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followup to #696
specifically what inference can we use after estimation.
question that I'm looking at right now is whether QuantReg should use t or normal distribution. (I'm adding a `use_t` option …
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The current evaluation metrics supported by `llm-eval` are robust. However, upon reviewing the documentation, I found that the current repo doesn't account for evaluating model toxicity. Assessing LLM…
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Security is all about *worst*-case guarantees. Despite this fact, the paper makes many of the inferences by looking at the *average*-case robustness.
This is fundamentally flawed.
If a defense…
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Hi Thomas Hugues,
many thanks for this pytorch version of KPConv.
Applying a trained model on new data with different point density using `test_model.py` recomputes the calibration if the `batch_l…
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some references (for the robust RLM case)
http://scholar.google.com/scholar?cites=17895094206680664513&as_sdt=2005&sciodt=0,5&hl=en
Question: if we impose a constraint and want to test inequalitie…
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### Search before asking
- [X] I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and fou…
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I'm new to this specific project, and I don't say any of the following with high confidence.
Things that I see as important for quantization:
*Inference speed*
- AWQ seems best on this front, t…
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HFA gives similar results to HMF, though inference of the mean seems to be more robust to initialization for the former.
Now, need to decide if there is a juicy problem that needs sophisticated dimen…
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Hi all,
Thanks for the awesome library. It would be fantastic to eventually see some options for trust-region optimization. For smaller dimensional problems in my field (e.g., variance components, …