Transformer-MM-Explainability
WeightWatcher
| Transformer-MM-Explainability | WeightWatcher | |
|---|---|---|
| 3 | 4 | |
| 704 | 1,393 | |
| - | 0.4% | |
| 0.0 | 9.1 | |
| 8 months ago | 19 days ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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Transformer-MM-Explainability
WeightWatcher
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Ask HN: Have you seen anything original produced by generative AI?
These models are pretty much always extrapolating [0]
Whether the extrapolation is crude/low-rank or astute/high-rank is a question of memorization vs generalization. That gets into the question of whether or not the model is over-fitted or under-fitted. There are certain heuristics borrowed from high dimensional statistical physics that can be used to guess how good the test performance of a model will be on a typical task without even knowing what the test data is [1].
Originality for me means finding better answers to sub-tasks, and then combining those answers together in a better way. This is the nirvana of cross-entropy minimization - the emergence of capability results from gaining the ability to amass a wider range of skills, improving upon them, and percolating those improvements towards multiply the leverage of other skills.
How long such a thing can keep improving with current tech, who knows, but you should really think critically about whether that sounds just like interpolation through the corpus.
[0] Learning in High Dimension Always Amounts to Extrapolation - https://arxiv.org/abs/2110.09485
[1] https://github.com/CalculatedContent/WeightWatcher
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Physics and Machine Learning
One of the things I love about physics is that, in addition to probably being my favorite of study in it's own right, it seems that a lot of the conceptual/mathematical content carries over and contributes to other fields. One example I've come across recently can be found here: https://github.com/CalculatedContent/WeightWatcher and here:
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
- A New Link to an Old Model Could Crack the Mystery of Deep Learning
What are some alternatives?
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
captum - Model interpretability and understanding for PyTorch
TorchDrift - Drift Detection for your PyTorch Models
explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
pytea - PyTea: PyTorch Tensor shape error analyzer
shap - A game theoretic approach to explain the output of any machine learning model.
cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both