23 Feb 2026, 17:14 UTC≈1,010 views10 reactions3 Starsread 7 August 2026 Relevance Feedback
Did you know that Google search results used to have 👍/👎 buttons?
It was the way to collect signals from users and improve the ranking.
Do you know why you don't see those buttons anymore?
People are either too lazy to click or they abuse the buttons to their advantage.
In 2026, however, real people don't have to rank raw search results anymore - there are all sorts of AI in the middle.
And her…
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Signed nne_controll_bot
9 Jun 2025, 12:03 UTC≈1,810 views16 reactionsread 7 August 2026 Embeddings Confidence
Let's say we want to know which embeddings are good and which are noisy.
Normally, you can't do this, because embedding model tries to represent the whole variety of objects.
Even if input data is random, it would still match with similar random data.
Using the score threshold is also not a good idea, embedding models were not trained to predict absolute scores,
only relative scores makes s…
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Signed nne_controll_bot
8 Jun 2025, 13:52 UTCviews —20 reactionsread 7 August 2026 Posted without readable text
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14 May 2025, 17:16 UTC≈1,990 views26 reactionsread 7 August 2026 MiniCOIL: Contextualized per-word embeddings
We continue our experiments with unorthodox methods to embed texts.
This time we want to solve the following problems of BM25:
- It can't differentiate homonyms: "bat" as and animal and a baseball "bat" are the same thing for BM25.
- It does loose the information during the stemming process: "information" and "informant" are the same for BM25.
How are we doing this?
…
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Signed nne_controll_bot
2 Jul 2024, 12:57 UTC≈3,650 views36 reactions1 Starread 7 August 2026 BM42: the next hybrid search baseline
You probably heard that embedding similarity struggles with exact keyword matches, especially when the keyword is a rare word, a name, or some kind of ID.
Usually, this problem is solved by combining the embedding similarity with an exact keyword search like BM25.
However, BM25 relies on pure statistics and has no idea about the meaning of the words.
This works well for larg…
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Signed nne_controll_bot
23 Jan 2024, 10:22 UTC≈4,890 views33 reactionsread 7 August 2026 https://techcrunch.com/2024/01/23/qdrant-open-source-vector-database/
We are so hiring!
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Signed Andrey
4 Dec 2023, 15:29 UTC≈5,250 views19 reactionsread 7 August 2026 Attention all Berliners!
You are invited to our first offline meetup!
Join us for talks on vector search, machine learning, and more.
I will also be participating, providing an overview of Qdrant's progress and future plans.
Still not convinced? We will have free pizza and beer!
The event is scheduled for December 8, 2023, at 18:00, in Berlin.
Please register https://lu.ma/vectorspace.
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Signed Andrey
8 Aug 2023, 12:34 UTC≈6,100 views22 reactionsread 7 August 2026 Vector Similaruty beyond Search
Vector similarity offers a range of powerful functions that go far beyond those available in traditional full-text search engines and the conventional kNN search.
We just scratched the surface of the topic but already found a lot of new ways to interact with the data, including:
- Dissimilarity search - that can be applied to anomaly detection, mislabeling detection, and data cleani…
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Signed nne_controll_bot
31 Aug 2022, 12:15 UTC≈8,570 views33 reactionsread 7 August 2026 How many layers to fine-tune?
Model fine-tuning allows you to improve the quality of the pre-trained models with just a fraction of the resources spent on training the original model. But there is a trade-off between the number of layers you tune and the precision you get.
Using fewer layers allows for faster training with a larger batch size, while more layers increase the model's capacity.
We've done experiments…
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Signed nne_controll_bot
29 Jun 2022, 11:46 UTC≈7,830 views13 reactionsread 7 August 2026 Video
One of the main features of the framework is caching. It allows you to infer large models only once and then use cached vectors during the training. It speeds up the process x100 times, simultaneously allowing you to use batch sizes that are unattainable in other ways.
(gif)
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Signed Andrey
29 Jun 2022, 11:45 UTC≈7,100 views14 reactionsread 7 August 2026 Similarity Learning lacks a framework. So we built one.
Many general-purpose frameworks allow you to train Computer Vision or NLP tasks quickly. However, Similarity Learning has peculiarities, which usually require an additional layer of complexity on top of the usual pipelines.
So, for example, the batch size in the training of similarity models has a much greater role than in other models. Labels either do not exi…
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Signed Andrey
4 May 2022, 12:40 UTC≈6,470 views21 reactionsread 7 August 2026 Metric Learning for Anomaly Detection
Anomaly detection is one of those tasks to which it is challenging to apply classical ML methods directly.
The balancing of normal and abnormal examples and the internal inconsistency of anomalies make classifier training a challenging task.
And the difficulty is often related to data labeling, which in the case of anomalies may not be trivial.
The metric learning approach avo…
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Signed nne_controll_bot
Showing the 12 most recent of 20 posts we hold for @neural_network_engineering. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.