Conditional Adversarial Architecture (CAA) - Revision history

BPeat at 19:49, 16 March 2024

2024-03-16T19:49:02Z

BPeat at 10:03, 13 July 2023

2023-07-13T10:03:34Z

BPeat at 16:47, 15 April 2023

2023-04-15T16:47:09Z

BPeat: Text replacement - "BingAI" to "Bing"

2023-03-31T01:25:14Z

Text replacement - "BingAI" to "Bing"

BPeat: Text replacement - "http:" to "https:"

2023-03-28T10:13:20Z

Text replacement - "http:" to "https:"

BPeat at 02:07, 25 March 2023

2023-03-25T02:07:47Z

BPeat at 20:53, 8 March 2023

2023-03-08T20:53:38Z

BPeat at 22:30, 3 March 2023

2023-03-03T22:30:04Z

BPeat at 13:05, 3 October 2020

2020-10-03T13:05:37Z

BPeat at 13:04, 3 October 2020

2020-10-03T13:04:57Z

@@ Line 21: / Line 21: @@
 * [https://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]
 * [https://www.ncbi.nlm.nih.gov/pubmed/30180591 Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A]
-* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [[Architectures]] supporting AI

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Conditional+Adversarial+Autoencoder+ai+deep+learning+teacher+student YouTube search...]
 [https://www.google.com/search?q=Conditional+Adversarial+Autoencoder+ai+deep+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Occlusions]]
+* [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]]
 * [https://blog.acolyer.org/2018/05/08/image-to-image-translation-with-conditional-adversarial-networks/ Image-to-image translation with conditional adversarial networks | Isola et al.]
 * [https://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]

@@ Line 12: / Line 12: @@
 * [https://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]
 * [https://www.ncbi.nlm.nih.gov/pubmed/30180591 Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A]
-* [[Generative AI]]  ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 * [[Architectures]] supporting AI

@@ Line 12: / Line 12: @@
 * [https://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]
 * [https://www.ncbi.nlm.nih.gov/pubmed/30180591 Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A]
-* [[Generative AI]]  ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[Generative AI]]  ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 * [[Architectures]] supporting AI

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Conditional+Adversarial+Autoencoder+ai+deep+learning+teacher+student YouTube search...]
+[https://www.youtube.com/results?search_query=Conditional+Adversarial+Autoencoder+ai+deep+learning+teacher+student YouTube search...]
-[http://www.google.com/search?q=Conditional+Adversarial+Autoencoder+ai+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Conditional+Adversarial+Autoencoder+ai+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Occlusions]]
-* [http://blog.acolyer.org/2018/05/08/image-to-image-translation-with-conditional-adversarial-networks/ Image-to-image translation with conditional adversarial networks | Isola et al.]
+* [https://blog.acolyer.org/2018/05/08/image-to-image-translation-with-conditional-adversarial-networks/ Image-to-image translation with conditional adversarial networks | Isola et al.]
-* [http://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]
+* [https://sleep.csail.mit.edu/ Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital]
-* [http://www.ncbi.nlm.nih.gov/pubmed/30180591 Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A]
+* [https://www.ncbi.nlm.nih.gov/pubmed/30180591 Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A]
 * [[Generative AI]]  ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 * [[Architectures]] supporting AI

@@ Line 14: / Line 14: @@
 * [[Generative]] Modeling
 * [[Architectures]] supporting AI
 https://www.researchgate.net/profile/Mingmin_Zhao3/publication/328049475/figure/fig2/AS:677681750351875@1538583328633/Our-teacher-student-network-model-used-in-RF-Pose-The-upper-pipeline-provides-training.png