxiv.org/abs/2311.04302 | The main contribution of this paper is a deep hardness result for consistency testing under many popular weak-memory models. |
AWDIT: An Optimal Weak Database Isolation Tester | 2504.06975v1 | Tunc2023 | \cite{Tunc2023} | Optimal Reads-From Consistency Checking for C11-Style Memory Models | http://arxiv.org/abs/2304.03714v2 | Over the years, several memory models have been proposed to capture the
subtle concurrency semantics of C/C++.One of the most fundamental problems
associated with a memory model M is consistency checking: given an execution X,
is X consistent with M? This problem lies at the heart of numerous
applications, including sp... | true | true | Tun{\c c}, H{\"u}nkar Can and Abdulla, Parosh Aziz and Chakraborty, Soham and Krishna, Shankaranarayanan and Mathur, Umang and Pavlogiannis, Andreas | 2,023 | null | https://dl.acm.org/doi/10.1145/3591251 | 10.1145/3591251 | Proceedings of the ACM on Programming Languages | Optimal Reads-From Consistency Checking for C11-Style Memory Models | [PDF] Optimal Reads-From Consistency Checking for C11-Style Memory ... | https://www.comp.nus.edu.sg/~umathur/papers/rc20-rf-consistency-pldi23.pdf | In this work we study the problem of consistency checking for popular variants of the C11 memory model, in particular, the RC20 model, its release-acquire. (RA) |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | kathail2024leveraging | \cite{kathail2024leveraging} | Leveraging genomic deep learning models for non-coding variant effect
prediction | http://arxiv.org/abs/2411.11158v1 | The majority of genetic variants identified in genome-wide association
studies of complex traits are non-coding, and characterizing their function
remains an important challenge in human genetics. Genomic deep learning models
have emerged as a promising approach to enable in silico prediction of variant
effects. These ... | true | true | Kathail, Pooja and Bajwa, Ayesha and Ioannidis, Nilah M | 2,024 | null | null | null | arXiv preprint arXiv:2411.11158 | Leveraging genomic deep learning models for non-coding variant effect
prediction | Leveraging genomic deep learning models for non-coding ... | https://arxiv.org/abs/2411.11158 | by P Kathail · 2024 · Cited by 4 — Here, we review progress in leveraging these models for non-coding variant effect prediction. We describe practical considerations for making such predictions. |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | zhou2015predicting | \cite{zhou2015predicting} | Predicting effects of noncoding variants with deep learning--based sequence model | null | null | true | false | Zhou, Jian and Troyanskaya, Olga G | 2,015 | null | null | null | Nature methods | Predicting effects of noncoding variants with deep learning--based sequence model | Predicting effects of noncoding variants with deep learning-based ... | https://pubmed.ncbi.nlm.nih.gov/26301843/ | To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations w... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | kelley2018sequential | \cite{kelley2018sequential} | Sequential regulatory activity prediction across chromosomes with convolutional neural networks | null | null | true | false | Kelley, David R and Reshef, Yakir A and Bileschi, Maxwell and Belanger, David and McLean, Cory Y and Snoek, Jasper | 2,018 | null | null | null | Genome research | Sequential regulatory activity prediction across chromosomes with convolutional neural networks | Sequential regulatory activity prediction across chromosomes with ... | https://pubmed.ncbi.nlm.nih.gov/29588361/ | By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. (_A_) The _AKT2_ locus exemplifies the genome-wide accuracy of Basenji predictions; gene promoters and the strongest distal regulatory ele... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | zhou2018deep | \cite{zhou2018deep} | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | null | null | true | false | Zhou, Jian and Theesfeld, Chandra L and Yao, Kevin and Chen, Kathleen M and Wong, Aaron K and Troyanskaya, Olga G | 2,018 | null | null | null | Nature genetics | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | Deep learning sequence-based ab initio prediction of variant effects ... | https://www.nature.com/articles/s41588-018-0160-6 | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | Nature Genetics Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variatio... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | chen2022sequence | \cite{chen2022sequence} | A sequence-based global map of regulatory activity for deciphering human genetics | null | null | true | false | Chen, Kathleen M and Wong, Aaron K and Troyanskaya, Olga G and Zhou, Jian | 2,022 | null | null | null | Nature genetics | A sequence-based global map of regulatory activity for deciphering human genetics | A sequence-based global map of regulatory activity for deciphering ... | https://www.nature.com/articles/s41588-022-01102-2 | Sequence classes cover diverse types of regulatory activities, such as promoter or cell type-specific enhancer activity, across the whole genome by integrating sequence-based predictions from histone marks, TFs and chromatin accessibility across a wide range of cell types. Next, we applied the Sei model to develop a gl... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | enformer | \cite{enformer} | Effective gene expression prediction from sequence by integrating long-range interactions | null | null | true | false | Avsec, {\v{Z}}iga and Agarwal, Vikram and Visentin, Daniel and Ledsam, Joseph R and Grabska-Barwinska, Agnieszka and Taylor, Kyle R and Assael, Yannis and Jumper, John and Kohli, Pushmeet and Kelley, David R | 2,021 | null | null | null | Nature methods | Effective gene expression prediction from sequence by integrating long-range interactions | Effective gene expression prediction from sequence by ... | https://www.nature.com/articles/s41592-021-01252-x | Here, we report substantially improved gene expression prediction accuracy from DNA sequences through the use of a deep learning architecture, called Enformer, that is able to integrate information from long-range interactions (up to 100 kb away) in the genome. We developed a new model architecture named Enformer (a po... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | NT | \cite{NT} | Nucleotide Transformer: building and evaluating robust foundation models for human genomics | null | null | true | false | Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza-Revilla, Javier and Lopez Carranza, Nicolas and Grzywaczewski, Adam Henryk and Oteri, Francesco and Dallago, Christian and Trop, Evan and de Almeida, Bernardo P and Sirelkhatim, Hassan and others | 2,024 | null | null | null | Nature Methods | Nucleotide Transformer: building and evaluating robust foundation models for human genomics | Nucleotide Transformer: building and evaluating robust foundation ... | https://www.nature.com/articles/s41592-024-02523-z | Here, we present an extensive study of foundation models pre-trained on DNA sequences, named Nucleotide Transformer, ranging from 50 million up to 2.5 billion parameters and integrating information from 3,202 human genomes and 850 genomes from diverse species. Inspired by trends in NLP, where larger training datasets a... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | DNABert | \cite{DNABert} | DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome | null | null | true | false | Ji, Yanrong and Zhou, Zhihan and Liu, Han and Davuluri, Ramana V | 2,021 | null | null | null | Bioinformatics | DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome | DNABERT: pre-trained Bidirectional Encoder Representations from ... | https://pubmed.ncbi.nlm.nih.gov/33538820/ | ## Save citation to file # DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome # DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome To address this challenge, we developed a novel pre-trained bidirection... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | devlin2019bert | \cite{devlin2019bert} | BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding | http://arxiv.org/abs/1810.04805v2 | We introduce a new language representation model called BERT, which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language representation models, BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning on
both left and right contex... | true | true | Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina | 2,019 | null | null | null | null | BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding | [PDF] BERT: Pre-training of Deep Bidirectional Transformers for Language ... | https://aclanthology.org/N19-1423.pdf | Unlike recent language repre-sentation models (Peters et al., 2018a; Rad-ford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a re-sult, the pre-trained BERT model can be fine-tuned with just one ... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | celikkanatrevisiting | \cite{celikkanatrevisiting} | Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning | null | null | true | false | Celikkanat, Abdulkadir and Masegosa, Andres R and Nielsen, Thomas Dyhre | 2,024 | null | null | null | null | Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning | Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning | http://arxiv.org/pdf/2411.02125v1 | Obtaining effective representations of DNA sequences is crucial for genome
analysis. Metagenomic binning, for instance, relies on genome representations
to cluster complex mixtures of DNA fragments from biological samples with the
aim of determining their microbial compositions. In this paper, we revisit
k-mer-based re... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | DNABert2 | \cite{DNABert2} | DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes | null | null | true | false | Zhou, Zhihan and Ji, Yanrong and Li, Weijian and Dutta, Pratik and Davuluri, Ramana V and Liu, Han | 2,024 | null | null | null | null | DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes | DNABERT-2: Efficient Foundation Model and Benchmark for Multi ... | https://github.com/MAGICS-LAB/DNABERT_2 | GitHub - MAGICS-LAB/DNABERT_2: [ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome [ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome DNABERT-2 is a foundation model tr... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | sanabria2024dna | \cite{sanabria2024dna} | DNA language model GROVER learns sequence context in the human genome | null | null | true | false | Sanabria, Melissa and Hirsch, Jonas and Joubert, Pierre M and Poetsch, Anna R | 2,024 | null | null | null | Nature Machine Intelligence | DNA language model GROVER learns sequence context in the human genome | DNA language model GROVER learns sequence context in ... - Nature | https://www.nature.com/articles/s42256-024-00872-0 | DNA language model GROVER learns sequence context in the human genome | Nature Machine Intelligence DNA language model GROVER learns sequence context in the human genome We established byte-pair encoding on the human genome and trained a foundation language model called GROVER (Genome Rules Obtained Via Extracted Repr... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | nguyen2024sequence | \cite{nguyen2024sequence} | Sequence modeling and design from molecular to genome scale with Evo | null | null | true | false | Nguyen, Eric and Poli, Michael and Durrant, Matthew G and Kang, Brian and Katrekar, Dhruva and Li, David B and Bartie, Liam J and Thomas, Armin W and King, Samuel H and Brixi, Garyk and others | 2,024 | null | null | null | Science | Sequence modeling and design from molecular to genome scale with Evo | Sequence modeling and design from molecular to genome ... | https://pubmed.ncbi.nlm.nih.gov/39541441/ | Sequence modeling and design from molecular to genome scale with Evo - PubMed Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of prot... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | HyenaDNA | \cite{HyenaDNA} | HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide
Resolution | http://arxiv.org/abs/2306.15794v2 | Genomic (DNA) sequences encode an enormous amount of information for gene
regulation and protein synthesis. Similar to natural language models,
researchers have proposed foundation models in genomics to learn generalizable
features from unlabeled genome data that can then be fine-tuned for downstream
tasks such as iden... | true | true | Nguyen, Eric and Poli, Michael and Faizi, Marjan and Thomas, Armin and Wornow, Michael and Birch-Sykes, Callum and Massaroli, Stefano and Patel, Aman and Rabideau, Clayton and Bengio, Yoshua and others | 2,024 | null | null | null | Advances in neural information processing systems | HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide
Resolution | HyenaDNA: Long-Range Genomic Sequence Modeling at Single ... | https://arxiv.org/abs/2306.15794 | View a PDF of the paper titled HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution, by Eric Nguyen and 12 other authors Leveraging Hyena's new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 mill... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | MoE0 | \cite{MoE0} | Adaptive Mixtures of Local Experts | null | null | true | false | Jacobs, Robert A. and Jordan, Michael I. and Nowlan, Steven J. and Hinton, Geoffrey E. | 1,991 | null | null | 10.1162/neco.1991.3.1.79 | Neural Computation | Adaptive Mixtures of Local Experts | Adaptive Mixtures of Local Experts - Computer Science | https://www.cs.toronto.edu/~hinton/absps/jjnh91.pdf | by RA Jacobs · Cited by 7088 — Each expert is a feed- forward network and all experts receive the same input and have the same number of outputs. The gating network is also feedforward, and |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | SparseMoE | \cite{SparseMoE} | Outrageously Large Neural Networks: The Sparsely-Gated
Mixture-of-Experts Layer | http://arxiv.org/abs/1701.06538v1 | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice... | true | true | Shazeer, Noam and Mirhoseini, Azalia and Maziarz, Krzysztof and Davis, Andy and Le, Quoc and Hinton, Geoffrey and Dean, Jeff | 2,017 | null | null | null | arXiv preprint arXiv:1701.06538 | Outrageously Large Neural Networks: The Sparsely-Gated
Mixture-of-Experts Layer | Outrageously Large Neural Networks: The Sparsely-Gated... | https://openreview.net/forum?id=B1ckMDqlg | We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | fedus2022switch | \cite{fedus2022switch} | Switch Transformers: Scaling to Trillion Parameter Models with Simple
and Efficient Sparsity | http://arxiv.org/abs/2101.03961v3 | In deep learning, models typically reuse the same parameters for all inputs.
Mixture of Experts (MoE) defies this and instead selects different parameters
for each incoming example. The result is a sparsely-activated model -- with
outrageous numbers of parameters -- but a constant computational cost. However,
despite s... | true | true | Fedus, William and Zoph, Barret and Shazeer, Noam | 2,022 | null | null | null | Journal of Machine Learning Research | Switch Transformers: Scaling to Trillion Parameter Models with Simple
and Efficient Sparsity | Switch Transformers: Scaling to Trillion Parameter Models ... | https://arxiv.org/abs/2101.03961 | View a PDF of the paper titled Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, by William Fedus and 2 other authors View a PDF of the paper titled Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, by William Fedus and 2 other auth... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | jiang2023mistral | \cite{jiang2023mistral} | Mistral 7B | http://arxiv.org/abs/2310.06825v1 | We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered
for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B
across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and
code generation. Our model leverages grouped-query attention (GQA) for faster
inferenc... | true | true | Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others | 2,023 | null | null | null | arXiv preprint arXiv:2310.06825 | Mistral 7B | Mistral 7B | http://arxiv.org/pdf/2310.06825v1 | We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered
for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B
across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and
code generation. Our model leverages grouped-query attention (GQA) for faster
inferenc... |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | 2506.01833v1 | deepseek | \cite{deepseek} | Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model | null | null | true | false | Liu, Aixin and Feng, Bei and Wang, Bin and Wang, Bingxuan and Liu, Bo and Zhao, Chenggang and Dengr, Chengqi and Ruan, Chong and Dai, Damai and Guo, Daya and others | 2,024 | null | null | null | arXiv preprint arXiv:2405.04434 | Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model | DeepSeek-V2: A Strong, Economical, and Efficient Mixture ... - GitHub | https://github.com/deepseek-ai/DeepSeek-V2 | GitHub - deepseek-ai/DeepSeek-V2: DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model Image 1: DeepSeek-V2 | DeepSeek-V2-Lite-Chat (SFT) | 16B | 2.4B | 32k | 🤗 HuggingFace | | DeepSeek-V2-Chat (RL) | 236B | 21B | 128k | 🤗 HuggingFace | We evaluate our model on AlpacaEval 2.0 and MTBench... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/nips/MorcosRB18 | \cite{DBLP:conf/nips/MorcosRB18} | Insights on representational similarity in neural networks with
canonical correlation | http://arxiv.org/abs/1806.05759v3 | Comparing different neural network representations and determining how
representations evolve over time remain challenging open questions in our
understanding of the function of neural networks. Comparing representations in
neural networks is fundamentally difficult as the structure of representations
varies greatly, e... | true | true | Ari S. Morcos and
Maithra Raghu and
Samy Bengio | 2,018 | null | https://proceedings.neurips.cc/paper/2018/hash/a7a3d70c6d17a73140918996d03c014f-Abstract.html | null | null | Insights on representational similarity in neural networks with
canonical correlation | Reviews: Insights on representational similarity in neural ... | https://proceedings.neurips.cc/paper/2018/file/a7a3d70c6d17a73140918996d03c014f-Reviews.html | This paper presents projection weighted canonical correlation analysis (CCA) as a method to interrogate neural network representations. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/nips/RaghuGYS17 | \cite{DBLP:conf/nips/RaghuGYS17} | SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning
Dynamics and Interpretability | http://arxiv.org/abs/1706.05806v2 | We propose a new technique, Singular Vector Canonical Correlation Analysis
(SVCCA), a tool for quickly comparing two representations in a way that is both
invariant to affine transform (allowing comparison between different layers and
networks) and fast to compute (allowing more comparisons to be calculated than
with p... | true | true | Maithra Raghu and
Justin Gilmer and
Jason Yosinski and
Jascha Sohl{-}Dickstein | 2,017 | null | https://proceedings.neurips.cc/paper/2017/hash/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html | null | null | SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning
Dynamics and Interpretability | SVCCA: Singular Vector Canonical Correlation Analysis for ... | http://papers.neurips.cc/paper/7188-svcca-singular-vector-canonical-correlation-analysis-for-deep-learning-dynamics-and-interpretability.pdf | by M Raghu · Cited by 831 — We propose a new technique, Singular Vector Canonical Correlation Analysis. (SVCCA), a tool for quickly comparing two representations in a way that is both.See more |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/icml/Kornblith0LH19 | \cite{DBLP:conf/icml/Kornblith0LH19} | Similarity of Neural Network Representations Revisited | http://arxiv.org/abs/1905.00414v4 | Recent work has sought to understand the behavior of neural networks by
comparing representations between layers and between different trained models.
We examine methods for comparing neural network representations based on
canonical correlation analysis (CCA). We show that CCA belongs to a family of
statistics for mea... | true | true | Simon Kornblith and
Mohammad Norouzi and
Honglak Lee and
Geoffrey E. Hinton | 2,019 | null | http://proceedings.mlr.press/v97/kornblith19a.html | null | null | Similarity of Neural Network Representations Revisited | Similarity of Neural Network Representations Revisited | http://arxiv.org/pdf/1905.00414v4 | Recent work has sought to understand the behavior of neural networks by
comparing representations between layers and between different trained models.
We examine methods for comparing neural network representations based on
canonical correlation analysis (CCA). We show that CCA belongs to a family of
statistics for mea... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/iclr/NguyenRK21 | \cite{DBLP:conf/iclr/NguyenRK21} | Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural
Network Representations Vary with Width and Depth | null | null | true | false | Thao Nguyen and
Maithra Raghu and
Simon Kornblith | 2,021 | null | https://openreview.net/forum?id=KJNcAkY8tY4 | null | null | Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural
Network Representations Vary with Width and Depth | Do Wide and Deep Networks Learn the Same Things? Uncovering ... | https://openreview.net/forum?id=KJNcAkY8tY4 | This paper studies whether neural networks with different architectures, especially different width and depth, learn similar representations. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | phang-etal-2021-fine | \cite{phang-etal-2021-fine} | Fine-Tuned Transformers Show Clusters of Similar Representations Across
Layers | http://arxiv.org/abs/2109.08406v2 | Despite the success of fine-tuning pretrained language encoders like BERT for
downstream natural language understanding (NLU) tasks, it is still poorly
understood how neural networks change after fine-tuning. In this work, we use
centered kernel alignment (CKA), a method for comparing learned
representations, to measur... | true | true | Phang, Jason and
Liu, Haokun and
Bowman, Samuel R. | 2,021 | null | https://aclanthology.org/2021.blackboxnlp-1.42/ | 10.18653/v1/2021.blackboxnlp-1.42 | null | Fine-Tuned Transformers Show Clusters of Similar Representations Across
Layers | [PDF] Fine-Tuned Transformers Show Clusters of Similar Representations ... | https://aclanthology.org/2021.blackboxnlp-1.42.pdf | In this work, we study how learned representa- tions change through fine-tuning by studying the similarity of representations between layers of |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/nips/LiuCYY24 | \cite{DBLP:conf/nips/LiuCYY24} | Exploring Consistency in Graph Representations:from Graph Kernels to
Graph Neural Networks | http://arxiv.org/abs/2410.23748v2 | Graph Neural Networks (GNNs) have emerged as a dominant approach in graph
representation learning, yet they often struggle to capture consistent
similarity relationships among graphs. While graph kernel methods such as the
Weisfeiler-Lehman subtree (WL-subtree) and Weisfeiler-Lehman optimal assignment
(WLOA) kernels ar... | true | true | Xuyuan Liu and
Yinghao Cai and
Qihui Yang and
Yujun Yan | 2,024 | null | http://papers.nips.cc/paper\_files/paper/2024/hash/f631e778fd3c1b871e9e3a94369335e9-Abstract-Conference.html | null | null | Exploring Consistency in Graph Representations:from Graph Kernels to
Graph Neural Networks | [2410.23748] Exploring Consistency in Graph Representations:from ... | https://arxiv.org/abs/2410.23748 | Our work aims to bridge the gap between neural network methods and kernel approaches by enabling GNNs to consistently capture relational structures in their |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/emnlp/BrownGKTK23 | \cite{DBLP:conf/emnlp/BrownGKTK23} | Understanding the Inner Workings of Language Models Through
Representation Dissimilarity | http://arxiv.org/abs/2310.14993v1 | As language models are applied to an increasing number of real-world
applications, understanding their inner workings has become an important issue
in model trust, interpretability, and transparency. In this work we show that
representation dissimilarity measures, which are functions that measure the
extent to which tw... | true | true | Davis Brown and
Charles Godfrey and
Nicholas Konz and
Jonathan H. Tu and
Henry Kvinge | 2,023 | null | https://doi.org/10.18653/v1/2023.emnlp-main.403 | 10.18653/V1/2023.EMNLP-MAIN.403 | null | Understanding the Inner Workings of Language Models Through
Representation Dissimilarity | Understanding the Inner-workings of Language Models ... | https://openreview.net/forum?id=bZel7wM6fN¬eId=6nDMKGYtp0 | In this work we show that representation dissimilarity measures, which are functions that measure the extent to which two model's internal representations |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | sun2024massive | \cite{sun2024massive} | Massive Activations in Large Language Models | http://arxiv.org/abs/2402.17762v2 | We observe an empirical phenomenon in Large Language Models (LLMs) -- very
few activations exhibit significantly larger values than others (e.g., 100,000
times larger). We call them massive activations. First, we demonstrate the
widespread existence of massive activations across various LLMs and
characterize their loca... | true | true | Mingjie Sun and Xinlei Chen and J Zico Kolter and Zhuang Liu | 2,024 | null | https://openreview.net/forum?id=F7aAhfitX6 | null | null | Massive Activations in Large Language Models | Massive Activations in Large Language Models | http://arxiv.org/pdf/2402.17762v2 | We observe an empirical phenomenon in Large Language Models (LLMs) -- very
few activations exhibit significantly larger values than others (e.g., 100,000
times larger). We call them massive activations. First, we demonstrate the
widespread existence of massive activations across various LLMs and
characterize their loca... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/emnlp/MartinezLB24 | \cite{DBLP:conf/emnlp/MartinezLB24} | Tending Towards Stability: Convergence Challenges in Small Language
Models | http://arxiv.org/abs/2410.11451v1 | Increasing the number of parameters in language models is a common strategy
to enhance their performance. However, smaller language models remain valuable
due to their lower operational costs. Despite their advantages, smaller models
frequently underperform compared to their larger counterparts, even when
provided with... | true | true | Richard Diehl Martinez and
Pietro Lesci and
Paula Buttery | 2,024 | null | https://aclanthology.org/2024.findings-emnlp.187 | null | null | Tending Towards Stability: Convergence Challenges in Small Language
Models | Convergence Challenges in Small Language Models - arXiv | https://arxiv.org/abs/2410.11451 | Abstract page for arXiv paper 2410.11451: Tending Towards Stability: Convergence Challenges in Small Language Models. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/nips/MengBAB22 | \cite{DBLP:conf/nips/MengBAB22} | Locating and Editing Factual Associations in GPT | http://arxiv.org/abs/2202.05262v5 | We analyze the storage and recall of factual associations in autoregressive
transformer language models, finding evidence that these associations
correspond to localized, directly-editable computations. We first develop a
causal intervention for identifying neuron activations that are decisive in a
model's factual pred... | true | true | Kevin Meng and
David Bau and
Alex Andonian and
Yonatan Belinkov | 2,022 | null | http://papers.nips.cc/paper\_files/paper/2022/hash/6f1d43d5a82a37e89b0665b33bf3a182-Abstract-Conference.html | null | null | Locating and Editing Factual Associations in GPT | Locating and Editing Factual Associations in GPT | http://arxiv.org/pdf/2202.05262v5 | We analyze the storage and recall of factual associations in autoregressive
transformer language models, finding evidence that these associations
correspond to localized, directly-editable computations. We first develop a
causal intervention for identifying neuron activations that are decisive in a
model's factual pred... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/emnlp/AzariaM23 | \cite{DBLP:conf/emnlp/AzariaM23} | The Internal State of an LLM Knows When It's Lying | http://arxiv.org/abs/2304.13734v2 | While Large Language Models (LLMs) have shown exceptional performance in
various tasks, one of their most prominent drawbacks is generating inaccurate
or false information with a confident tone. In this paper, we provide evidence
that the LLM's internal state can be used to reveal the truthfulness of
statements. This i... | true | true | Amos Azaria and
Tom M. Mitchell | 2,023 | null | https://doi.org/10.18653/v1/2023.findings-emnlp.68 | 10.18653/V1/2023.FINDINGS-EMNLP.68 | null | The Internal State of an LLM Knows When It's Lying | The Internal State of an LLM Knows When It's Lying | http://arxiv.org/pdf/2304.13734v2 | While Large Language Models (LLMs) have shown exceptional performance in
various tasks, one of their most prominent drawbacks is generating inaccurate
or false information with a confident tone. In this paper, we provide evidence
that the LLM's internal state can be used to reveal the truthfulness of
statements. This i... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/emnlp/ChenTGW00YY24 | \cite{DBLP:conf/emnlp/ChenTGW00YY24} | Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection | http://arxiv.org/abs/2410.02330v1 | As a manner to augment pre-trained large language models (LLM), knowledge
injection is critical to develop vertical domain large models and has been
widely studied. Although most current approaches, including parameter-efficient
fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge
across all LLM la... | true | true | Tianxiang Chen and
Zhentao Tan and
Tao Gong and
Yue Wu and
Qi Chu and
Bin Liu and
Jieping Ye and
Nenghai Yu | 2,024 | null | https://aclanthology.org/2024.findings-emnlp.347 | null | null | Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection | Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection | http://arxiv.org/pdf/2410.02330v1 | As a manner to augment pre-trained large language models (LLM), knowledge
injection is critical to develop vertical domain large models and has been
widely studied. Although most current approaches, including parameter-efficient
fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge
across all LLM la... |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:conf/emnlp/ZhaoLLZ024 | \cite{DBLP:conf/emnlp/ZhaoLLZ024} | Defending Large Language Models Against Jailbreak Attacks via Layer-specific
Editing | null | null | true | false | Wei Zhao and
Zhe Li and
Yige Li and
Ye Zhang and
Jun Sun | 2,024 | null | https://aclanthology.org/2024.findings-emnlp.293 | null | null | Defending Large Language Models Against Jailbreak Attacks via Layer-specific
Editing | Defending Large Language Models Against Jailbreak Attacks via ... | https://aclanthology.org/2024.findings-emnlp.293/ | In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) to enhance the |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | jin-etal-2025-exploring | \cite{jin-etal-2025-exploring} | Exploring Concept Depth: How Large Language Models Acquire Knowledge and
Concept at Different Layers? | http://arxiv.org/abs/2404.07066v7 | Large language models (LLMs) have shown remarkable performances across a wide
range of tasks. However, the mechanisms by which these models encode tasks of
varying complexities remain poorly understood. In this paper, we explore the
hypothesis that LLMs process concepts of varying complexities in different
layers, intr... | true | true | Jin, Mingyu and
Yu, Qinkai and
Huang, Jingyuan and
Zeng, Qingcheng and
Wang, Zhenting and
Hua, Wenyue and
Zhao, Haiyan and
Mei, Kai and
Meng, Yanda and
Ding, Kaize and
Yang, Fan and
Du, Mengnan and
Zhang, Yongfeng | 2,025 | null | https://aclanthology.org/2025.coling-main.37/ | null | null | Exploring Concept Depth: How Large Language Models Acquire Knowledge and
Concept at Different Layers? | Exploring Concept Depth: How Large Language Models ... | https://aclanthology.org/2025.coling-main.37.pdf | by M Jin · 2025 · Cited by 30 — In this paper, we design a probing framework to understand how concepts at various levels are en- coded within LLMs and investigate whether the |
Spectral Insights into Data-Oblivious Critical Layers in Large Language
Models | 2506.00382v1 | DBLP:journals/corr/abs-2412-09563 | \cite{DBLP:journals/corr/abs-2412-09563} | Does Representation Matter? Exploring Intermediate Layers in Large
Language Models | http://arxiv.org/abs/2412.09563v1 | Understanding what defines a good representation in large language models
(LLMs) is fundamental to both theoretical understanding and practical
applications. In this paper, we investigate the quality of intermediate
representations in various LLM architectures, including Transformers and State
Space Models (SSMs). We f... | true | true | Oscar Skean and
Md Rifat Arefin and
Yann LeCun and
Ravid Shwartz{-}Ziv | 2,024 | null | https://doi.org/10.48550/arXiv.2412.09563 | 10.48550/ARXIV.2412.09563 | CoRR | Does Representation Matter? Exploring Intermediate Layers in Large
Language Models | Does Representation Matter? Exploring Intermediate ... | https://openreview.net/forum?id=FN0tZ9pVLz&referrer=%5Bthe%20profile%20of%20Ravid%20Shwartz-Ziv%5D(%2Fprofile%3Fid%3D~Ravid_Shwartz-Ziv2) | We find that intermediate layers consistently provide better representations for downstream tasks compared to final layers.See more |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | rusu2016progressive | \cite{rusu2016progressive} | Progressive Neural Networks | http://arxiv.org/abs/1606.04671v4 | Learning to solve complex sequences of tasks--while both leveraging transfer
and avoiding catastrophic forgetting--remains a key obstacle to achieving
human-level intelligence. The progressive networks approach represents a step
forward in this direction: they are immune to forgetting and can leverage prior
knowledge v... | true | true | Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia | 2,016 | null | null | null | arXiv preprint arXiv:1606.04671 | Progressive Neural Networks | Progressive Neural Networks | http://arxiv.org/pdf/1606.04671v4 | Learning to solve complex sequences of tasks--while both leveraging transfer
and avoiding catastrophic forgetting--remains a key obstacle to achieving
human-level intelligence. The progressive networks approach represents a step
forward in this direction: they are immune to forgetting and can leverage prior
knowledge v... |
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