@mybmc No action taken on this blocked & broken drainage chamber on LJ Rd despite formal complaint, follow-ups. Please take immediate action. Serious health menace for residents. Local sewage not draining into municipal gutters ⇒ clogged sewage ⇒ rats, mosquitos, cockroaches...
We identify that Conv Nets implement a variant of the same general mechanism of feature learning as in fully-connected networks. The covariances of the filters in CNNs again recover the average gradient outer-product (AGOP) of the model, additionally averaged over input patches.
@andrewgwils 2. https://t.co/JWiWizMMgb
How to train kernel models with large number of centers (or inducing points)?
A new algorithm, EigenPro3, to train such models requiring only O(p) memory for p centers. Prior work such as FALKON needed O(p^2) memory, ie, infeasible for large models
@andrewgwils You might find these papers interesting too:
1. https://t.co/RjwtzBG9nm
recursively adapting kernel functions inspired by how neural networks learn features
These models, Recursive Feature Machines, are SotA on tabular datasets and bridge the gap to fully connected networks
We now have a simple, powerful, and stable alternative to Deep Nets
A challenge in classical kernel models is choosing the 'right' kernel
By examining NN training, we identified the modification necessary to empower kernels - FEATURE LEARNING
#NeuralNetworkFreeSince2023
What is the nature of feature learning in deep networks? We propose that neural networks recover a statistic known as the average gradient outer product (AGOP).
Github: https://t.co/P5FW59VD8Y
arXiv: https://t.co/MW9Uo6nOnj
@Yizhezhu_ How many samples do I need to generate to achieve the rate? If there are n true samples, can you generate O(sqrt(n)) samples and still achieve small W1-distance?
@ysbhalgat Paragraph structure is very important for technical communication.
The same text should perhaps be divided into 3-4 separate paragraphs, each with a purpose.
@ysbhalgat Not a good summary
- Needs bullets. Currently, I need to read an entire sentence with obscure 10-word-named schemes to get to the start of the next sentence
- The Capitalization Of Words In The Sentences Makes It Harder To Read Easily
- Topics can be segregated based on themes
Rahul Parhi and I wrote a tutorial style article to explain modern theory of deep learning and neural function spaces via elementary signal/image processing concepts (Fourier transform, Radon transform, L1 regularization, sparsity) https://t.co/nDsmX2L6XI