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I can't think of anything that neural nets can't beat, except small tabular data with boosted decision trees. Can you give some examples?

zelphirkalt 4y ago
Explicability is a big part of it It is often worth being a percent less accurat but having an explainable result.
- adamsmith143 4y ago
  I've been on a lot of ML teams and outside of Finance and a few other sensitive topics explainability has always been irrelevant.
  - zelphirkalt 4y ago
    What happens, when your model exhibits a discriminating bias? How do you find out, what is going wrong? Knowing, what the model pays attention to can be pretty helpful.
    - PubliusMI 4y ago
    - adamsmith143 4y ago
      Not aware of any court cases where someone successfully sued because they were shown one product recommendation or Ad on a webpage instead of another.
patrick451 4y ago
The black box nature of a neural net is a problem. For model based design, a bit more accuracy out of a black box doesn't really help when you need, for example, state space matrices in a control design.
niemandhier 4y ago
Small data problems, where’re never the less have a really good idea of how things are causally related.
jstx1 4y ago
(I don't really agree with GP's point but for the sake of answering your question) 1. Collaborative filtering based on a sparse dataset of implicit interactions. 2. Many time series applications.
- marcyb5st 4y ago
  Didn't all recommendations engines move to two-towers like models? I remember that it "solved" the freshness problem (ie when adding a new item to your catalog how do you recommend it to users if there are no ratings/interactions). Of course as long as you have a good model that creates items embeddings. Regarding time series, don't everyone moved to attention based models? Not challenging your answer, just curious. I work mostly with Graph NNs and quite a bit out of touch with the rest of the field.
insane_dreamer 4y ago
> we often use ML over DL in scientific analysis because we need models that can be inspected/explained not just results > also, DL generally requires more data whereas you can get by with ML on less data if you have domain knowledge

zelphirkalt 4y ago

Explicability is a big part of it It is often worth being a percent less accurat but having an explainable result.

adamsmith143 4y ago
I've been on a lot of ML teams and outside of Finance and a few other sensitive topics explainability has always been irrelevant.
- zelphirkalt 4y ago
  What happens, when your model exhibits a discriminating bias? How do you find out, what is going wrong? Knowing, what the model pays attention to can be pretty helpful.
  - PubliusMI 4y ago
  - adamsmith143 4y ago
    Not aware of any court cases where someone successfully sued because they were shown one product recommendation or Ad on a webpage instead of another.

patrick451 4y ago

The black box nature of a neural net is a problem. For model based design, a bit more accuracy out of a black box doesn't really help when you need, for example, state space matrices in a control design.

niemandhier 4y ago

Small data problems, where’re never the less have a really good idea of how things are causally related.

jstx1 4y ago

(I don't really agree with GP's point but for the sake of answering your question) 1. Collaborative filtering based on a sparse dataset of implicit interactions. 2. Many time series applications.

marcyb5st 4y ago
Didn't all recommendations engines move to two-towers like models? I remember that it "solved" the freshness problem (ie when adding a new item to your catalog how do you recommend it to users if there are no ratings/interactions). Of course as long as you have a good model that creates items embeddings. Regarding time series, don't everyone moved to attention based models? Not challenging your answer, just curious. I work mostly with Graph NNs and quite a bit out of touch with the rest of the field.

insane_dreamer 4y ago

> we often use ML over DL in scientific analysis because we need models that can be inspected/explained not just results > also, DL generally requires more data whereas you can get by with ML on less data if you have domain knowledge