-
The current implementation of the neural network's training and forward propagation methods is not functioning correctly. Specifically, the methods do not align with the principles of Kolmogorov–Arnol…
-
- [x] loading data with augmentations
- [x] more annotated data with different magnifications/lightings
- [ ] easily train and compare models
- [ ] inference using trained model, e.g. measure laten…
-
This is a meta-issue to track the issues that we need to solve in order for Octavian to become a competitive option for training neural networks on the CPU.
- [ ] #40
- [ ] #56
-
**Abstract:** The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-th…
-
@lisitsyn @khalednasr please initiate this later in the project.
-
Anyone has interest to utilize the sparsity to accelerate DNNs?
I am working on the fork https://github.com/wenwei202/caffe/tree/scnn and currently, on average, achieve ~5x CPU and ~3x GPU layer-wi…
-
Hello,
Big fan of your videos, I decided to look into your code this time !
In neural_network_tutorials.ipynb, 4/ Deep Neural Networks (Numpy).
If I change` ys = xs**2`, for anything else (`…
-
I have taken a closer look at your quantum neural network architecture, which is of great help to my research, do you have a paper on this architecture, I would like to see a specific paper.
-
https://medium.com/@saadsalmanakram/kolmogorov-arnold-networks-a-comprehensive-guide-to-neural-network-advancement-5919fc8f81b1
-
## 論文リンク
https://arxiv.org/abs/1906.01563
## 公開日(yyyy/mm/dd)
2019/06/04
## 概要
ハミルトン形式の議論を参考にして、保存量であるべきエネルギーが保存されるように教師なしで学習するモデルを考案(モデルが保存量とするのは正確にはエネルギーではない)。
位相空間での $ q, p $ を入力としてハミルトニアン…