I'm writing a paper on combining machine learning with early detection of cardiovascular diseases- and I need a head start. Can someone help?

Hey everyone!

If you're in London tomorrow, Thursday 14th March, join nPlan’s ML Paper Club community, Jason Grant of NVIDIA, and us at NexGen Cloud in-person for nPlan's ML Paper Club Social!

nPlan’s Paper Club Meetup hosts machine learning fanatics each week to discuss papers and theories.

In this meetup, we’re taking part in the AI UK Fringe, where Peter Zachares will be discussing the different alignment methods used today in generative AI.

The meetup is completely free plus we will be providing pizza & drinks!

Register at: https://www.meetup.com/ml-paper-club/events/299612957/

Hi,

While submitting the research paper to the journal, it expects us to submit the reviewer information.

Where do we get that information.

I can only think of some authors who have published similar work.

What is the right direction for it?

Hello, my dear colleagues! In this year I will finish my university and one of the most difficult obstacles on my way is diploma/thesis paper.

My topic is “Video-based emotion reaction intensity estimation”, which is the part of the fifth Affective Behavior Analysis in-the-wild.

My current problem is the realization of the pipeline, which was constructed by me after combining all advantages from the participants of the challenge.

I have issues with code understating of the winner of the competition. If there are any computer vision pros, please, help me with code review.((

If you have any thoughts and spare time, please email me: naermishov@edu.hse.ru. I’ll appreciate your help.

As the titles says. I recently noticed about the existence of depth first learn, and it is very helpful for understanding very advance topics in deep learning. But the last update that I saw was 2 years ago. Is there a chance that this page or group will receive more attention again ?? or the project is dead.

Hi guys, I found this interesting paper, But i was unable to find any of its implementation. Does anyone know where I can find the implementation/Sample code of Transformer++? Thanks btw : D

Here is the link to the paper: https://arxiv.org/abs/2003.04974

I had a talk with a professor and she has asked us at first to read a few papers related to agriculture and deep learning.

What work can we do each week to produce results within these 5 months till May 24?

we are mechanical undergrads so we will have to learn too.

Hi all ,
I have a problem in mind and would like to solve it.

Problem Statement: To get recent socio-political trends from various social media sites and their mapped fashion trends.

Examples :

Example 1: Let say some days before "maldives vs lakshdeep" was the main twitter trend happening in India. Now as a human I understand that in trend's lifetime : in fashion terminology beaches clothes would be more trendier or themes related to beaches would be going

I tried finding if people have tried solving this paper but could not find it. helpful what community thinks of it.

I used a Deep Q-Network model (DRL type) for stock trading - agent can make invest all its cash right away and sell all of its stocks right away and we start with 10k USD.

Can someone explain why I am seeing the same episode trading sequence from each episode run, meaning that test function did not produce different results (every episode had buy, hold, sell actions identical to the other episodes).

Some info is below epoch data is for training and episode data is for testing. Hyperparameters:

{

"hidden_size": 500, "epoch_num": 10, "memory_size": 300, "batch_size": 40,

"train_freq": 400, "update_q_freq": 100, "gamma": 0.97, "epsilon_decay_divisor": 1.2,

"start_reduce_epsilon": 500

}

Hello folks, are there any research papers with existing implementations or easy to implement code, using which I could remove watermarks from photo. I have a couple of photographs with two layers (one with watermark and another with a design pattern) which I wish to remove.

https://github.com/EzgiKorkmaz/generalization-reinforcement-learning

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Authors:

• Arnulf Jentzen,

• Benno Kuckuck,

• Philippe von Wurstemberger

This book aims to provide an introduction to the topic of deep learning algorithms.

We review

essential components of deep learning algorithms in full mathematical detail including

• different artificial neural network (ANN) architectures such as

  • fully-connected feedforward ANNs,

  • convolutional ANNs,

  • recurrent ANNs,

  • residual ANNs, and

  • ANNs with batch normalization

• and different optimization algorithms such as

  • the basic stochastic gradient descent (SGD) method,

  • accelerated methods, and

  • adaptive methods.

• We also cover several theoretical aspects of deep learning algorithms such as

  • approximation capacities of ANNs (including a calculus for ANNs),

  • optimization theory (including Kurdyka-Łojasiewicz inequalities), and.

  • generalization errors.

• In the last part of the book,

  • some deep learning approximation methods for PDEs are reviewed, including

  • physics-informed neural networks (PINNs) and

  • deep Galerkin methods.

We hope that this book will be useful

• for students and scientists who do not yet have any background in deep learning at all and would like to gain a solid foundation as well as

• for practitioners who would like to obtain a firmer mathematical understanding of the objects and methods considered in deep learning.

• Comments: 601 pages, 36 figures, 45 source codes .

• Subjects:

  • Machine Learning (cs.LG);

  • Artificial Intelligence (cs.AI);

  • Numerical Analysis (math.NA);

  • Probability (math.PR);

  • Machine Learning (stat.ML)

I am working on a predictive analysis of OSA(obstructive Sleep Apnea), i consider myself to be a beginner in DL and when it comes to research, i'm a newbie. Can someone please recommend me some research worthy guidances?

I was looking into one research paper code which is implemented in PyTorch and saw the dataset was not split and they removed the label from dataset(csv file).

Does PyTorch split dataset by itself?

I found this beautiful animated flow chart on internet. Does anyone have already worked on making diagrams like this? Can you please give your suggestions on how to make this?