I work in machine learning for healthcare and people often come to me suggesting an expert system. Usually what they mean is a hand-crafted version of a decision tree. I haven’t been able to find any good articles really articulating the cost/benefit of each approach with examples and I’m wondering: What has everyone else’s experience has been?

Examples I usually give:

• One part of the core algorithm in an app was a giant decision tree from ~5-10 years ago, 3-4 deep with very complex conditions. One of the smartest guys around decided to understand and document the code. Even after several days of experimentation and talking to the author, he couldn’t explain it all. Due to the feeling that it was important, we weren’t able to remove the unknown code and therefore could clean it up either. That code was doing pattern matching for mobile keyboards – would’ve been a good fit for ML.
• Another project would jump-start text query parsers with a hand-written grammar. Once it seemed reasonable they’d release it, collect query data, annotate it, then replace it with an ML system. That meant a much faster time for both the first and second releases.
• I’ve seen a great example in a book but I forget which – it talks about classifying something as a bird or not and demonstrates the challenges of building a high quality system.

We glad to present and discuss our paper named Linear Distillation Learning (LDL). Is a simple remedy to improve the performance of linear networks through distillation.

In deep learning models, distillation often allows the smaller/shallow network to mimic the larger models in a much more accurate way, while a network of the same size trained on the one-hot targets can’t achieve comparable results to the cumbersome model. Our neural networks without activation functions achieved high classification score on a small amount of data on MNIST and Omniglot datasets.

The approach is based on using a linear function for each class in dataset, which is trained to simulate output of teacher linear network for each class separately. When the model is trained, we can apply classification by novelty detection for each class. Our framework distilling randomized prior functions for data, since prior functions are linear, in couple with bootstrap methods it provides a Bayes posterior.

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Highlights: https://medium.com/@asadulaevarip/linear-distillation-learning-da76a2f3a933

arXiv: https://arxiv.org/abs/1906.05431

Twitter: https://twitter.com/postmachines/status/1146848387658108928

I’ve been reading through Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction (Author’s GitHub repo) and I found it quite interesting. My field is more general event stream processing rather than medical but I am thinking this approach is worth exploring.

I’ve not seen this paper discussed before so I thought I’d raise it here and ask what peoples thoughts are.

Unfortunately for me, I can’t access the source data to reproduce their results (since you need to be in the medical field to access Physionet data) and Chinese is totally foreign to me so I’ve had to use the help of Google Translate with the couple of PDF files I found in the GH repo that explain the data format).

I’ve gone through and annotated the file format to help me at least understand it better – may be useful for others trying to explore this: Annotated data format

Hello everyone!

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I am trying to reproduce this paper in Python: A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement by Jean-Marc Valin

Additionally, there is a blog post by the author explaining the paper differently: RNNoise: Learning Noise Suppression, and a GitHub repository with the code for training the proposed network.

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However, I have difficulty understanding the concepts regarding preparing input data for training and prediction. Can someone give me practical notes on how I can achieve this?

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Some questions I have in section II:

1. The paper and blog post computes 22 bands at first. Where a DCT is applied on the log spectrum, resulting in 22 Bark-frequency cepstral coefficients. Which is closely related to the Mel-Frequency Cepstral Coefficients. What does this mean, and how does this work?

2. The author also includes the temporal derivative and the second temporal derivative of the first six Bark-frequency cepstral coefficients across frames. What does this mean?

3. In formula (5) the pitch correction for every band is calculated, with that the author computes the DCT of the pitch correlation across frequency bands and include the first six coefficients. I assume DCT returns a finite set of results. So only 6 of the first coefficients is used per band, correct?

4. The author mentions including the pitch period as well as a spectral non-stationarity metric. What does this mean?

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Some background: I have mostly worked with visual data and convolutional neural networks, so I have almost no knowledge about digital signal processing. Please bear with me.

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Thanks in advance!

Now, my team is developing a application quite similarity as https://www.celebslike.me of Microsoft Corp before (2016). We seperate the asual canonical pipeline of face recognition system into 3 phrase:

• Phase 1: Face Detection + Face Aligment
• Phase 2: Face Embedding
• Phase 3: Face matching / Face recognion or something like face similarity.

At (3) we use the technical that is the embedded vectors got from a given model that ours is from Facenet in this case to conjunct with Elasticsearch for similarity search.

We are still implementing above solution for now but personally I want to research some nice solutions (if any) for my knowledge also help to improve our product better.

How do you think about another solution(s) for this phase (3) ?

Any ideas are welcome !

The environment I used is a card game with which has some randomness when the cards are drawn.

I used 10 different seeds to train the models. Thus 10 models trained for my algorithm, 10 for DQN baseline, etc.

For this study, I’m measuring performance based on the rewards from running the trained models in the environment.

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1. How should I test the difference between the average reward when using my algorithm vs that of the baselines?

For example, should I get the average of 10,000 episodes of the DQN model and then get the average and SD of the 100,000 episodes (from adding up the results of 10,000 episodes from 10 DQN models)? Then after completing the previous step for all the model groups (my own algorithm, other baselines, etc.), compare the rewards with Welch’s t-test?

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1. Also, should I used the same random seed (different from training obviously) for the environment when testing the 10 different runs or should I use a different one each time? For example, testing the 10 different trained DQN models with seed 1 for all 100,000 episodes vs using seeds 1 to 10 for 10,000 episodes each.

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Some advice would be helpful. Thanks for reading this and I apologize if any part of this sounds confusing. English is not my first language.

From the abstract:

Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure– property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

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Paper: https://sci-hub.tw/https://www.nature.com/articles/s41586-019-1335-8

Source Code: https://github.com/materialsintelligence/mat2vec

listening to the planet money episode on the plight of the Uighur people:

https://twitter.com/planetmoney/status/1147240518411309056

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In the Uighur region every home is bugged, every apartment building filled with cameras, every citizen’s face recorded from every angle in every expression, all DNA recorded, every interaction recorded and NLP used to extract risk for being a dissident. These databases then restrict ability to do anything or go anywhere, and will put you in a concentration camp if your score is too bad.

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Maybe google have done some cool things with ML, but my impression is that globally this is 90% being used for utter totalitarian evil.