47 Matching Annotations
  1. May 2021
  2. Mar 2021
  3. Jan 2021
    1. In these cases there seems to be some sort of adaptive gating mechanism that disables the typical energy sinks in order to allow entropic disintegration->search->annealing to happen.

      at what cadence (if there is any threshold) can the brain reach high-energy states while not being overwhelmed?

  4. Oct 2020
    1. This is in stark contrast to the way that babies learn. They can recognize new objects after only seeing them a few times, and do so with very little effort and minimal external interaction. If ML’s greatest goal is to understand how humans learn, one must emulate the speed at which they do so. This direction of research is exemplified by a variety of techniques that may or may not fit into an existing paradigm; LeCun classified these tasks under the umbrella of “self-supervised learning.”

      Now "self-supervised" is hardly what babies do, when you see the importance of interactions in learning (see e.g. https://doi.org/10.1016/j.tics.2020.01.006 )

    2. practitioners are interested in developing DL approaches that accelerate the numerical solution of partial differential equations (PDEs).

      See e.g. Neural Ordinary Differential Equations https://arxiv.org/abs/1806.07366 ? Also https://julialang.org/blog/2019/01/fluxdiffeq/ for a nice intro

  5. Sep 2020
  6. Jul 2020
  7. Jun 2020
  8. May 2020
  9. Apr 2020
    1. import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 
    2. TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.
    3. In the 1980s, the Hidden Markov Model (HMM) was applied to the speech recognition system. HMM is a statistical model which is used to model the problems that involve sequential information. It has a pretty good track record in many real-world applications including speech recognition.  In 2001, Google introduced the Voice Search application that allowed users to search for queries by speaking to the machine.  This was the first voice-enabled application which was very popular among the people. It made the conversation between the people and machines a lot easier.  By 2011, Apple launched Siri that offered a real-time, faster, and easier way to interact with the Apple devices by just using your voice. As of now, Amazon’s Alexa and Google’s Home are the most popular voice command based virtual assistants that are being widely used by consumers across the globe. 
    4. Learn how to Build your own Speech-to-Text Model (using Python) Aravind Pai, July 15, 2019 Login to Bookmark this article (adsbygoogle = window.adsbygoogle || []).push({}); Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!
    1. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.
  10. Aug 2019
    1. so there won’t be a blinking bunny, at least not yet, let’s train our bunny to blink on command by mixing stimuli ( the tone and the air puff)

      Is it just that how we all learn and evolve? 😲

  11. 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.
  12. 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.

  13. Mar 2019
  14. 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)

    1. Silvia can see patterns in her own parenting that mimic those of her mother, although she knows that her mother made many mistakes.

      Clasifico esto como neural sculpting porque lo que Silvia vio en su hogar a temprana edad (de 0-7 años) formó y determinó hasta inconscientemente el camino que ella reforzó y reprodujo cuando le tocó ser madre. Ejemplo:

  15. 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.

  16. 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.

  17. Mar 2017
    1. To create it, Musk has said that he thinks we will probably have to inject a computer interface into the jugular where it will travel into the brain and unfold into a mesh of electric connections that connect directly to the neurons.

      Yeah, nothing could go wrong with this approach...

    2. Elon Musk’s neural lace project could turn us all into cyborgs, and he says that it’s only four or five years away.

      This seems incredibly ambitious--if not dangerous!

  18. 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,\(\frac{dE}{dw_i}=-(t-y)y(1-y)x_i\).

  19. Apr 2016
    1. Effect of step size. The gradient tells us the direction in which the function has the steepest rate of increase, but it does not tell us how far along this direction we should step.

      That's the reason why step size is an important factor in optimization algorithm. Too small step can cause the algorithm longer to converge. Too large step can cause that we change the parameters too much thus overstepped the optima.

  20. Jan 2016
  21. Jul 2015
  22. 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.