12 Matching Annotations
  1. Jul 2019
    1. Compared with neural networks configured by a pure grid search,we find that random search over the same domain is able to find models that are as good or betterwithin a small fraction of the computation time.
  2. Jun 2019
    1. Throughout the past two decades, he has been conducting research in the fields of psychology of learning and hybrid neural network (in particular, applying these models to research on human skill acquisition). Specifically, he has worked on the integrated effect of "top-down" and "bottom-up" learning in human skill acquisition,[1][2] in a variety of task domains, for example, navigation tasks,[3] reasoning tasks, and implicit learning tasks.[4] This inclusion of bottom-up learning processes has been revolutionary in cognitive psychology, because most previous models of learning had focused exclusively on top-down learning (whereas human learning clearly happens in both directions). This research has culminated with the development of an integrated cognitive architecture that can be used to provide a qualitative and quantitative explanation of empirical psychological learning data. The model, CLARION, is a hybrid neural network that can be used to simulate problem solving and social interactions as well. More importantly, CLARION was the first psychological model that proposed an explanation for the "bottom-up learning" mechanisms present in human skill acquisition: His numerous papers on the subject have brought attention to this neglected area in cognitive psychology.
    1. However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

      Use min-max scaling for image processing & neural networks.

  3. Mar 2019
  4. Oct 2018
    1. Do neural networks dream of semantics?

      Neural networks in visual analysis, linguistics Knowledge graph applications

      1. Data integration,
      2. Visualization
      3. Exploratory search
      4. Question answering

      Future goals: neuro-symbolic integration (symbolic reasoning and machine learning)

  5. Aug 2017
    1. This is a very easy paper to follow, but it looks like their methodology is a simple way to improve performance on limited data. I'm curious how well this is reproduced elsewhere.

    1. The takeaway is that you should not be using smaller networks because you are afraid of overfitting. Instead, you should use as big of a neural network as your computational budget allows, and use other regularization techniques to control overfitting

      What about the rule of thumb stating that you should have roughly 5-10 times as many data points as weights in order to not overfit?

  6. Apr 2017
    1. If we write that out as equations, we get:

      It would be easier to understand what are x and y and W here if the actual numbers were used, like 784, 10, 55000, etc. In this simple example there are 3 x and 3 y, which is misleading. In reality there are 784 x elements (for each pixel) and 55,000 such x arrays and only 10 y elements (for each digit) and then 55,000 of them.

  7. Nov 2016
    1. Softmax分类器所做的就是最小化在估计分类概率(就是 Li=efyi/∑jefjLi=efyi/∑jefjL_i =e^{f_{y_i}}/\sum_je^{f_j})和“真实”分布之间的交叉熵.

      而这样的好处,就是如果样本误分的话,就会有一个非常大的梯度。而如果使用逻辑回归误分的越严重,算法收敛越慢。比如,\(t_i=1\) 而 \(y_i=0.0000001\),cost function 为 \(E=\frac{1}{2}(t-y)^2\) 那么,\(\frac{dE}{dw_i}=-(t-y)y(1-y)x_i\).

  8. Jan 2016
  9. Jul 2015
  10. Jun 2015
    1. Enter the Daily Mail website, MailOnline, and CNN online. These sites display news stories with the main points of the story displayed as bullet points that are written independently of the text. “Of key importance is that these summary points are abstractive and do not simply copy sentences from the documents,” say Hermann and co.

      Someday, maybe projects like Hypothesis will help teach computers to read, too.