I’m trying to compare the predictive accuracy of few regression models. For simplicity, let’s say that I have a polynomial of degree 6 and a GAM model with many knots. One simple approach would be to compare the RMSE and/or MAE. In this case, the GAM model has a lower RMSE and MAE than those of the polynomial model, but the difference is small. Now based on the RMSE and MAE values, I should choose the GAM model, but the small difference is making me question whether it makes sense to take the GAM model over the simpler polynomial model.

Searching around, I found that one can use the Diebold-Mariano (or the similar HLN) test to compare the predictive accuracy of two forecasts in time series. The DM/HLN tests determine whether there is any significant difference between the forecasts. However, I think it would not be appropriate to use the DM/HLN-test in my case, since the tests compute autocovariance at lags in order to derive the test-statistic and that would make little sense in the context of non-time series forecasts.

Are there any similar tests that can be used for non-time series forecasts?

I’ve recently encountered some of the point cloud to persistence diagram representations in time series analysis, and they seem very interesting, if not familiar to those of us who ended up here from a computational/algorithmic background.

I’ve so far primarily encountered these topics written on in, naturally, topology and analysis settings, but was wondering if there’s yet been heavy adaptation in the ML sphere? With an influx of interest in geometric/topological domains these days, I would think this would be a popular topic.

So I suppose that my objective is to learn from more informed people on some of the relevant existing work in ML, as well as what we know of the strengths and criticisms regarding these representations of time series.

I have a project to detect if myself or guests are present in a given room using a picamera. Does anyone know of any pretrained human presence detection models available?

Paper – Code

tl;dr: Instance Segmentation slow, YOLACT make fast (29.8 COCO mAP, 33.5 Titan Xp fps).

Hi all, my paper was recently accepted ICCV 2019 Oral so I thought I’d post it here. (Note: fps numbers were rebenchmarked for ICCV and I haven’t updated it elsewhere).

Today, object detection has several methods that do well (e.g., Faster R-CNN+++, RetinaNet), and several that do well enough but are also fast (e.g., YOLOv2-3, SSD). On the other hand, the same isn’t true for instance segmentation. We have good methods (e.g., Mask R-CNN and its derivatives, Retina-Mask), but no fast methods that do well enough on a complex dataset like COCO.

YOLACT changes this. We obtain 29.8 mAP (30.1 after a stupid bug fix, but the paper’s out now >.>) on COCO at 33.5 fps on a single Titan Xp, making YOLACT the best fast instance segmention method out at the moment. And it’s simple: predict a set of k basis masks (prototypes) over the whole image and in parallel predict a set of k linear combination coefficients (mask coefficients) for each detection. Then to generate masks for a detection, just multiply the mask coefficients into the prototypes and add (which can be implemented as one matrix multiplication per image). This whole process takes ~5-6 ms to add a masks to any existing object detector.

I also came up with “Fast NMS”, a close approximation to traditional per-class NMS that’s 12ms faster.

Feel free to AMA.

I’m working on a data labeling project to assist with the labeling and sharing of datasets. The goal is to minimize your time scraping the web for images and labeling data manually. Why let your side projects go to waste when you can share your datasets with others? I have included an AI powered annotation tool as well.

Do check out https://hungryai.com/home and let me know what you think.

Join Mathew Salvaris and Ilia Karmanov an on a lively discussion about all things deep learning. I was blown away by these two incredibly talented data scientists. Nothing inspires me more than having a conversation with people who are literally 10 times smarter than me. We discuss Mat’s work on building out patterns for distributed deep learning on Azure. Ilia discusses the latest and greatest on video action detection. We talk about computer vision, interpretability, robustness, ML engineering and the democratisation of deep learning. Finishing off we discuss where the deep learning space is going in the next 5 years!

https://youtu.be/Zw_h1h0f_qA

Has anyone made an AI trained on hand writing to generate a specific sentience?
If not how would one go about making this?

Hello guys,

I have recently implemented two papers about attention-based BatchNormalization.

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2) Attentive Normalization :

Arxiv link : https://arxiv.org/abs/1908.01259

Official Pytorch implementation : Not yet released but will be available here https://github.com/ivMCL/AttentiveNorm

My Keras implementation : https://github.com/Cyril9227/Keras_AttentiveNormalization

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2) Instance Enhancement Batch Normalization :

Arxiv link : https://arxiv.org/abs/1908.01259

Official Pytorch implementation : https://github.com/gbup-group/IEBN

My Keras implementation : https://github.com/Cyril9227/Keras_IEBN

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Both implementations work as a simple droppin replacement of standard BatchNorm layer. Any feedbacks are welcome !

Thank’s 🙂

I am from Germany and I looked for jobs both here in Germany and USA for Deep Reinforcement Learning positions. Every single position I’ve found required a Ph.D. I understand why, the field is new and mostly academic / research work. Still I wonder if anyone has any information about getting a job maybe not as a researcher scientist (where Ph.D. would be required) but maybe as a research engineer when having a M.Sc degree? As a research engineer you implement papers to solve current problems. The question is, is there any hope for the field of Deep Reinforcement Learning currently? I know for “classic” Deep Learning (supervised etc) such positions exist, but I am very interested in deep RL.

I am nearning the end of my M.Sc. in robotics with the master thesis being on Deep Learning. I am teaching myself deep RL on my free time and would like to pursue a career in that field. I find RL and agents interacting with the environment fascinating.

Would like to hear your opinion.