https://pytorch.devpost.com/submissions

You can ‘Like’ any number of submission, and talk to the submission authors by directly commenting on their project. Each project has an interactive demo and video.

This specification describes how a system implemented as computer programs on one or more computers in one or more locations can generate a sequence of audio data that includes a respective audio sample at each of multiple time steps . For example , the sequence of audio data can represent speech in a particular natural language or a piece of music.

https://patentimages.storage.googleapis.com/1b/d3/ac/60abc0081cdf1b/US20190251987A1.pdf

It seems that this patent covers Wavenet. It has the iconic diagram demonstrating dilated convolutions and the equations for the layers.

Currently looking for either open source projects or paid solution that can be used for an Entity Matching problem.

Example:

Entity 1, name: aad, address: aacb, phone : 123

Entity 2, name: aad, address: aacb 1, phone: +4 123

Should be matched as same entity.

So far I have only found this open source solution: https://github.com/anhaidgroup/deepmatcher

Are there more open sourced solutions?

Also, are there any paid solutions for this problem?

I know how I could approach the problem myself, but before I do I want to see what is current SOTA.

Thank you.

I’d like to implement a simple dot-product attention mechanism such that the output at each timestep is computed by attending to the preceding L elements. This is similar to the standard setup for autoregressive attention, but differing in that only a fixed window is attended to at each timestep.

Suppose we are training on sequences of length N and want to compute attention over windows of L elements. The options that I can think of are:

1. Compute all N² elements of the attention matrix and apply a mask so that only the N*L elements of interest are used. This is inefficient for L<<N and often impractical for large N due to memory constraints.
2. Manually window the inputs into overlapping sequences of length L, then apply attention to each window. This only requires N*L dot products, but involves tiling/repeating the inputs (attention keys/values) L times which is impractical for large L.
3. Manually loop over N and L and individually compute each of the N*L dot products. This is efficient in an algorithmic sense but practically will be terrible if implemented using a high-level DL library.

My question is whether or not this operation can be efficiently computed with high-level DL libraries.

Often 3D object detection models are benchmarked on one dataset e.g. KITTI, and this dataset is captured with one particular sensor rig (camera and/or LIDAR).

Is there work to adapt/generalize model trained with dataset from one sensor rig to other sensor rig?

I’ve been working on a dataset, something that I’ve never worked on before.

I am in the process of building a classification model which can separate 2 classes using 2 continuous features & around 10-15 multiclass categorical features. The classes are heavily imbalanced (3:1 ratio) & I have over 500k observations.

I’ve tried a few methods like downsampling, class balancing along with a few algorithms like Logistic Regression, KNN, Random Forest, a few Gradient Boosting algorithms etc.. All these models are giving me poor results.

I am working locally & don’t have access to a cloud service, hence I’m not keen on using NNs or SVMs which tend to be more computationally expensive.

What else can I do?

Thanks

Imagine we have a training set of real 3D models (by this I mean, for example, 3D models of a rooms in buildings), and with this training set, we train a neural network to understand the relationship between a voxel and the 3D model in which it resides (I hypothesize that something like light would be a factor). Then, we use our neural network to predict one voxel outside of the captured (by a camera) 3D model. Is this possible? Would the desired results be achieved?

Lately in my work and personal research, I have noticed that whenever I google anything related to ML/AI/DS there is a whole page of medium/tds articles. Some of these are actually useful but more often than not they’re garbage and doing a really bad job of presenting a topic of interest.

Do other people have a way of weeding through these? I think Medium should introduce some sort of usefulness tagging mechanism to actually tell if an article does a good job in explaining a concept or not. These days every ‘data scientist’ with little to no communication skills is writing Medium posts.

Hi everyone! I found this provocative post on HN, which is quite related also to this other Reddit post (on how the objective of Kaggle competitions from the competition creator’s perspective isn’t necessarily a useful model), I’m curious to know your thoughts.

As far as I understand BERT can work as kind of embedding but context-sensitive.

Can I use pretrained BERT like pretrained embedding in my model?

If I can, what simplest way to do so?

In general, I want to make something like a context-sensitive replacement for char/word lvl default embeddings for my models.