home.social

#ise2024 — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #ise2024, aggregated by home.social.

  1. Knowledge-driven AI @[email protected] ·

    N-gram language models are quite simple and approximate the probability of a sequence of words in a language by applying the Bayes Rule for conditional probabilities, the Markov Assumption for simplifying complexity, and the Maximum Likelihood Estimation to approximate probabilities from frequency counts in a corpus.

    lecture slides: drive.google.com/file/d/1NkFex

    #nlp #llm #languagemodel #BayesTheorem #ise2024 #dh @fiz_karlsruhe @lysander07 @sourisnumerique @enorouzi @shufan

    #nlp #llm #languagemodel #bayestheorem #ise2024 #dh
  2. Knowledge-driven AI @[email protected] ·

    N-gram language models are quite simple and approximate the probability of a sequence of words in a language by applying the Bayes Rule for conditional probabilities, the Markov Assumption for simplifying complexity, and the Maximum Likelihood Estimation to approximate probabilities from frequency counts in a corpus.

    lecture slides: drive.google.com/file/d/1NkFex

    #nlp #llm #languagemodel #BayesTheorem #ise2024 #dh @fiz_karlsruhe @lysander07 @sourisnumerique @enorouzi @shufan

    #nlp #llm #languagemodel #bayestheorem #ise2024 #dh
  3. Knowledge-driven AI @[email protected] ·

    N-gram language models are quite simple and approximate the probability of a sequence of words in a language by applying the Bayes Rule for conditional probabilities, the Markov Assumption for simplifying complexity, and the Maximum Likelihood Estimation to approximate probabilities from frequency counts in a corpus.

    lecture slides: drive.google.com/file/d/1NkFex

    #nlp #llm #languagemodel #BayesTheorem #ise2024 #dh @fiz_karlsruhe @lysander07 @sourisnumerique @enorouzi @shufan

    #nlp #llm #languagemodel #bayestheorem #ise2024 #dh
  4. Knowledge-driven AI @[email protected] ·

    N-gram language models are quite simple and approximate the probability of a sequence of words in a language by applying the Bayes Rule for conditional probabilities, the Markov Assumption for simplifying complexity, and the Maximum Likelihood Estimation to approximate probabilities from frequency counts in a corpus.

    lecture slides: drive.google.com/file/d/1NkFex

    #nlp #llm #languagemodel #BayesTheorem #ise2024 #dh @fiz_karlsruhe @lysander07 @sourisnumerique @enorouzi @shufan

    #dh #ise2024 #bayestheorem #languagemodel #llm #nlp
  5. Knowledge-driven AI @[email protected] ·

    N-gram language models are quite simple and approximate the probability of a sequence of words in a language by applying the Bayes Rule for conditional probabilities, the Markov Assumption for simplifying complexity, and the Maximum Likelihood Estimation to approximate probabilities from frequency counts in a corpus.

    lecture slides: drive.google.com/file/d/1NkFex

    #nlp #llm #languagemodel #BayesTheorem #ise2024 #dh @fiz_karlsruhe @lysander07 @sourisnumerique @enorouzi @shufan

    #nlp #llm #languagemodel #bayestheorem #ise2024 #dh