RESEARCH

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08 July 2023 22h05

One relatively good one is https://
arxiv.org/abs/2212.08073. I don’t find it that speculative tbh.

→ View original post on X — @drhassanrashidi

Measuring AI Systems with Proper Evaluation Metrics

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@drhassanrashidi

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08 July 2023 21h05

Life becomes much better if you measure things with their proper measuring tools.

The Food Chain of AI, Machine Learning, and Computer Science

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08 July 2023 17h57

AI eats ML eats CS as the food chain goes

Positional Embeddings and Learned Query-Key Weights in Transformers

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08 July 2023 16h13

That's true, but you still have positional embeddings, and Q and K weights that are learned

Fine-tuning RoBERTa for Sentiment Classification to 95% Accuracy

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08 July 2023 15h41

Then, you can boost that performance (on the same movie review sentiment classification dataset) to ~95% finetuning model = AutoModelForSequenceClassification.from_pretrained( "siebert/sentiment-roberta-large-english", num_labels=2)

Ditching Word2Vec for Superior LLM Embeddings in Classification

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08 July 2023 15h40

I would ditch Word2Vec; the embeddings learned by LLMs are much better. For sentiment classification, you can start with DistilBERT as a base model and tune a few layers (see https://
magazine.sebastianraschka.com/p/finetuning-l
arge-language-models
…) 1/2

Sentiment Analysis and Classification Discussion

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08 July 2023 15h36

Sentiment analysis or classification 😛

→ View original post on X — @debashis_dutta

Probabilistic Machine Learning: Comprehensive Guide to Generative AI

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@debashis_dutta

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08 July 2023 15h26

Probabilistic #MachineLearning #GenerativeAI #AI @Khulood_Almani @Analytics_699 @GlenGilmore @MargaretSiegien @sallyeaves @baski_LA @ChuckDBrooks @HakomTimeSeries @labordeolivier @BetaMoroney @SabineVdL @KanezaDiane @MaiaGabunia @amalmerzouk https://
probml.github.io/pml-book/book2
.html?s=09
…