Difference: InterOpNov2021KD (1 vs. 8)

Revision 82023-04-26 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Changed:
<
<

Raffaele D'Abrusco

Introduction

5' pdf

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
>
>
Raffaele D'Abrusco Introduction 5' pdf
Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
 
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' pdf
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' pdf
Changed:
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<
>
>
Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link
Deleted:
<
<
Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link
 
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Revision 72021-11-04 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Changed:
<
<

Raffaele D'Abrusco

Introduction

5' pdf

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
>
>

Raffaele D'Abrusco

Introduction

5' pdf

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
 
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' pdf
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' pdf

Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link

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Added:
>
>
META FILEATTACHMENT attachment="slides_session_KDIG_IVOA_2021FallInterOp.pdf" attr="" comment="" date="1636049545" name="slides_session_KDIG_IVOA_2021FallInterOp.pdf" path="slides_session_KDIG_IVOA_2021FallInterOp.pdf" size="197990" user="RaffaeleDAbrusco" version="1"
 

Revision 62021-11-03 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Raffaele D'Abrusco

Introduction

5' pdf
Changed:
<
<

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' pdf
>
>

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' pdf
 
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' pdf

Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link

META FILEATTACHMENT attachment="KD-IG_anomalies.pdf" attr="" comment="" date="1635945677" name="KD-IG_anomalies.pdf" path="KD-IG_anomalies.pdf" size="35575806" user="RaffaeleDAbrusco" version="1"
Added:
>
>
META FILEATTACHMENT attachment="skoda-bayesian-redshift.pdf" attr="" comment="" date="1635970467" name="skoda-bayesian-redshift.pdf" path="skoda-bayesian-redshift.pdf" size="1855828" user="RaffaeleDAbrusco" version="1"
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Revision 52021-11-03 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Changed:
<
<
Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' TBD
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' TBD
>
>

Raffaele D'Abrusco

Introduction

5' pdf

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' pdf
Added:
>
>
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' pdf
 
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' pdf

Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link

META FILEATTACHMENT attachment="KD-IG_anomalies.pdf" attr="" comment="" date="1635945677" name="KD-IG_anomalies.pdf" path="KD-IG_anomalies.pdf" size="35575806" user="RaffaeleDAbrusco" version="1"

Revision 42021-11-03 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' TBD
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' TBD
Changed:
<
<
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' TBD
>
>
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' pdf
  Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link
Added:
>
>
META FILEATTACHMENT attachment="KD-IG_anomalies.pdf" attr="" comment="" date="1635945677" name="KD-IG_anomalies.pdf" path="KD-IG_anomalies.pdf" size="35575806" user="RaffaeleDAbrusco" version="1"
 

Revision 32021-10-27 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Changed:
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<
Time: Wednesday Nov 03 22:00 UTC
>
>
Time: Wednesday Nov 03 22:00 UTC
 
Changed:
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<
Speaker(s) Title and Abstract Time Material
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' TBD
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

7' TBD
>
>
Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).

7' TBD
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

Bayesian deep learning is a relatively new approach that starts to enter the astronomy. Unlike majority of the current methods it does provide the uncertainty of its predictions. So we can visually check the suspicious cases with high uncertainty. We demonstrate this in the experiment with spectroscopic redshift prediction from SDSS quasar catalogues .This allowed us to find a number of quasars which are probably normal stars with wrong estimate of redshift from the SDSS pipeline.

7' TBD
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' TBD
Deleted:
<
<
Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).


7' TBD
 
Changed:
<
<
Moderator: TBD, Notetaker: TBD, Etherpad link
>
>
Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link
 

Revision 22021-10-26 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Speaker(s) Title and Abstract Time Material
Changed:
<
<
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective
at identifying anomalous objects in large astronomical datasets. So
far, that has meant "finding objects in sparsely populated regions of
a multidimensional feature space". This is done using a number of
methods that includes ensemble methods such as random forests
searches, and more recently generative models that identify anomalies
as those objects are more difficult to reconstruct by the trained
model. This has produced huge lists of anomalies in diverse datasets
that include SDSS galaxy spectra, Kepler and TESS light curves, and
X-ray catalogs. Yet, most of those anomalies are not followed up,
because of a cultural difficulty for scientists to interpret
multi-dimensional scatter plots that have no labels in their axes. We
argue that such cultural barrier can be overcome with novel ways to
combine domain knowledge expertise with data visualization, or even
incorporating domain knowledge directly into the anomaly detection
algorithms. We would like to discuss ways in which VO tools can help
in the identification of anomalies that represent true astronomical
discoveries, by harvesting the currently publicly available catalogs
of anomalies.

7' TBD
>
>
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.

7' TBD
 
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

7' TBD
Changed:
<
<
Ashish Mahabal

7' TBD
>
>
Ashish Mahabal

Data Sheets and Model Cards

Astronomy datasets have been growing, and so are the attempts to use them wth a variety of machine learning techniques. While we would like to use all data, data fusion for diverse uneven or not-fully-matched datasets can be a challenge. Creating machine learning and artificial intelligence models for such datasets and follow-up validation can be challenging owing to lack of large labeled training datasets. To address this two related concepts that have emerged recently in data science are that of Data Sheets for data sets (Gebru et al. arXiv:1803.09010), and model cards for models (Mitchell et al., arXiv:1810.03993). This is just like each component in the electronics industry comes with a datasheet that describes operating characteristics, test results, recommended use etc., We recommend that for each astronomy dataset uniform and standardized datasheets that advertise similar meta-properties should be created, not just stating what “is” but also where each of the dataset could go, much like lego-blocks. This will enable data fusion, and also thwart mis-guided use of datasets. Similarly the models that we build will carry not just the usual provenance, but explicit characteristics displaying known biases and hence added caution when being used in certain ways. While this trend started in social fields where bias is explicit, it has been successfully applied in the Planetary Data System (PDS) setup for identifying key descriptors in an equally diverse dataspace (https://pds.nasa.gov/datastandards/documents/im/v1/index_1G00.html#10.31%C2%A0%C2%A0class_pds_observation_area).


7' TBD
  Moderator: TBD, Notetaker: TBD, Etherpad link

Revision 12021-10-25 - RaffaeleDAbrusco

 
META TOPICPARENT name="InterOpNov2021"

Knowledge Discovery 1

Time: Wednesday Nov 03 22:00 UTC

Speaker(s) Title and Abstract Time Material
Rafael Martinez Galarza

Harvesting outliers: data barriers to turn anomalies into discoveries

Over the last few years astronomers have become increasingly effective
at identifying anomalous objects in large astronomical datasets. So
far, that has meant "finding objects in sparsely populated regions of
a multidimensional feature space". This is done using a number of
methods that includes ensemble methods such as random forests
searches, and more recently generative models that identify anomalies
as those objects are more difficult to reconstruct by the trained
model. This has produced huge lists of anomalies in diverse datasets
that include SDSS galaxy spectra, Kepler and TESS light curves, and
X-ray catalogs. Yet, most of those anomalies are not followed up,
because of a cultural difficulty for scientists to interpret
multi-dimensional scatter plots that have no labels in their axes. We
argue that such cultural barrier can be overcome with novel ways to
combine domain knowledge expertise with data visualization, or even
incorporating domain knowledge directly into the anomaly detection
algorithms. We would like to discuss ways in which VO tools can help
in the identification of anomalies that represent true astronomical
discoveries, by harvesting the currently publicly available catalogs
of anomalies.

7' TBD
Petr Skoda

SDSS redshift prediction based on Bayesian Deep Learning

7' TBD
Ashish Mahabal

7' TBD

Moderator: TBD, Notetaker: TBD, Etherpad link

 
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