The capacity of the dense associative memory networks
This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bao, Han [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
11 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:469 ; year:2022 ; day:16 ; month:01 ; pages:198-208 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2021.10.058 |
|---|
| Katalog-ID: |
ELV056028806 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV056028806 | ||
| 003 | DE-627 | ||
| 005 | 20230626042559.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220105s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.neucom.2021.10.058 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica |
| 035 | |a (DE-627)ELV056028806 | ||
| 035 | |a (ELSEVIER)S0925-2312(21)01544-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 35.70 |2 bkl | ||
| 084 | |a 42.12 |2 bkl | ||
| 100 | 1 | |a Bao, Han |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The capacity of the dense associative memory networks |
| 264 | 1 | |c 2022transfer abstract | |
| 300 | |a 11 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. | ||
| 520 | |a This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. | ||
| 650 | 7 | |a Hopfield network |2 Elsevier | |
| 650 | 7 | |a DAM networks |2 Elsevier | |
| 650 | 7 | |a Capacity |2 Elsevier | |
| 650 | 7 | |a Noise recovery |2 Elsevier | |
| 700 | 1 | |a Zhang, Richong |4 oth | |
| 700 | 1 | |a Mao, Yongyi |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
| 773 | 1 | 8 | |g volume:469 |g year:2022 |g day:16 |g month:01 |g pages:198-208 |g extent:11 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.neucom.2021.10.058 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
| 936 | b | k | |a 42.12 |j Biophysik |q VZ |
| 951 | |a AR | ||
| 952 | |d 469 |j 2022 |b 16 |c 0116 |h 198-208 |g 11 | ||
| author_variant |
h b hb |
|---|---|
| matchkey_str |
baohanzhangrichongmaoyongyi:2022----:hcpctoteesascaie |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
35.70 42.12 |
| publishDate |
2022 |
| allfields |
10.1016/j.neucom.2021.10.058 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica (DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Bao, Han verfasserin aut The capacity of the dense associative memory networks 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier Zhang, Richong oth Mao, Yongyi oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 https://doi.org/10.1016/j.neucom.2021.10.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 469 2022 16 0116 198-208 11 |
| spelling |
10.1016/j.neucom.2021.10.058 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica (DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Bao, Han verfasserin aut The capacity of the dense associative memory networks 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier Zhang, Richong oth Mao, Yongyi oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 https://doi.org/10.1016/j.neucom.2021.10.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 469 2022 16 0116 198-208 11 |
| allfields_unstemmed |
10.1016/j.neucom.2021.10.058 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica (DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Bao, Han verfasserin aut The capacity of the dense associative memory networks 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier Zhang, Richong oth Mao, Yongyi oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 https://doi.org/10.1016/j.neucom.2021.10.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 469 2022 16 0116 198-208 11 |
| allfieldsGer |
10.1016/j.neucom.2021.10.058 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica (DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Bao, Han verfasserin aut The capacity of the dense associative memory networks 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier Zhang, Richong oth Mao, Yongyi oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 https://doi.org/10.1016/j.neucom.2021.10.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 469 2022 16 0116 198-208 11 |
| allfieldsSound |
10.1016/j.neucom.2021.10.058 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica (DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Bao, Han verfasserin aut The capacity of the dense associative memory networks 2022transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier Zhang, Richong oth Mao, Yongyi oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 https://doi.org/10.1016/j.neucom.2021.10.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 469 2022 16 0116 198-208 11 |
| language |
English |
| source |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 |
| sourceStr |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:469 year:2022 day:16 month:01 pages:198-208 extent:11 |
| format_phy_str_mv |
Article |
| bklname |
Biochemie: Allgemeines Biophysik |
| institution |
findex.gbv.de |
| topic_facet |
Hopfield network DAM networks Capacity Noise recovery |
| dewey-raw |
570 |
| isfreeaccess_bool |
false |
| container_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| authorswithroles_txt_mv |
Bao, Han @@aut@@ Zhang, Richong @@oth@@ Mao, Yongyi @@oth@@ |
| publishDateDaySort_date |
2022-01-16T00:00:00Z |
| hierarchy_top_id |
ELV002603926 |
| dewey-sort |
3570 |
| id |
ELV056028806 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056028806</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626042559.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2021.10.058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056028806</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(21)01544-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bao, Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The capacity of the dense associative memory networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hopfield network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DAM networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Capacity</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Noise recovery</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Richong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mao, Yongyi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:469</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:16</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:198-208</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2021.10.058</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">469</subfield><subfield code="j">2022</subfield><subfield code="b">16</subfield><subfield code="c">0116</subfield><subfield code="h">198-208</subfield><subfield code="g">11</subfield></datafield></record></collection>
|
| author |
Bao, Han |
| spellingShingle |
Bao, Han ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery The capacity of the dense associative memory networks |
| authorStr |
Bao, Han |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV002603926 |
| format |
electronic Article |
| dewey-ones |
570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl The capacity of the dense associative memory networks Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery Elsevier |
| topic |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery |
| topic_unstemmed |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery |
| topic_browse |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Hopfield network Elsevier DAM networks Elsevier Capacity Elsevier Noise recovery |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
r z rz y m ym |
| hierarchy_parent_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| hierarchy_parent_id |
ELV002603926 |
| dewey-tens |
570 - Life sciences; biology |
| hierarchy_top_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV002603926 |
| title |
The capacity of the dense associative memory networks |
| ctrlnum |
(DE-627)ELV056028806 (ELSEVIER)S0925-2312(21)01544-7 |
| title_full |
The capacity of the dense associative memory networks |
| author_sort |
Bao, Han |
| journal |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| journalStr |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
198 |
| author_browse |
Bao, Han |
| container_volume |
469 |
| physical |
11 |
| class |
570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Bao, Han |
| doi_str_mv |
10.1016/j.neucom.2021.10.058 |
| dewey-full |
570 |
| title_sort |
capacity of the dense associative memory networks |
| title_auth |
The capacity of the dense associative memory networks |
| abstract |
This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. |
| abstractGer |
This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. |
| abstract_unstemmed |
This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
The capacity of the dense associative memory networks |
| url |
https://doi.org/10.1016/j.neucom.2021.10.058 |
| remote_bool |
true |
| author2 |
Zhang, Richong Mao, Yongyi |
| author2Str |
Zhang, Richong Mao, Yongyi |
| ppnlink |
ELV002603926 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.neucom.2021.10.058 |
| up_date |
2024-07-06T19:13:35.729Z |
| _version_ |
1803858178977300480 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056028806</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626042559.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2021.10.058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001638.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056028806</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(21)01544-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bao, Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The capacity of the dense associative memory networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper revisits the dense associative memory (DAM) networks and studies rigorously the capacity of the DAM networks. We present the capacity theorem of the DAM networks with an attraction radius or a noise level from the messages and prove that the probe can converge to the targeted message just after the one-step update. Under this convergence, the capacity of DAM networks is between a lower bound and an upper bound. Although when the attraction radius is 0.0 away from the messages, i.e. noiseless, previous literature provides an approximate result. However, a rigorous proof is not given in this study. In addition, we consider a more general notion of capacity which allows the retrieval of messages from noisy probes (the attraction radius is not 0.0 ). We demonstrates that the convergence result can be acquired just after the one-step update when the probe is a corrupted version with a Gaussian noise from one message. We further provide simulated experiments to validate theorems herein.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hopfield network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DAM networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Capacity</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Noise recovery</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Richong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mao, Yongyi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:469</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:16</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:198-208</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2021.10.058</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">469</subfield><subfield code="j">2022</subfield><subfield code="b">16</subfield><subfield code="c">0116</subfield><subfield code="h">198-208</subfield><subfield code="g">11</subfield></datafield></record></collection>
|
| score |
7.400646 |