x-posting from /r/tensorflow

Hi! I’m working on Tensorflow bindings for Luna (http://luna-lang.org/).

We allow you to build ML models by connecting visual components together – every component can define a new network layer and its dependencies. The API is highly inspired by Keras functional API.

Here you can find an example of what it looks like

Luna has the ability to display visualizations below its components, so you could inspect the look of your network on each step (after adding the first layer, adding the second layer, etc). We want to provide interactive visualizations of the network you’ve built so far. I’d love to ask you what visualizations you would find the most helpful during building neural networks?

We were initially thinking about something like that – so you could see the structure of your network, the weights and activation functions, but we are very open for discussion here. We want to create something that would be helpful while building various kinds of networks.

Which features do you think are most important to visualize? The weights? The activations on each layer? Something else entirely?

Hey y’all, I’m pretty much a complete novice when it comes to machine learning, but I had what seemed to me like an interesting idea. Don’t hesitate to dismiss this as the hunch of a layman if that is what it seems to be in your more educated opinions.

So, I’ve been screwing around with GPT-2-Lite recently just for fun. Something I noticed is that one skill the model has 100% on lock is always closing parentheses (Yes, I’m anthropomorphizing lol). Further, it seems to have a pretty good sense of how parentheticals relate to preceding text — i.e. they tend to contain elaboration or qualification on the immediately preceding topic, or a digression that somewhat interrupts the flow of the text. You can get this behavior on demand by feeding the model a prompt that ends with an open paren. For example, both parentheticals in this sentence from the Gettysburg address were generated that way:

It is for us the living (who, in fact are in perpetual combat here, and who are still here), rather, to be dedicated here to the unfinished work (and for that work to be paid) which they who fought here have thus far so nobly advanced.

So, I guess it must have picked up on the characteristics of phrases following the ‘(‘ character in the training set. Same would apply to the open quote character, em-dash, and function words like “which” that head clauses with certain predictable characteristics. My thought is that there may be other semantic features in the training set which could be detected programmatically but are not currently delimited by such explicit and compact cues. Would you expect there to be any value in augmenting the training data with some made up cues for those features? E.g., you could trawl through the text and insert ‘|’ characters bounding every sentence that meets some sentiment analysis criterion (maybe extreme negative sentiment); or you could do the same for compound or complex sentences. What do you reckon would happen if you fed the resulting trained model some text that ends with the opening delimiter? Would that give you a way to reliably elicit completions that have the target semantic feature? I don’t have the hardware or know-how to do the training part, so I can’t test this myself.

First game is over, but I won’t spoil it 🙂

livestream: https://www.twitch.tv/openai

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EDIT: top comment is a spoiler, just a heads up

Just got home from drinking and we were making fun of some common stereotypes that my friends exhibit from their careers, such as the doctor dude having unintelligible handwriting. Honestly though, I couldn’t think of any stereotypes among us folks that work with machine learning – whether data science, research, engineer, whatever.

So, what are common stereotypes that I seem to be blissfully unaware of?

P.S: I’m still a bit tipsy, so apologies if this is inappropriate to ask here.

Github: https://github.com/AruniRC/detectron-self-train

Project page: http://vis-www.cs.umass.edu/unsupVideo/

(accepted at CVPR 2019)

Abstract. This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readily available. We automatically obtain labels on the target data by using high-confidence detections from the existing detector, augmented with hard (misclassified) examples acquired by exploiting temporal cues using a tracker. These automatically-obtained labels are then used for re-training the original model. A modified knowledge distillation loss is proposed, and we investigate several ways of assigning soft-labels to the training examples from the target domain. Our approach is empirically evaluated on challenging face and pedestrian detection tasks: a face detector trained on WIDER-Face, which consists of high-quality images crawled from the web, is adapted to a large-scale surveillance data set; a pedestrian detector trained on clear, daytime images from the BDD-100K driving data set is adapted to all other scenarios such as rainy, foggy, night-time. Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.

Assume that we have an input string “I need to buy some chicken”. After working a bit on this string, suppose that we’ve reduced it to “buy chicken”

My question is, how can we understand that chicken is something related to cafe or supermarket, but not related to locksmith or post office.

More specifically, I have n number of point of interest types and I am trying to come up with n probabilities p_1, p_2, …, p_n where each probability represents the likelihood (or meaningfulness) of string-type pairs.

My ultimate goal is to have an unequality containing these n probabilities, which should of course be meaningful

I want to have

p(chicken, synagogue) < p(chicken, supermarket)

But not

p(chicken, train_station) > p(chicken, café)

I have tried to do google searches and determine these probabilities according to the number of results but it wasn’t satisfying at all.

For example, when I searched “chicken breast EMBASSY”: I got 24,500,000 results. For “chicken breast SUPERMARKET”, number of results was 11,600,000.

If we compute the probabilities by only taking these numbers into account, we’d arrive at a conclusion where p(chicken, supermarket) < p(chicken, embassy) which would of course be wrong.

Do you have any suggestions on how to approach this problem?

Context: Imagine that you are not dealing with sentences, only solo words. Therefore methods like FLAIR/FastText may not be very appropriated to pretrain with your corpora.

Datasets collected for network science and machine learning research.

https://github.com/benedekrozemberczki/datasets/

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