arxiv: https://arxiv.org/abs/1910.12354

github: https://github.com/vkurenkov/language-grounding-multigoal

Abstract:

In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear.We generate a simple instructional language for the GridWorld environment, that is built around three language elements (order connectors) defining the order of execution: one linear – “comma” and two non-linear – “but first”, “but before”. We apply one of the deep reinforcement learning baselines – Double DQN with frame stacking and ablate several extensions such as Prioritized Experience Replay and Gated-Attention architecture.Our results show that the introduction of non-linear order connectors improves the success rate on instructions with a higher number of sub-goals in 2-3 times, but it still does not exceed 20%. Also, we observe that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting. Source code and experiments’ results are available at this https URL

I’m looking for user data for my Computer Science Masters project “Using Community Detection to Improve Music Recommendations”.

I’ll be using machine learning to examine user music data from Spotify with the aim of improving the songs people are recommended.

I’ve produced a web app where you can consent to data being (anonymously) sampled from your Spotify account. It only takes about 1 minute to log in and would really help me out.

Thanks!

https://james-atkin-spotify-project.herokuapp.com/

A few questions about this paper:

1. If you train an ensemble of SOTA architectures on Imagenet and average their results, do you beat STNS?
2. Why not fine tune the teacher? Why involve the student at all? Why not have the teacher fine tune with noisy labels and get rid of the student completely?
3. The noisy part to the student seems odd to me. Why would this work other than the fact of adding noise you sort of anneal the solution. Why not add noise to the gradient or do what i suggest in 2.

I see Q Le has investigated noisy gradients already. https://arxiv.org/pdf/1511.06807.pdf

I’m trying to predict the ranking of figure skaters in the annual world championship by their scores in earlier competition events in the season. The obvious method to do is by average the scores for each skater across past events and rank them by those averages. However, since no two events are the same, the goal for my project is to separate the skater effect, the intrinsic ability of each skater, by the event effect, how an event influence the score of a skater.

In the previous 5 parts of my projects, I’ve developed several models to predict the ranking of skaters (as outlined in an earlier Reddit post). In this last part of my project, I try to combine these rankings into a final ranking that hopefully will be more accurate than any of the previous rankings individually. You can read the write-up for it here.

I used two different approach to combine the rankings:

• An unsupervised approach using the centuries-old method of Borda count that is used to tally ranked votes.

• A supervised approach using logistic regression to combine the scores from each model more intelligently, using the world championship itself as a guide.

Finally, all of the 7 ranking models that I developed in my project are benchmarked on the 5 seasons in the test set. I won’t spoil the details and explanations of the final result (you can see a glimpse of it here), but let’s just say that predicting sports is hard AF!

You can check out the Github repo of the project for all my analyses. I’m more than happy to answer any question or feedback you might have for my project. Thank you for taking the time to read it.

I just found out about disentangled variational autoencoders and they seem pretty exciting to me.

I was wondering if there was something similar with GANs and so I searched and found this https://arxiv.org/abs/1906.06034 “InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers”.

What should I search for in order to find papers that have the best performance, or is there a new technique for disentanglement (or something along those lines)?

Thanks.

We do a lot of image classification using the Tensorflow Object Detection API.

Our images often appear in groups, e.g. a cluster of fish swimming by a camera. Ofter our model will recognize some of the fish – but not all of them. This is obviously a mistake a human would never make.

I am researching whether there are any examples of object detectors/image classifiers using context to improve results? I.e. knowing that there are three fish swimming through would increase the model’s propensity to find a fourth nearby?

Another way to potentially attack this problem would be to identify clusters of objects -> then reexamine the cluster only to identify the number of objects within the cluster. The complexity with this issue is that some of the objects we are examining appear in clusters, while others do not.

So to summarize, my two questions are:

• Is there research on, and how would you suggest I go about improving an object detector by using contextual features within an image?
• In situations where there are clusters of objects, would you recommend recognizing those clusters as individual images and then subsequently processing them to identify the number of images within the cluster? Are there examples / is there research on this?

Thanks in advance – obviously doing my own research as well, but keen to hear if the community has any thoughts/examples!

Github: https://github.com/ACI-Institute/faces4coco

Over half of the 120,000 images in the 2017 COCO(Common Objects in Context) dataset contain people, and while COCO’s bounding box annotations include some 90 different classes, there is only one class for people. Those bounding boxes encompass the entire body of the person (head, body, and extremities), but being able to detect and isolate specific parts is useful and has many applications in machine learning. Detecting faces in particular is useful, so we’ve created a dataset that adds faces to COCO.

I’m a masters student in the UK hoping to get 1-2 years of research experience at the intersection of machine learning and neuroscience (so not so much image recognition, speech recognition, etc.)

The two academic options that immediately came to mind were the Gatsby Comp. Neuroscience at UCL and Cambridge’s Computational Biological Learning group. I’m not familiar with industry labs and would like your help in comparing Deepmind, Google Brain (UK site), Microsoft Research Cambridge, etc. Which one of these offers the most relevant research in terms of machine learning applied to neuroscience as I am hoping to enter academia for computational neuroscience? A lab that publishes well is also a nice plus as always. Thanks.