With Tensorflow 2.0 arriving soon, keras is going to be tightly integrated to be used for building neural nets. What do you use for building nets? For personal projects and for your career?

Hello,

I’m a current graduate student working on developing a research project and my current idea is for human-in-the-loop mobile health systems and how real time interacts with active learning with a single user.

I’m wondering if this is even a good idea before going further, and if theirs any similar research people know of.

Proposal Draft

Hello 👋

I am currently working on my master’s thesis (early stage) and I plan to do measuring and optimizing the video processing consistency and quality. For example, if we are doing the segmentation for the video we can get inconsistent predictions on consecutive frames.

I struggle very hard to find a related works papers – currently I have found only one good paper (Title of the paper: New Method for Evaluation of Video Segmentation Quality – 2015). Most of the papers are related to improvements in the model/segmentation metrics for one frame.

I would appreciate if someone could help me to get started with a few links to papers related to this problem, relevant search keywords etc. Also, if you have any thoughts about my topic of interest in general – I would also be happy to hear!

Abstract: We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the log loss we use for training is replaced by a low temperature logarithm. By tuning the two temperatures we create loss functions that are nonconvex already in the single layer case. When replacing the last layer of the neural nets by our bi-temperature generalization of logistic loss, the training becomes more robust to noise. We visualize the effect of tuning the two temperatures in a simple setting and show the efficacy of our method on large data sets. Our methodology is based on Bregman divergences and is superior to a related two-temperature method using the Tsallis divergence.

https://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html

We made a comparison of various Pandas functions for indexing, vectorization and filtering. We benchmarked and compared their performance. For example, we found out that using a vectorized function to transform data is 82000x faster than using a for-loop with iloc[]. Check out the blog post with more details on various Pandas optimizations here: https://medium.com/bigdatarepublic/advanced-pandas-optimize-speed-and-memory-a654b53be6c2

Language Tasks and Language Games: On Methodology in Current Natural Language Processing Research

David Schlangen(Submitted on 28 Aug 2019)

“This paper introduces a new task and a new dataset”, “we improve the state of the art in X by Y” — it is rare to find a current natural language processing paper (or AI paper more generally) that does not contain such statements. What is mostly left implicit, however, is the assumption that this necessarily constitutes progress, and what it constitutes progress towards. Here, we make more precise the normally impressionistically used notions of language task and language game and ask how a research programme built on these might make progress towards the goal of modelling general language competence.

https://arxiv.org/abs/1908.10747

This is an example of an image classifier built with Keras to identify presence of traffic objects.

Specifically, the VGG16 network was used as the pre-trained model, and the images themselves were manipulated using PIL for cropping purposes, and using Grad-CAM and cv2 to respectively generate a heatmap, and superimpose the heatmap on the relevant image.

Findings can be found here. Hope that you find this of use, and any feedback or tips greatly appreciated.