Filters








168,196 Hits in 6.6 sec

Interpretability of deep learning models: A survey of results

Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani Srivastava, Alun Preece, Simon Julier, Raghuveer M. Rao, Troy D. Kelley, Dave Braines (+3 others)
2017 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)  
We call such models as interpretable deep networks. Interpretability is not a monolithic notion.  ...  In the process, we perform a gap analysis of what needs to be done to improve model interpretability.  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S.  ... 
doi:10.1109/uic-atc.2017.8397411 dblp:conf/uic/ChakrabortyTRHA17 fatcat:l7wmx67lujbmvofmtde4idgvoy

Automatic interpretation of otoliths using deep learning [article]

Endre Moen, Nils Olav Handegard, Vaneeda Shalini Devi Allken, Ole Thomas Albert, Alf Harbitz, Ketil malde
2018 biorxiv/medrxiv   pre-print
Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images.  ...  The model is trained and validated on a large collection of images of Greenland halibut otoliths.  ...  .0204713.t001 Automatic interpretation of otoliths using deep learning Table 2 . 2 MSE (Eq 1) and mean CV (Eq 3) for predictions.  ... 
doi:10.1101/418285 fatcat:23isptpeizbxzjsdbodhe72a5u

Automatic interpretation of otoliths using deep learning

Endre Moen, Nils Olav Handegard, Vaneeda Allken, Ole Thomas Albert, Alf Harbitz, Ketil Malde, Heather M. Patterson
2018 PLoS ONE  
Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images.  ...  The model is trained and validated on a large collection of images of Greenland halibut otoliths.  ...  .0204713.t001 Automatic interpretation of otoliths using deep learning Table 2 . 2 MSE (Eq 1) and mean CV (Eq 3) for predictions.  ... 
doi:10.1371/journal.pone.0204713 pmid:30557335 pmcid:PMC6296523 fatcat:rxvwxmqglbedja4xyetzhixmx4

Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning [article]

Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau
2020 arXiv   pre-print
Massif provides both a high-level, interpretable overview of the effect of an attack on a DNN, and a low-level, detailed description of the affected neurons.  ...  Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such applications.  ...  We will recruit students with basic knowledge of deep learning models.  ... 
arXiv:2001.07769v3 fatcat:bmhhiaktknapldz4pkfmfuxcju

Interpretation of deep learning in genomics and epigenomics

Amlan Talukder, Clayton Barham, Xiaoman Li, Haiyan Hu
2020 Briefings in Bioinformatics  
We first describe state-of-the-art DNN interpretation methods in representative machine learning fields.  ...  We also present the biological discoveries that resulted from these interpretation methods.  ...  Conf lict of Interest There is no conf lict of interest declared.  ... 
doi:10.1093/bib/bbaa177 pmid:34020542 pmcid:PMC8138893 fatcat:4xlkzvvalrcipc2fcawu4kzigi

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions [article]

Ankit Ramchandani, Chao Fan, Ali Mostafavi
2020 arXiv   pre-print
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate  ...  The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies.  ...  Any opinions, findings, conclusions and/or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, Amazon Web Services  ... 
arXiv:2008.00115v1 fatcat:rvtxbdyxn5cydpihlzqamumymy

Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders

Tamás Grósz, Mikko Kurimo
2020 Workshop on Machine Learning Methods in Visualisation for Big Data  
In this work, we concentrate on visual interpretation by depicting the hidden activation vectors of the DNN, and propose the usage of deep Autoencoders (DAE) to transform these hidden representations for  ...  The main advantage of using Autoencoders over the existing ones is that after the training phase, it applies a fixed transformation that can be used to visualize any hidden activation vector without any  ...  what the model has learned.  ... 
doi:10.2312/mlvis.20201103 dblp:conf/mlvis-ws/GroszK20 fatcat:ptnbaze5dfethghoqdebf6nbjm

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

Ankit Ramchandani, Chao Fan, Ali Mostafavi
2020 IEEE Access  
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate  ...  The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies.  ...  Any opinions, findings, conclusions and/or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, Amazon Web Services  ... 
doi:10.1109/access.2020.3019989 pmid:34786287 pmcid:PMC8545302 fatcat:3iwzjkrhxjeadfnsqwsofmkt4a

Application and interpretation of deep learning for identifying pre-emergence magnetic-field patterns [article]

Dattaraj B. Dhuri, Shravan M. Hanasoge, Aaron C. Birch, Hannah Schunker
2020 arXiv   pre-print
Given increasing popularity of deep learning, techniques developed here for interpretation of the trained CNN -- using network pruning and synthetic data -- are relevant for future applications in solar  ...  Our results are better than a baseline classification TSS obtained using discriminant analysis of only the unsigned magnetic flux.  ...  The filter outputs are a result of complex operations performed by the CNN in the hidden layers and may not be easily interpreted. Table 5 .  ... 
arXiv:2009.06287v1 fatcat:rb24atva5vhubcwscolthvisaq

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

Domjan Barić, Petar Fumić, Davor Horvatić, Tomislav Lipic
2021 Entropy  
The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions.  ...  This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/e23020143 pmid:33503822 fatcat:oug65tfkcjgqbhv42fcx7mjxsi

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond [article]

Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller
2020 arXiv   pre-print
Interpretability and explanation methods for gaining a better understanding about the problem solving abilities and strategies of nonlinear Machine Learning such as Deep Learning (DL), LSTMs, and kernel  ...  In this work we aim to (1) provide a timely overview of this active emerging field and explain its theoretical foundations, (2) put interpretability algorithms to a test both from a theory and comparative  ...  the learning process of a deep model, in particular the emergence of novel prediction strategies during training.  ... 
arXiv:2003.07631v1 fatcat:pvjjzqns2bdtxlvganye4yipey

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Tudor Florin Ursuleanu, Andreea Roxana Luca, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici
2021 Diagnostics  
Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image.  ...  All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a  ...  /Reconstruction), (Deep Learning and Images and Applications in Medicine), (Deep Learning and Interpretation Medical Images).Figure 1shows our search structure of the survey paper.  ... 
doi:10.3390/diagnostics11081373 fatcat:6p7usnvnxnewtivzeth745s3ga

Automated stellar classification for large surveys: a review of methods and results [article]

C.A.L. Bailer-Jones
2001 arXiv   pre-print
I finish with a brief look at the developments still required in order to apply a stellar classifier to a large survey.  ...  Current and future large astronomical surveys will yield multiparameter databases on millions or even billions of objects.  ...  Interpretability A final issue in comparing these models is their interpretability.  ... 
arXiv:astro-ph/0102223v1 fatcat:czbgxgmcqvbjbnlzqhmvig2hki

Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results [article]

Pier Luigi Ventre, Stefano Salsano, Marco Polverini, Antonio Cianfrani, Ahmed Abdelsalam, Clarence Filsfils, Pablo Camarillo, Francois Clad
2020 arXiv   pre-print
In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results.  ...  of billions of devices and millions of services in the cloud).  ...  As part of our survey activity we have also reported our vision and our experience in terms of lessons learned and future research topics.  ... 
arXiv:1904.03471v5 fatcat:2qofywgo7rcwrblltqog4fyzm4

Arithmetic Circuits: A survey of recent results and open questions

Amir Shpilka, Amir Yehudayoff
2009 Foundations and Trends® in Theoretical Computer Science  
Nevertheless, there has been a lot of progress in the area and beautiful results have been found, some in the last few years.  ...  This algebraic model of computation attracted a large amount of research in the last five decades, partially due to its simplicity and elegance.  ...  Another model that was considered in Ref. [SV09] is that of small depth read-once formulas.  ... 
doi:10.1561/0400000039 fatcat:vejtujygx5ddjkm2crbxh2udcq
« Previous Showing results 1 — 15 out of 168,196 results