A Twitter discussion has brought to our attention that an ICML2021 paper, “Momentum Residual Neural Networks” (by Michael Sander, Pierre Ablin, Mathieu Blondel and Gabriel Peyré) has allegedly been plagiarized by another paper, “m-RevNet: Deep Reversible Neural Networks with Momentum” (by Duo Li, Shang-Hua Gao), which has been accepted at ICCV2021.
The main figures of both papers, look almost identical, and the authors of the ICML2021 paper wrote a blog post that gathered a list of plagiarism evidence: https://michaelsdr.github.io/momentumnet/plagiarism/
See the comparison yourself:
“Momentum residual neural networks” (https://arxiv.org/abs/2102.07870)
“m-RevNet: Deep Reversible Neural Networks with Momentum” (https://arxiv.org/abs/2108.05862)
I assume that the ICCV2021 committee has been notified of this, so we will need to see what the final investigation results are from program chairs.
https://reddit.com/link/ipatwx/video/ofds2c09f2m51/player
Day 159.
Today's post is a 5-minute summary of the NLP paper "ICD Coding From Clinical Text Using Multi-Filter Residual Convolutional Neural Network".
Today's paper uses multi-filter and residual CNN to achieve SOTA results in ICD coding. Check it out below:
Best,
Ryan
They all sound the same and my limited knowledge can't really differentiate between them since it seems like the general idea of what they're trying to accomplish is the same.
Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and provide insights of the feature learning process.
http://ieeexplore.ieee.org/document/8275035/
Hope this is the right place to post a question like this. I have been reading up on resnets and am starting to get an ok understanding of them. I've found many sources that have solid implementation details about the architecture and the forward pass. However, in pytorch the backward pass is simply done with a call to backward(). I would like to know how that actually works in these cases. My first guess is that because the skip connection function is just an addition, gradient of 1, the backprop gradient calculated is the same with or without the skip connections, but that really doesn't sound right.
And it works very well. Looking for new ideas to keep this work going.
In short: I've made a neural network that predicts with 99% accuracy whether a subject is thinking about moving the right fist, the left fist, both fists, both feet, or his own stuff.
I am open to new ideas and discussions.
Paper: https://iopscience.iop.org/article/10.1088/1741-2552/ac4430
