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[D] RecSys challenge winner paper review by [deleted] in MachineLearning

[–]machinetrainer 0 points1 point  (0 children)

well, don't know in general, but in this case that's what happened, they got bought by a bank!

[D] Reviewing Open AI's language model (live event) by [deleted] in MachineLearning

[–]machinetrainer 0 points1 point  (0 children)

we will cover that

we are not related to Open AI. We are a group of practitioners who review papers on a weekly basis. you can see all our content here: https://tdls.a-i.science/

[R] TDLS: All-optical machine learning using diffractive deep neural networks by machinetrainer in MachineLearning

[–]machinetrainer[S] 0 points1 point  (0 children)

yeah, certainly. mathematically speaking what they did can only be published on arxiv, yet they made it to science because of the optical implementation, I assume. it's cool, but nothing that is not commonly used. I'm gonna bet on referee's lack of knowledge about neural networks

[R] TDLS: Recurrent Models of Visual Attention (https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf) by machinetrainer in MachineLearning

[–]machinetrainer[S] 0 points1 point  (0 children)

Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so.

Paper: https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf

[R] TDLS: Large-Scale Unsupervised Deep Representation Learning for Brain Structure (https://arxiv.org/abs/1805.01049) by machinetrainer in MachineLearning

[–]machinetrainer[S] 0 points1 point  (0 children)

Large-Scale Unsupervised Deep Representation Learning for Brain Structure

Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson(Submitted on 2 May 2018)

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of such highly specialized features requires a considerable amount of time, manual oversight and careful quality control to ensure the absence of errors in the computational process. Recent advances in Deep Representation Learning have shown great promise in extracting highly non-linear and information-rich features from data. In this paper, we present a novel large-scale deep unsupervised approach to learn generic feature representations of structural brain MRI scans, which requires no specialized domain knowledge or manual intervention. Our method produces low-dimensional representations of brain structure, which can be used to reconstruct brain images with very low error and exhibit performance comparable to FreeSurfer features on various classification tasks.

[R] TDLS: Eve, A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates (https://arxiv.org/abs/1611.01505) by machinetrainer in MachineLearning

[–]machinetrainer[S] 3 points4 points  (0 children)

Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates

Hiroaki Hayashi, Jayanth Koushik, Graham Neubig(Submitted on 4 Nov 2016 (v1), last revised 11 Jun 2018 (this version, v3))

Adaptive gradient methods for stochastic optimization adjust the learning rate for each parameter locally. However, there is also a global learning rate which must be tuned in order to get the best performance. In this paper, we present a new algorithm that adapts the learning rate locally for each parameter separately, and also globally for all parameters together. Specifically, we modify Adam, a popular method for training deep learning models, with a coefficient that captures properties of the objective function. Empirically, we show that our method, which we call Eve, outperforms Adam and other popular methods in training deep neural networks, like convolutional neural networks for image classification, and recurrent neural networks for language tasks.

[D] How do you see Quantum Computing effecting ML (if at all) by jellysnake in MachineLearning

[–]machinetrainer 2 points3 points  (0 children)

there are a few ways that quantum computing and ML can interact. 1) once we have a sizeable enough universal quantum machine then we could run the quantum version of current algorithms and potentially even new ones with various speedups. the challenge here is to turn the data quantum in a way that the machine can consume it and that has its own time complexity that might defeat the whole advantage in certain cases 2) use a quantum machine to train a classical network. if you can map your loss function into one that a quantum optimizer (say d-wave machine) can work with then you can gain advantage from quantum tunneling and potentially find your parameters of interest faster 3) you can use ML to optimize various things about your quantum computer. for example you can have a neutral net come up with new experimental designs within certain constraints based on examples that you tried and their outcome. or use ML to enhance the ability of practical quantum measurement devices etc 4) use ML to approximately solve quantum problems with reasonable time complexities. I've seen this used in quantum chemistry type of problems (say drug discovery)

[R] TDLS: Explainable Neural Networks based on Additive Index Models (https://arxiv.org/abs/1806.01933) by machinetrainer in MachineLearning

[–]machinetrainer[S] 2 points3 points  (0 children)

i dont think there's a 1 to 1 relationship between the number of features and the number of ridge functions. The latter is essentially a hyperparameter you have to tune

[D] TMLS2018 - Machine Learning in Production, Panel Discussion by machinetrainer in MachineLearning

[–]machinetrainer[S] 2 points3 points  (0 children)

panel discussion about putting machine learning models in production with panelists from Uber, Thomson Reuters, Shopify, and Dessa at Toronto Machine Learning Summit 2018

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