For the GLOW experiment we stacked three GLOW transformations at different scales eachwitheightaffinecoupling blocks spaced byactnorms andpermutations each parameterized byaCNN with twohidden layers with 512 filters each. In a recent arXiv submission, Arjovsky et al.[2] suggested that in the presence of an observable variability intheenvironmente(e.g. While this procedure workedondistributions that were very similar tobegin with, inthe majority of cases the log-likelihood fit toB did not provide informative gradients when evaluated on the transformed dataset, as the KL-divergence between distributions with disjoint supports is infinite. The code is available in lrmf_gradient_simulation.ipynb. LRMF objective(Eq 2) decreases over time and reaches zero when two datasets are aligned.

Attached IPython notebooks were tested to work as expected in Colab. On replacing the Gaussian prior with a learned density in normalizing flows. As mentioned in the main paper, FFJORD LRMF performed on par with Real NVP version. The dynamics can be found in the Figure 10. Rightmost column shows LRMF convergence.

We train machine learning models to predict the order of the Shafarevich-Tate group of an elliptic curve over $\mathbb{Q}$. Building on earlier work of He, Lee, and Oliver, we show that a feed-forward neural network classifier trained on subsets of the invariants arising in the Birch--Swinnerton-Dyer conjectural formula yields higher accuracies ($> 0.9$) than any model previously studied. In addition, we develop a regression model that may be used to predict orders of this group not seen during training and apply this to the elliptic curve of rank 29 recently discovered by Elkies and Klagsbrun. Finally we conduct some exploratory data analyses and visualizations on our dataset. We use the elliptic curve dataset from the L-functions and modular forms database (LMFDB).

This repository shares example code and example prompts for accomplishing common tasks with the OpenAI API. To try these examples yourself, you'll need an OpenAI account. Create a free account to get started. Most code examples are written in Python, though the concepts can be applied in any language. Use them as starting points upon which to elaborate, discover, and invent.

These days I get pinged almost every week, and often more frequently than that, about another paper that just came out claiming a new approach to Neural Networks for tabular data that, among other things, handily beats XGBoost. I've now come to consider this entire genre of ML "research" as tantamount to quackery, due to the really poor quality of considerations that go into most of the work in this category. So I usually don't even bother to check the claims. Nonetheless, there is this little voice in my head that goes something like "well Bojan, maybe you are overreacting, and maybe you are lettign your own biases get the best of you. Why don't you take a look and see what's going on." So this week I did exactly that.

Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. However, the prediction performance (accuracy, recall, precision), usability, and efficiency of existing methods still have much room to be improved. Here, we report ECRECer, a cloud platform for accurately predicting EC numbers based on novel deep learning techniques. To build ECRECer, we evaluate different protein representation methods and adopt a protein language model for protein sequence embedding. After embedding, we propose a multi-agent hierarchy deep learning-based framework to learn the proposed tasks in a multi-task manner. Specifically, we used an extreme multi-label classifier to perform the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against four representative methods demonstrate that ECRECer delivers the highest performance, which improves accuracy and F1 score by 70% and 20% over the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous enzymes in the Swiss-Prot database with incomplete EC numbers to their full fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-), ECRECer annotated it with "1.14.11.38", which supported by further protein structure analysis based on AlphaFold2. Finally, we established a webserver (https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve usability.

Entering the 22nd of 150 epochs after 10 hours of training, I realized the 3000 wav file dataset was a bit tough to swallow for my 5 year old MacBook Pro. The Free Spoken Digit Dataset contains recordings from 6 speakers and 50 of each digit per speaker in 8kHz .wav As I was following along the outstanding video series on Sound Generation With Neural Networks by Valerio Velardo, I found myself stuck in an endless training phase. The goal is to train a custom-made Variational Auto-Encoder to generate sound digits. The preprocessing of the FSDD wav files was performed locally and generated a training dataset of 3000 spectrograms in .npy

The main aim of the project is to create amazing line art portraits. Lets cartoonize the lineart portraits, its still in the making but have a look at some pretty pictures. The amazing results that the model has produced has a secret sauce to it. The initial model couldn't create the sort of output I was expecting, it mostly struggled with recognizing facial features. I wanted to break-in and produce results that could recognize any pose.

This is the official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Please refer to section below for Citation details. Create a new conda environment (Python 3.7): Please refer to cairosvg's documentation for additional requirements of CairoSVG. If this is not working for you, download the dataset manually from Google Drive, place the files in the dataset folder, and unzip (this may take a few minutes). NOTE: The icons_tensor/ folder contains the 100k icons in pre-augmented PyTorch tensor format, which enables to easily reproduce our work.

This project is an example demonstrating how to use Python to train two different machine learning models to detect anomalies in an electric motor. The first model relies on the classic machine learning technique of Mahalanobis distance. The second model is an autoencoder neural network created with TensorFlow and Keras. Data was captured using an ESP32 and MSA301 3-axis accelerometer taped to a ceiling fan. Each sample is about 200 samples of all 3 axes captured over the course of 1 second.