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  1. AU="Cohen, Niv"
  2. AU="Kraft, Andrew D"
  3. AU="Joanne B. Cole"
  4. AU="Steiger Patrick"
  5. AU="Dashti, Kobra"
  6. AU="Henrik Szőke"

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  1. Article ; Online: Disentanglement of single-cell data with biolord.

    Piran, Zoe / Cohen, Niv / Hoshen, Yedid / Nitzan, Mor

    Nature biotechnology

    2024  

    Abstract: Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and ... ...

    Abstract Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.com/nitzanlab/biolord .
    Language English
    Publishing date 2024-01-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-023-02079-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  2. Article ; Online: Detecting anomalous proteins using deep representations.

    Michael-Pitschaze, Tomer / Cohen, Niv / Ofer, Dan / Hoshen, Yedid / Linial, Michal

    NAR genomics and bioinformatics

    2024  Volume 6, Issue 1, Page(s) lqae021

    Abstract: Many advances in biomedicine can be attributed to identifying unusual proteins and genes. Many of these proteins' unique properties were discovered by manual inspection, which is becoming infeasible at the scale of modern protein datasets. Here, we ... ...

    Abstract Many advances in biomedicine can be attributed to identifying unusual proteins and genes. Many of these proteins' unique properties were discovered by manual inspection, which is becoming infeasible at the scale of modern protein datasets. Here, we propose to tackle this challenge using anomaly detection methods that automatically identify unexpected properties. We adopt a state-of-the-art anomaly detection paradigm from computer vision, to highlight unusual proteins. We generate meaningful representations without labeled inputs, using pretrained deep neural network models. We apply these protein language models (pLM) to detect anomalies in function, phylogenetic families, and segmentation tasks. We compute protein anomaly scores to highlight human prion-like proteins, distinguish viral proteins from their host proteome, and mark non-classical ion/metal binding proteins and enzymes. Other tasks concern segmentation of protein sequences into folded and unstructured regions. We provide candidates for rare functionality (e.g. prion proteins). Additionally, we show the anomaly score is useful in 3D folding-related segmentation. Our novel method shows improved performance over strong baselines and has objectively high performance across a variety of tasks. We conclude that the combination of pLM and anomaly detection techniques is a valid method for discovering a range of global and local protein characteristics.
    Language English
    Publishing date 2024-02-27
    Publishing country England
    Document type Journal Article
    ISSN 2631-9268
    ISSN (online) 2631-9268
    DOI 10.1093/nargab/lqae021
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Set Features for Fine-grained Anomaly Detection

    Cohen, Niv / Tzachor, Issar / Hoshen, Yedid

    2023  

    Abstract: Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it ... ...

    Abstract Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements. We compute the anomaly score of each sample using a simple density estimation method. Our simple-to-implement approach outperforms the state-of-the-art in image-level logical anomaly detection (+3.4%) and sequence-level time-series anomaly detection (+2.4%).
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Publishing date 2023-02-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: No Free Lunch

    Reiss, Tal / Cohen, Niv / Hoshen, Yedid

    The Hazards of Over-Expressive Representations in Anomaly Detection

    2023  

    Abstract: Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our ... ...

    Abstract Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our networks, making their representations more expressive. In this paper, we provide theoretical and empirical evidence to the contrary. In fact, we empirically show cases where very expressive representations fail to detect even simple anomalies when evaluated beyond the well-studied object-centric datasets. To investigate this phenomenon, we begin by introducing a novel theoretical toy model for anomaly detection performance. The model uncovers a fundamental trade-off between representation sufficiency and over-expressivity. It provides evidence for a no-free-lunch theorem in anomaly detection stating that increasing representation expressivity will eventually result in performance degradation. Instead, guidance must be provided to focus the representation on the attributes relevant to the anomalies of interest. We conduct an extensive empirical investigation demonstrating that state-of-the-art representations often suffer from over-expressivity, failing to detect many types of anomalies. Our investigation demonstrates how this over-expressivity impairs image anomaly detection in practical settings. We conclude with future directions for mitigating this issue.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-06-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Set Features for Anomaly Detection

    Cohen, Niv / Tzachor, Issar / Hoshen, Yedid

    2023  

    Abstract: This paper proposes set features for detecting anomalies in samples that consist of unusual combinations of normal elements. Many leading methods discover anomalies by detecting an unusual part of a sample. For example, state-of-the-art segmentation- ... ...

    Abstract This paper proposes set features for detecting anomalies in samples that consist of unusual combinations of normal elements. Many leading methods discover anomalies by detecting an unusual part of a sample. For example, state-of-the-art segmentation-based approaches, first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend well to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution of its elements. We compute the anomaly score of each sample using a simple density estimation method, using fixed features. Our approach outperforms the previous state-of-the-art in image-level logical anomaly detection and sequence-level time series anomaly detection.

    Comment: arXiv admin note: substantial text overlap with arXiv:2302.12245
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: The Single-Noun Prior for Image Clustering

    Cohen, Niv / Hoshen, Yedid

    2021  

    Abstract: Self-supervised clustering methods have achieved increasing accuracy in recent years but do not yet perform as well as supervised classification methods. This contrasts with the situation for feature learning, where self-supervised features have recently ...

    Abstract Self-supervised clustering methods have achieved increasing accuracy in recent years but do not yet perform as well as supervised classification methods. This contrasts with the situation for feature learning, where self-supervised features have recently surpassed the performance of supervised features on several important tasks. We hypothesize that the performance gap is due to the difficulty of specifying, without supervision, which features correspond to class differences that are semantic to humans. To reduce the performance gap, we introduce the "single-noun" prior - which states that semantic clusters tend to correspond to concepts that humans label by a single-noun. By utilizing a pre-trained network that maps images and sentences into a common space, we impose this prior obtaining a constrained optimization task. We show that our formulation is a special case of the facility location problem, and introduce a simple-yet-effective approach for solving this optimization task at scale. We test our approach on several commonly reported image clustering datasets and obtain significant accuracy gains over the best existing approaches.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 006 ; 004
    Publishing date 2021-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Improving Zero-Shot Models with Label Distribution Priors

    Kahana, Jonathan / Cohen, Niv / Hoshen, Yedid

    2022  

    Abstract: Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate solution, but require manual labels which are often unavailable. Zero-shot ... ...

    Abstract Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate solution, but require manual labels which are often unavailable. Zero-shot models (e.g., CLIP) do not require manual labels but are not as accurate as supervised ones, particularly when the attribute is numeric. We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot models for regression and classification on unlabelled datasets. Our method does not use any annotated images. Instead, we assume a prior over the label distribution in the dataset. We then train an adapter network on top of CLIP under two competing objectives: i) minimal change of predictions from the original CLIP model ii) minimal distance between predicted and prior distribution of labels. Additionally, we present a novel approach for selecting prompts for Vision & Language models using a distributional prior. Our method is effective and presents a significant improvement over the original model. We demonstrate an improvement of 28% in mean absolute error on the UTK age regression task. We also present promising results for classification benchmarks, improving the classification accuracy on the ImageNet dataset by 2.83%, without using any labels.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-12-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Red PANDA

    Cohen, Niv / Kahana, Jonathan / Hoshen, Yedid

    Disambiguating Anomaly Detection by Removing Nuisance Factors

    2022  

    Abstract: Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.g., age, race or gender, may be considered anomalous by some operators ... ...

    Abstract Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.g., age, race or gender, may be considered anomalous by some operators while others may consider this attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection. Our approach then learns representations which do not contain information over the nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes that are relevant for detecting anomalies, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-07-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Sub-Image Anomaly Detection with Deep Pyramid Correspondences

    Cohen, Niv / Hoshen, Yedid

    2020  

    Abstract: Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the ... ...

    Abstract Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Publishing date 2020-05-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Chip-scale atomic wave-meter enabled by machine learning.

    Edrei, Eitan / Cohen, Niv / Gerstel, Elam / Gamzu-Letova, Shani / Mazurski, Noa / Levy, Uriel

    Science advances

    2022  Volume 8, Issue 15, Page(s) eabn3391

    Abstract: The quest for miniaturized optical wave-meters and spectrometers has accelerated the design of novel approaches in the field. Particularly, random spectrometers (RS) using the one-to-one correlation between the wavelength and an output random ... ...

    Abstract The quest for miniaturized optical wave-meters and spectrometers has accelerated the design of novel approaches in the field. Particularly, random spectrometers (RS) using the one-to-one correlation between the wavelength and an output random interference pattern emerged as a promising tool combining high spectral resolution and cost-effectiveness. Recently, a chip-scale platform for RS has been demonstrated with a markedly reduced footprint. Yet, despite the evident advantages of such modalities, they are very susceptible to environmental fluctuations and require an external calibration process. To address these challenges, we demonstrate a paradigm shift in the field, enabled by the integration of atomic vapor with a photonic chip and the use of a machine learning classification algorithm. Our approach provides a random wave-meter on chip device with accurate calibration and enhanced robustness against environmental fluctuations. The demonstrated device is expected to pave the way toward fully integrated spectrometers advancing the field of silicon photonics.
    Language English
    Publishing date 2022-04-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.abn3391
    Database MEDical Literature Analysis and Retrieval System OnLINE

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