Background: 5th year phd student in ML at a top 20 university in the US. I think I have a decent publication record (~5 publications at top venues), but not many citations (<100).

I was fortunate to receive several offers for research scientist positions at a few companies. Some are within actual research groups at big tech companies, while others have the title “research scientist” but are more data scientist roles (think finance, but not trading).

Without divulging too much information, my offers from the big tech companies were lower than expected, at around 200K total comp (130K base, rest in stock + sign-on). In contrast my best offer from a finance company is around 300K.

Right now I am still leaning towards the big tech positions since I’ll be able to publish, but I feel like I am getting low-balled. I’ve heard of phds getting well over 300K for similar positions a few years ago, albeit with slightly stronger publication records. Is it unreasonable for me to ask for more?

It’s been a while since I worked on high-dimensional density estimation, and I need to get back in the game. Which is the current state of the art for this problem? Is it the Autoregressive Energy Machine? And what’s the model/s you commonly use when you have to estimate an high-dimensional density? Links to libraries/model implementations would be very appreciated.

I’m also interested in a slightly different problem – sampling from the estimated density. Ideally I would be interested in models which are both good at estimating the target density, and which allow easy/efficient sampling. However, if it’s not possible to find models which excel at both tasks, I’d be fine with separate recommendations for when I’m mostly interested in accurate estimation, and for when I’m mostly interested in sampling from a target density (I guess VAEs or GANs would be the recommended approaches in the second case).

Hi everyone, I’ve been increasingly impressed with results for training models (mostly computer vision tasks) on synthetic data. I noticed Google commented on using data from speech synthesis models to train the [latest version of their on-device ASR model](https://arxiv.org/abs/1811.06621).

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[I wrote a brief primer](https://heartbeat.fritz.ai/synthetic-data-a-bridge-over-the-data-moat-29f392a52f27) on some of the things I’m seeing as well as limitations / open questions and was wondering if anyone here has used synthetically generated data in production?

Some insight into Google DeepMind’s recent research and Jane’s work connecting Neuroscience, AI and ML: https://simpleweb.co.uk/qa-with-google-deepminds-jane-wang/

Hello,

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I’ve built an LSTM autoencoder using Keras, similar to this tutorial: https://blog.keras.io/building-autoencoders-in-keras.html . I use padding with a mask to incorporate sequences of different length. However, when I look at the TSNE plot of the reduced features, I see my points clustering more by sequence duration than by features. Yes, within some larger clusters it does cluster within by interesting features, but the larger clusters are all by sequence length. Do you guys/gals have any suggestions to minimize the effect of sequence length on the encoded features?

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Thanks a Billion!

I have noticed that many new datasets are based on data collected from Youtube videos. I am wondering what rules govern data collection from Youtube.

Does Youtube permit their data to be used for research purposes? Do people sign an agreement with Youtube prior to using it? Is it legal to use it? Are there ethics rules for this?

Has anyone of you thought about this, please share your views.

That’s simple, do you think we can face an algorithm producer to validator consumer and generate new algorithms to whatever problem we want to resolve, for example create new ML algorithms?

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I don’t know if this is possible, but what we would need to start do it?

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Crazy morning ideas 😛

Hi, I’m Myeongjun Kim. I’m currently taking a master’s course. My major is computer vision using deep learning. This is the second reddit posting. I’ve implemented the paper “Attention Augmented Convolutional Networks written by Google Brain” as a Pytorch. Already implemented in the paper as Tensorflow, I changed it to Pytorch.
I am currently conducting an experiment in a simple layer(3 convolution layers) and will further conduct an experiment on ResNet in paper. (CIFAR-100)

Thank you so much for reading the long story. Any feedback is welcome 🙂

Github URL: https://github.com/leaderj1001/Attention-Augmented-Conv2d

Thank you !!