Discriminative vs. Generative - Revision history

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

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BPeat at 02:45, 6 March 2024

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BPeat at 19:06, 2 September 2023

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BPeat at 17:05, 10 July 2023

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

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

BPeat at 01:58, 25 March 2023

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@@ Line 20: / Line 20: @@
 * [[Feature Exploration/Learning]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Model Contrasts...

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Generative+Discriminative+Modeling ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Feature Exploration/Learning]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

@@ Line 19: / Line 19: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 * [[Feature Exploration/Learning]]
-* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Model Contrasts...

@@ 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=Generative+Discriminative+Modeling YouTube search...]
 [https://www.google.com/search?q=Generative+Discriminative+Modeling ...Google search]
-* [[Algorithms]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 * [[Feature Exploration/Learning]]
 * [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]

@@ Line 10: / Line 10: @@
 * [[Algorithms]]
 * [[Feature Exploration/Learning]]
-* [[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]]
 Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Model Contrasts...

@@ 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=Generative+Discriminative+Modeling YouTube search...]
+[https://www.youtube.com/results?search_query=Generative+Discriminative+Modeling YouTube search...]
-[http://www.google.com/search?q=Generative+Discriminative+Modeling ...Google search]
+[https://www.google.com/search?q=Generative+Discriminative+Modeling ...Google search]
 * [[Algorithms]]
@@ Line 14: / Line 14: @@
 Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Model Contrasts...
-* [http://en.wikipedia.org/wiki/Discriminative_model Discriminative]
+* [https://en.wikipedia.org/wiki/Discriminative_model Discriminative]
 ** learn the (hard or soft) boundary between classes
 ** providing classification splits (probabilistic or non-probabilistic manner)
@@ Line 61: / Line 61: @@
 machine learning classification and [[Regression]] analysis that do not require the joint distribution, discriminative models can yield superior performance (in part because they have fewer variables to compute). On the other hand, generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks.  In addition, most discriminative models are inherently supervised and cannot easily support unsupervised learning. Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model.
-Discriminative models and [[Generative]] models also differ in introducing the Posterior probability. To maintain the least expected loss, the minimization of result's misclassification should be acquired. In the discriminative model, the [http://en.wikipedia.org/wiki/Posterior_probability posterior probabilities], P(y|x), is inferred from a parametric model, where the parameters come from the training data. Points of estimation of the parameters are obtained from the maximization of likelihood or distribution computation over the parameters. On the other hand, considering that the generative models focus on the joint probability, the class posterior possibility P(k) is considered in [[Bayes#Bayes' Theorem|Bayes' Theorem]].  [http://en.wikipedia.org/wiki/Discriminative_model Discriminative Modeling - Wikipedia]
+Discriminative models and [[Generative]] models also differ in introducing the Posterior probability. To maintain the least expected loss, the minimization of result's misclassification should be acquired. In the discriminative model, the [https://en.wikipedia.org/wiki/Posterior_probability posterior probabilities], P(y|x), is inferred from a parametric model, where the parameters come from the training data. Points of estimation of the parameters are obtained from the maximization of likelihood or distribution computation over the parameters. On the other hand, considering that the generative models focus on the joint probability, the class posterior possibility P(k) is considered in [[Bayes#Bayes' Theorem|Bayes' Theorem]].  [https://en.wikipedia.org/wiki/Discriminative_model Discriminative Modeling - Wikipedia]
-<img src="http://duphan.files.wordpress.com/2016/09/discriminative_vs_generative.png" width="800">
+<img src="https://duphan.files.wordpress.com/2016/09/discriminative_vs_generative.png" width="800">
@@ Line 73: / Line 73: @@
 = <span id="Snorkel"></span>Snorkel =
-* [http://mc.ai/the-cold-start-problem-with-stanford-snorkel/ Stanford Snorkel - MC.ai]   ...a [[Python]] library to help you label data for supervised machine learning tasks
+* [https://mc.ai/the-cold-start-problem-with-stanford-snorkel/ Stanford Snorkel - MC.ai]   ...a [[Python]] library to help you label data for supervised machine learning tasks
-* [http://www.snorkel.org/ snorkel.org]
+* [https://www.snorkel.org/ snorkel.org]
-* [http://dawn.cs.stanford.edu/2017/05/08/snorkel/ Snorkel and The Dawn of Weakly Supervised Machine Learning | A. Ratner, S. Bach, H. Ehrenberg, and C. Ré Stanford DAWN]
+* [https://dawn.cs.stanford.edu/2017/05/08/snorkel/ Snorkel and The Dawn of Weakly Supervised Machine Learning | A. Ratner, S. Bach, H. Ehrenberg, and C. Ré Stanford DAWN]
-* [http://www.snorkel.ai/ Snorkel Flow | Snorkel]  ...serve as a replacement of armies of human labelers which at the moment do it by hand
+* [https://www.snorkel.ai/ Snorkel Flow | Snorkel]  ...serve as a replacement of armies of human labelers which at the moment do it by hand
-** [http://www.forbes.com/sites/igorbosilkovski/2020/07/14/meet-the-stanford-ai-lab-alums-that-raised-15-million-to-optimize-machine-learning/#3d4065ea42ee Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning | Igor Bosilkovski - Forbes]
+** [https://www.forbes.com/sites/igorbosilkovski/2020/07/14/meet-the-stanford-ai-lab-alums-that-raised-15-million-to-optimize-machine-learning/#3d4065ea42ee Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning | Igor Bosilkovski - Forbes]
 Snorkel system is noteworthy here and has been gaining a lot of traction. As part of DAWN project, Snorkel enables users to train models without hand labeling any training data. The users define labeling functions using arbitrary heuristics. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of their recently proposed machine learning paradigm, data programming.  In a Snorkel system
 Subject Matter Experts write labeling functions (LFs) that express weak supervision sources like distant supervision, patterns, and heuristics.
 Snorkel applies the LFs over unlabeled data and learns a generative model to combine the LFs’ outputs into probabilistic labels.
-Snorkel uses these labels to train a discriminative classification model, such as a deep neural network. [http://medium.com/hivedata/ground-truth-gold-intelligent-data-labeling-and-annotation-632f63d9662f Ground Truth Gold — Intelligent data labeling and annotation | Mohan Reddy - The Hive - Medium]
+Snorkel uses these labels to train a discriminative classification model, such as a deep neural network. [https://medium.com/hivedata/ground-truth-gold-intelligent-data-labeling-and-annotation-632f63d9662f Ground Truth Gold — Intelligent data labeling and annotation | Mohan Reddy - The Hive - Medium]
-http://miro.medium.com/max/1000/1*_zEVGeb9fmlFptdt9dfJ8Q.png
+https://miro.medium.com/max/1000/1*_zEVGeb9fmlFptdt9dfJ8Q.png
 {|<!-- T -->
@@ Line 92: / Line 92: @@
 <youtube>c_ZTjK5TaD0</youtube>
 <b>XLDB-2019: Snorkel - A System for Training Set Modeling
-</b><br>Speaker: Chris Ré, Stanford University Session: Social Implications of Extreme Scale Analytical Systems  XLDB-2019 website: http://conf.slac.stanford.edu/xldb2019/
+</b><br>Speaker: Chris Ré, Stanford University Session: Social Implications of Extreme Scale Analytical Systems  XLDB-2019 website: https://conf.slac.stanford.edu/xldb2019/
 |}
 |<!-- M -->
@@ Line 101: / Line 101: @@
 <b>Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
 </b><br>Paroma Varma is a PHD student at Stanford University working on Machine Learning, particularly on the project Snorkel, a system to program training data efficiently using weak supervision. The idea is to use noisy sources of signal and higher levels of abstraction to rapidly generate training labels.  This lecture was a part of the Applied Deep Learning Fellowship held at the Weights and Biases Headquarters in the spring of 2019.
-http://www.paroma.xyz/
+https://www.paroma.xyz/
 |}
 |}<!-- B -->

@@ Line 32: / Line 32: @@
-* [[Generative]]
+* [[Generative AI]]
 ** requires less training samples
 ** model the distribution of individual classes