- published: 10 May 2021
- views: 338
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Similarity search
Similarity Search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects.
Nearest neighbor search and Range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex.
The most general approach to similarity search that allows construction of efficient index structures use the mathematical notion of Metric space.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Similarity_search
Podcasts:
-
BayLearn 2020: Hamming Space Locality Preserving Neural Hashing for Similarity Search
Hamming Space Locality Preserving Neural Hashing for Similarity Search BayLearn 2020 Poster #61 Presenter: Daphna Idelson Abstract: We propose a novel method for learning to map a large-scale dataset in the feature representation space to binary hash codes in the hamming space, for fast and efficient approximate nearest-neighbor similarity search. Our method is composed of a simple neural network and a novel training scheme, that aims to preserve the locality relations between the original data points. We achieve distance preservation of the original cosine space upon the new hamming space, by introducing a loss function that translates the relational similarities in both spaces to probability distributions - and optimizes the KL divergence between them. We also introduce a simple data s...
published: 10 May 2021 -
ICLR14: J Masci: Sparse similarity-preserving hashing
ICLR 2014 Talk: "Sparse similarity-preserving hashing Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro Paper: http://openreview.net/document/69982ebb-b3e4-4685-9ca7-8b29a29b3a39 Slides: http://www.idsia.ch/~masci/slides/2014-ICLR-sparsehash.pdf
published: 19 Apr 2014 -
Algorithmic Speedups via Locality Sensitive Hashing & Bio-Inspired Hashing - September 8, 2021
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and reviews papers on bio-inspired hashing, specifically LSH inspired by fruit flies. He first gives an overview of what algorithmic speedups are, why they are useful and how we can use them. He then dives into a specific technique called locality sensitive hashing (LSH) and goes over the motivations of using these types of hash algorithms and how they work. Lastly, Anshuman talks about the potential biological relevances of these hash mechanisms. He looks at the paper “A neural algorithm for a fundamental computing problem” which outlined a version of LSH inspired by fruit flies that uses sparse projections, expands dimensionality and uses a Winner-Takes-All mechanism. Paper reviewed: “A Neural Algorithm f...
published: 14 Sep 2021 -
SISAP 2020: Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
Authors: Omid Jafari, Parth Nagarkar and Jonathan Montano
published: 30 Oct 2020 -
BayLearn 2020: Batch Reinforcement Learning Through Continuation Method
published: 10 May 2021 -
Deep Learning - 016 Employing indexing structures for efficient retrieval of semantic neighbors
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In cou...
published: 28 Apr 2021 -
Central Similarity Quantization for Efficient Image and Video Retrieval
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng Description: Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., \emph{hash center} that ref...
published: 17 Jul 2020 -
Robust Property Preserving Hashes
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Session
published: 05 Oct 2018 -
Alexandr Andoni - The Geometry of Similarity Search (May 19, 2017)
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
published: 06 Feb 2019 -
Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field. Check out the following interesting papers. Happy learning! Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction" Paper: https://aclanthology.org/2023.findings-eacl.125/ Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion" Paper: https://aclanthology.org/C18-1102/ Paper Title: "Extract with Order for Coherent Multi-Document Summarization" Paper: https://aclan...
published: 25 Sep 2017
developed with YouTube
4:59
BayLearn 2020: Hamming Space Locality Preserving Neural Hashing for Similarity Search
- Order: Reorder
- Duration: 4:59
- Uploaded Date: 10 May 2021
- views: 338
Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020 Poster #61
Presenter: Daphna Idelson
Abstract:
We propose a novel method ...
Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020 Poster #61
Presenter: Daphna Idelson
Abstract:
We propose a novel method for learning to map a large-scale dataset in the feature representation space to binary hash codes in the hamming space, for fast and efficient approximate nearest-neighbor similarity search. Our method is composed of a simple neural network and a novel training scheme, that aims to preserve the locality relations between the original data points. We achieve distance preservation of the original cosine space upon the new hamming space, by introducing a loss function that translates the relational similarities in both spaces to probability distributions - and optimizes the KL divergence between them. We also introduce a simple data sampling method by representing the database with randomly generated proxies, used as reference points to query points from the training set. Experimenting with three publicly available standard ANN benchmarks we demonstrate significant improvement over other binary hashing methods, achieving an improvement of between 7% to 17%. As opposed to other methods, we show high performance in both low (64 bits) and high (768 bits) dimensional bit representation, offering increased accuracy when resources are available and flexibility in choice of ANN strategy.
https://wn.com/Baylearn_2020_Hamming_Space_Locality_Preserving_Neural_Hashing_For_Similarity_Search
Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020 Poster #61
Presenter: Daphna Idelson
Abstract:
We propose a novel method for learning to map a large-scale dataset in the feature representation space to binary hash codes in the hamming space, for fast and efficient approximate nearest-neighbor similarity search. Our method is composed of a simple neural network and a novel training scheme, that aims to preserve the locality relations between the original data points. We achieve distance preservation of the original cosine space upon the new hamming space, by introducing a loss function that translates the relational similarities in both spaces to probability distributions - and optimizes the KL divergence between them. We also introduce a simple data sampling method by representing the database with randomly generated proxies, used as reference points to query points from the training set. Experimenting with three publicly available standard ANN benchmarks we demonstrate significant improvement over other binary hashing methods, achieving an improvement of between 7% to 17%. As opposed to other methods, we show high performance in both low (64 bits) and high (768 bits) dimensional bit representation, offering increased accuracy when resources are available and flexibility in choice of ANN strategy.
14:31
ICLR14: J Masci: Sparse similarity-preserving hashing
- Order: Reorder
- Duration: 14:31
- Uploaded Date: 19 Apr 2014
- views: 549
ICLR 2014 Talk: "Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
Paper: http:/...
ICLR 2014 Talk: "Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
Paper: http://openreview.net/document/69982ebb-b3e4-4685-9ca7-8b29a29b3a39
Slides: http://www.idsia.ch/~masci/slides/2014-ICLR-sparsehash.pdf
https://wn.com/Iclr14_J_Masci_Sparse_Similarity_Preserving_Hashing
ICLR 2014 Talk: "Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
Paper: http://openreview.net/document/69982ebb-b3e4-4685-9ca7-8b29a29b3a39
Slides: http://www.idsia.ch/~masci/slides/2014-ICLR-sparsehash.pdf
- published: 19 Apr 2014
- views: 549
1:05:25
Algorithmic Speedups via Locality Sensitive Hashing & Bio-Inspired Hashing - September 8, 2021
- Order: Reorder
- Duration: 1:05:25
- Uploaded Date: 14 Sep 2021
- views: 1103
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and reviews papers on bio-inspired hashing, specifically LSH inspired by fruit f...
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and reviews papers on bio-inspired hashing, specifically LSH inspired by fruit flies.
He first gives an overview of what algorithmic speedups are, why they are useful and how we can use them. He then dives into a specific technique called locality sensitive hashing (LSH) and goes over the motivations of using these types of hash algorithms and how they work. Lastly, Anshuman talks about the potential biological relevances of these hash mechanisms. He looks at the paper “A neural algorithm for a fundamental computing problem” which outlined a version of LSH inspired by fruit flies that uses sparse projections, expands dimensionality and uses a Winner-Takes-All mechanism.
Paper reviewed: “A Neural Algorithm for a Fundamental Computing Problem” by Dasgupta et al. : https://www.science.org/doi/abs/10.1126/science.aam9868
0:00 Overview
1:11 Algorithmic Speedups
14:28 Locality Sensitive Hashing
45:54 Bio-inspired Hashing
- - - - -
Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications.
Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence:
https://numenta.com/news-digest/
Subscribe to our Newsletter for the latest Numenta updates:
https://tinyurl.com/NumentaNewsletter
Our Social Media:
https://twitter.com/Numenta
https://www.facebook.com/OfficialNumenta
https://www.linkedin.com/company/numenta
Our Open Source Resources:
https://github.com/numenta
https://discourse.numenta.org/
Our Website:
https://numenta.com/
https://wn.com/Algorithmic_Speedups_Via_Locality_Sensitive_Hashing_Bio_Inspired_Hashing_September_8,_2021
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and reviews papers on bio-inspired hashing, specifically LSH inspired by fruit flies.
He first gives an overview of what algorithmic speedups are, why they are useful and how we can use them. He then dives into a specific technique called locality sensitive hashing (LSH) and goes over the motivations of using these types of hash algorithms and how they work. Lastly, Anshuman talks about the potential biological relevances of these hash mechanisms. He looks at the paper “A neural algorithm for a fundamental computing problem” which outlined a version of LSH inspired by fruit flies that uses sparse projections, expands dimensionality and uses a Winner-Takes-All mechanism.
Paper reviewed: “A Neural Algorithm for a Fundamental Computing Problem” by Dasgupta et al. : https://www.science.org/doi/abs/10.1126/science.aam9868
0:00 Overview
1:11 Algorithmic Speedups
14:28 Locality Sensitive Hashing
45:54 Bio-inspired Hashing
- - - - -
Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications.
Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence:
https://numenta.com/news-digest/
Subscribe to our Newsletter for the latest Numenta updates:
https://tinyurl.com/NumentaNewsletter
Our Social Media:
https://twitter.com/Numenta
https://www.facebook.com/OfficialNumenta
https://www.linkedin.com/company/numenta
Our Open Source Resources:
https://github.com/numenta
https://discourse.numenta.org/
Our Website:
https://numenta.com/
- published: 14 Sep 2021
- views: 1103
21:27
SISAP 2020: Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
- Order: Reorder
- Duration: 21:27
- Uploaded Date: 30 Oct 2020
- views: 49
Authors:
Omid Jafari, Parth Nagarkar and Jonathan Montano
Authors:
Omid Jafari, Parth Nagarkar and Jonathan Montano
https://wn.com/Sisap_2020_Improving_Locality_Sensitive_Hashing_By_Efficiently_Finding_Projected_Nearest_Neighbors
Authors:
Omid Jafari, Parth Nagarkar and Jonathan Montano
- published: 30 Oct 2020
- views: 49
5:34
BayLearn 2020: Batch Reinforcement Learning Through Continuation Method
- Order: Reorder
- Duration: 5:34
- Uploaded Date: 10 May 2021
- views: 73
- published: 10 May 2021
- views: 73
9:10
Deep Learning - 016 Employing indexing structures for efficient retrieval of semantic neighbors
- Order: Reorder
- Duration: 9:10
- Uploaded Date: 28 Apr 2021
- views: 306
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision te...
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.
The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.
https://wn.com/Deep_Learning_016_Employing_Indexing_Structures_For_Efficient_Retrieval_Of_Semantic_Neighbors
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.
The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.
- published: 28 Apr 2021
- views: 306
1:00
Central Similarity Quantization for Efficient Image and Video Retrieval
- Order: Reorder
- Duration: 1:00
- Uploaded Date: 17 Jul 2020
- views: 110
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng Description: Existing data-dependent hashing methods usually learn h...
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng Description: Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., \emph{hash center} that refers to a set of data points scattered in the Hamming space with a sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli distributions. Finally, we propose the Central Similarity Quantization (CSQ) that optimizes the central similarity between data points w.r.t.\ their hash centers instead of optimizing the local similarity. CSQ is generic and applicable to both image and video hashing scenarios. Extensive experiments on large-scale image and video retrieval tasks demonstrate that CSQ can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, achieving a noticeable boost in retrieval performance, i.e. 3\%-20\% in mAP over the previous state-of-the-arts.
https://wn.com/Central_Similarity_Quantization_For_Efficient_Image_And_Video_Retrieval
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng Description: Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., \emph{hash center} that refers to a set of data points scattered in the Hamming space with a sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli distributions. Finally, we propose the Central Similarity Quantization (CSQ) that optimizes the central similarity between data points w.r.t.\ their hash centers instead of optimizing the local similarity. CSQ is generic and applicable to both image and video hashing scenarios. Extensive experiments on large-scale image and video retrieval tasks demonstrate that CSQ can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, achieving a noticeable boost in retrieval performance, i.e. 3\%-20\% in mAP over the previous state-of-the-arts.
- published: 17 Jul 2020
- views: 110
3:11
Robust Property Preserving Hashes
- Order: Reorder
- Duration: 3:11
- Uploaded Date: 05 Oct 2018
- views: 316
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Session
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Session
https://wn.com/Robust_Property_Preserving_Hashes
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Session
- published: 05 Oct 2018
- views: 316
1:05:32
Alexandr Andoni - The Geometry of Similarity Search (May 19, 2017)
- Order: Reorder
- Duration: 1:05:32
- Uploaded Date: 06 Feb 2019
- views: 324
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
https://wn.com/Alexandr_Andoni_The_Geometry_Of_Similarity_Search_(May_19,_2017)
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
- published: 06 Feb 2019
- views: 324
8:51
Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]
- Order: Reorder
- Duration: 8:51
- Uploaded Date: 25 Sep 2017
- views: 2533
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (ht...
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
https://wn.com/Lecture_15.4_—_Semantic_Hashing_—_Deep_Learning_|_Geoffrey_Hinton_|_Uoft
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
- published: 25 Sep 2017
- views: 2533
4:59
BayLearn 2020: Hamming Space Locality Preserving Neural Hashing for Similarity Search
Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020 Post...
published: 10 May 2021
BayLearn 2020: Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020: Hamming Space Locality Preserving Neural Hashing for Similarity Search
- Report rights infringement
- published: 10 May 2021
- views: 338
Hamming Space Locality Preserving Neural Hashing for Similarity Search
BayLearn 2020 Poster #61
Presenter: Daphna Idelson
Abstract:
We propose a novel method for learning to map a large-scale dataset in the feature representation space to binary hash codes in the hamming space, for fast and efficient approximate nearest-neighbor similarity search. Our method is composed of a simple neural network and a novel training scheme, that aims to preserve the locality relations between the original data points. We achieve distance preservation of the original cosine space upon the new hamming space, by introducing a loss function that translates the relational similarities in both spaces to probability distributions - and optimizes the KL divergence between them. We also introduce a simple data sampling method by representing the database with randomly generated proxies, used as reference points to query points from the training set. Experimenting with three publicly available standard ANN benchmarks we demonstrate significant improvement over other binary hashing methods, achieving an improvement of between 7% to 17%. As opposed to other methods, we show high performance in both low (64 bits) and high (768 bits) dimensional bit representation, offering increased accuracy when resources are available and flexibility in choice of ANN strategy.
14:31
ICLR14: J Masci: Sparse similarity-preserving hashing
ICLR 2014 Talk: "Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, M...
published: 19 Apr 2014
ICLR14: J Masci: Sparse similarity-preserving hashing
ICLR14: J Masci: Sparse similarity-preserving hashing
- Report rights infringement
- published: 19 Apr 2014
- views: 549
ICLR 2014 Talk: "Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
Paper: http://openreview.net/document/69982ebb-b3e4-4685-9ca7-8b29a29b3a39
Slides: http://www.idsia.ch/~masci/slides/2014-ICLR-sparsehash.pdf
1:05:25
Algorithmic Speedups via Locality Sensitive Hashing & Bio-Inspired Hashing - September 8, 2021
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and review...
published: 14 Sep 2021
Algorithmic Speedups via Locality Sensitive Hashing & Bio-Inspired Hashing - September 8, 2021
Algorithmic Speedups via Locality Sensitive Hashing & Bio-Inspired Hashing - September 8, 2021
- Report rights infringement
- published: 14 Sep 2021
- views: 1103
Anshuman Mishra talks about algorithmic speedups via locality sensitive hashing and reviews papers on bio-inspired hashing, specifically LSH inspired by fruit flies.
He first gives an overview of what algorithmic speedups are, why they are useful and how we can use them. He then dives into a specific technique called locality sensitive hashing (LSH) and goes over the motivations of using these types of hash algorithms and how they work. Lastly, Anshuman talks about the potential biological relevances of these hash mechanisms. He looks at the paper “A neural algorithm for a fundamental computing problem” which outlined a version of LSH inspired by fruit flies that uses sparse projections, expands dimensionality and uses a Winner-Takes-All mechanism.
Paper reviewed: “A Neural Algorithm for a Fundamental Computing Problem” by Dasgupta et al. : https://www.science.org/doi/abs/10.1126/science.aam9868
0:00 Overview
1:11 Algorithmic Speedups
14:28 Locality Sensitive Hashing
45:54 Bio-inspired Hashing
- - - - -
Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications.
Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence:
https://numenta.com/news-digest/
Subscribe to our Newsletter for the latest Numenta updates:
https://tinyurl.com/NumentaNewsletter
Our Social Media:
https://twitter.com/Numenta
https://www.facebook.com/OfficialNumenta
https://www.linkedin.com/company/numenta
Our Open Source Resources:
https://github.com/numenta
https://discourse.numenta.org/
Our Website:
https://numenta.com/
21:27
SISAP 2020: Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
Authors:
Omid Jafari, Parth Nagarkar and Jonathan Montano
published: 30 Oct 2020
SISAP 2020: Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
SISAP 2020: Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
- Report rights infringement
- published: 30 Oct 2020
- views: 49
Authors:
Omid Jafari, Parth Nagarkar and Jonathan Montano
5:34
BayLearn 2020: Batch Reinforcement Learning Through Continuation Method
published: 10 May 2021
BayLearn 2020: Batch Reinforcement Learning Through Continuation Method
BayLearn 2020: Batch Reinforcement Learning Through Continuation Method
- Report rights infringement
- published: 10 May 2021
- views: 73
9:10
Deep Learning - 016 Employing indexing structures for efficient retrieval of semantic neighbors
Deep learning added a huge boost to the already rapidly developing field of computer visio...
published: 28 Apr 2021
Deep Learning - 016 Employing indexing structures for efficient retrieval of semantic neighbors
Deep Learning - 016 Employing indexing structures for efficient retrieval of semantic neighbors
- Report rights infringement
- published: 28 Apr 2021
- views: 306
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.
The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.
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Central Similarity Quantization for Efficient Image and Video Retrieval
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Fen...
published: 17 Jul 2020
Central Similarity Quantization for Efficient Image and Video Retrieval
Central Similarity Quantization for Efficient Image and Video Retrieval
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- published: 17 Jul 2020
- views: 110
Authors: Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng Description: Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., \emph{hash center} that refers to a set of data points scattered in the Hamming space with a sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli distributions. Finally, we propose the Central Similarity Quantization (CSQ) that optimizes the central similarity between data points w.r.t.\ their hash centers instead of optimizing the local similarity. CSQ is generic and applicable to both image and video hashing scenarios. Extensive experiments on large-scale image and video retrieval tasks demonstrate that CSQ can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, achieving a noticeable boost in retrieval performance, i.e. 3\%-20\% in mAP over the previous state-of-the-arts.
3:11
Robust Property Preserving Hashes
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Sessio...
published: 05 Oct 2018
Robust Property Preserving Hashes
Robust Property Preserving Hashes
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- published: 05 Oct 2018
- views: 316
Presentation by Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan at Crypto 2018 Rump Session
1:05:32
Alexandr Andoni - The Geometry of Similarity Search (May 19, 2017)
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
published: 06 Feb 2019
Alexandr Andoni - The Geometry of Similarity Search (May 19, 2017)
Alexandr Andoni - The Geometry of Similarity Search (May 19, 2017)
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- published: 06 Feb 2019
- views: 324
More details: https://www.simonsfoundation.org/event/the-geometry-of-similarity-search/
8:51
Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Langu...
published: 25 Sep 2017
Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]
Lecture 15.4 — Semantic Hashing — [ Deep Learning | Geoffrey Hinton | UofT ]
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- published: 25 Sep 2017
- views: 2533
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
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Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
Similarity search
Similarity Search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects.
Nearest neighbor search and Range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex.
The most general approach to similarity search that allows construction of efficient index structures use the mathematical notion of Metric space.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Similarity_search
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