I’m writing my MSc thesis and would like to use a couple of images from a NeurIPS paper. What should I follow to avoid copyright issues? I can’t find any licensing information on their website

What’s everyone reading atm about ML?

Hey,

Long-time lurker here 🙂

I’m working on a project in the UK and USA on building medical decision assistance to help doctors in hospitals with management decisions. The overarching aim is to build medical assistants that can help doctors deliver ideal medical care anywhere on the planet (or even space). These assistants will one day ensure that the clinicians taking care of us make fewer mistakes, deliver multi-specialty expert care (rather than bouncing between single-specialty doctors who can’t put everything together), understand and appraise the evidence and apply it us as individuals AND reduce the cost of medical care.

A little about me: I am a hospital physician with an engineering and technical background. I also advise the Centres for Disease Control and Prevention initiative on digitizing medicine and building clinical decision assistance.

The project is focusing initially on building assistance for stroke hyperacute thrombolysis decisions. Reasons for this choice include: it’s a binary decision (to give or not to give), has relatively few research papers to understand, access to domain knowledge, access to data and access to testing sites.

ELI5: A stroke is when an area gets damaged in the brain due to a blockage in the blood flow to that area of the brain, usually in the form of a blood clot. In some circumstances, it is possible to unblock and prevent brain damage by dissolving the clot with a strong unblocking drug (or sometimes fishing the clot out with a wire). We want to see if we can train a program that can help doctors make better-informed decisions when deciding what treatment to give the patient.

I am hoping to get together a small group to consult if anyone is interested in applying ML in the medical field. Thanks!

Hello, I work with machine learning, and I have personal projects. However, I don’t know how to presentation these projects in GitHub. I would emphasis on Jupyter Notebook only or using some template likes Cookiecutter. Have you been watching some users or repository that has a good organization on ML Projects?

Lately, I have been reading some of the papers from Vicarious Inc. Some of you may have read about the (over-hyped) Recursive Cortical Network (RCN) or Schema Network. Some of you found it very infuriating, while others were skeptical about the company.

I will be honest, the papers they present were good and bad in their own ways. The RCN paper does prove that generative modelling is superior to traditional CNNs. Then again, they did not test it on a major dataset like Imagenet (the compilers of the Omniglot dataset performed their own experiments using the RCN recently). The Schema Network sounded revolutionary when it performed “zero-shot knowledge transfer” using a model based algorithm. They never benchmarked meta-reinforcement learning algorithms. On top of that, they did not use the RCN to parse proper entities when playing breakout (it was mentioned that they assumed an object based vision system, referring to their generative vision model). I find their breakthroughs interesting, but I also feel that they are way too hyped by the media when other attention deserving work remain lost on arxiv. What do you guys think?

There’s a fair numbers of papers that been withdrawn from ICLR yesterday and today, including many with 6/3/3 scores which isn’t that bad. At first I assumed they would be submitted to CVPR, but many of these papers are on optimization, robustness, pruning and other topics which I’d guess are not very suitable for CVPR.

Where are these papers being sent to? I got bad scores on one of my papers and would like to submit it to another conference, but I don’t think CVPR makes sense for me specifically.

So, the idea is simple: feed bert context vector to gru layer.

class Model(torch.nn.Module): def __init__(self): super(Model, self).__init__() self.bert = BertModel.from_pretrained('bert-base-uncased') freeze(self.bert) self.rnn = torch.nn.GRU(768, 128, 1, batch_first=True, bidirectional=False) self.linear = torch.nn.Linear(128, 5) def forward(self, x, lengths): x, _ = self.bert(x) x = torch.nn.utils.rnn.pack_padded_sequence( x, lengths, batch_first=True) self.rnn.flatten_parameters() _, x = self.rnn(x) x = self.linear(x.squeeze(0)) return x 

But model loss do not decrease.

Here is Colab link

Has anyone here done structured pruning for object detection tasks and managed to achieve good results (minimal time with maximum accuracy)?

https://openreview.net/forum?id=BJe_z1HFPr

Abstract: In this paper, we present a deep convolutional neural network (CNN) which performs arbitrary resize operation on intermediate feature map resolution at stage-level. Motivated by weight sharing mechanism in neural architecture search, where a super-network is trained and sub-networks inherit the weights from the super-network, we present a novel CNN approach. We construct a spatial super-network which consists of multiple sub-networks, where each sub-network is a single scale network that obtain a unique spatial configuration, the convolutional layers are shared across all sub-networks. Such network, named as Resizable Neural Networks, are equivalent to training infinite single scale networks, but has no extra computational cost. Moreover, we present a training algorithm such that all sub-networks achieve better performance than individually trained counterparts. On large-scale ImageNet classification, we demonstrate its effectiveness on various modern network architectures such as MobileNet, ShuffleNet, and ResNet. To go even further, we present three variants of resizable networks: 1) Resizable as Architecture Search (Resizable-NAS). On ImageNet, Resizable-NAS ResNet-50 attain 0.4% higher on accuracy and 44% smaller than the baseline model. 2) Resizable as Data Augmentation (Resizable-Aug). While we use resizable networks as a data augmentation technique, it obtains superior performance on ImageNet classification, outperform AutoAugment by 1.2% with ResNet-50. 3) Adaptive Resizable Network (Resizable-Adapt). We introduce the adaptive resizable networks as dynamic networks, which further improve the performance with less computational cost via data-dependent inference.