Top 10 Arxiv Papers Today


2.523 Mikeys
#1. Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
Recent works on implicit regularization have shown that gradient descent converges to the max-margin direction for logistic regression with one-layer or multi-layer linear networks. In this paper, we generalize this result to homogeneous neural networks, including fully-connected and convolutional neural networks with ReLU or LeakyReLU activations. In particular, we study the gradient flow (gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time. We also formulate a natural constrained optimization problem related to margin maximization, and prove that both the normalized margin and its smoothed version converge to the objective value at a KKT point of the optimization problem. Furthermore, we extend the above results to a large family of loss functions. We conduct several experiments...
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BrundageBot: Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. Kaifeng Lyu and Jian Li https://t.co/iFhdDqZKgH
arxivml: "Gradient Descent Maximizes the Margin of Homogeneous Neural Networks", Kaifeng Lyu, Jian Li https://t.co/rQ3HtB4iIT
StatsPapers: Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. https://t.co/Wq8DGus1Zq
Github

Robust evasion attacks against neural network to find adversarial examples

Repository: nn_robust_attacks
User: carlini
Language: Python
Stargazers: 343
Subscribers: 9
Forks: 102
Open Issues: 3
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Authors: 2
Total Words: 19227
Unqiue Words: 3404

2.192 Mikeys
#2. Grounding Object Detections With Transcriptions
Yasufumi Moriya, Ramon Sanabria, Florian Metze, Gareth J. F. Jones
A vast amount of audio-visual data is available on the Internet thanks to video streaming services, to which users upload their content. However, there are difficulties in exploiting available data for supervised statistical models due to the lack of labels. Unfortunately, generating labels for such amount of data through human annotation can be expensive, time-consuming and prone to annotation errors. In this paper, we propose a method to automatically extract entity-video frame pairs from a collection of instruction videos by using speech transcriptions and videos. We conduct experiments on image recognition and visual grounding tasks on the automatically constructed entity-video frame dataset of How2. The models will be evaluated on new manually annotated portion of How2 dev5 and val set and on the Flickr30k dataset. This work constitutes a first step towards meta-algorithms capable of automatically construct task-specific training sets.
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This repository contains code and metadata of How2 dataset

Repository: how2-dataset
User: srvk
Language: Python
Stargazers: 29
Subscribers: 6
Forks: 3
Open Issues: 0
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Authors: 4
Total Words: 5891
Unqiue Words: 1905

2.192 Mikeys
#3. Machine Learning Approach to Earthquake Rupture Dynamics
Sabber Ahamed, Eric G. Daub
Simulating dynamic rupture propagation is challenging due to the uncertainties involved in the underlying physics of fault slip, stress conditions, and frictional properties of the fault. A trial and error approach is often used to determine the unknown parameters describing rupture, but running many simulations usually requires human review to determine how to adjust parameter values and is thus not very efficient. To reduce the computational cost and improve our ability to determine reasonable stress and friction parameters, we take advantage of the machine learning approach. We develop two models for earthquake rupture propagation using the artificial neural network (ANN) and the random forest (RF) algorithms to predict if a rupture can break a geometric heterogeneity on a fault. We train the models using a database of 1600 dynamic rupture simulations computed numerically. Fault geometry, stress conditions, and friction parameters vary in each simulation. We cross-validate and test the predictive power of the models using an...
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arxivml: "Machine Learning Approach to Earthquake Rupture Dynamics", Sabber Ahamed, Eric G. Daub https://t.co/MKdmAJOH25
Memoirs: Machine Learning Approach to Earthquake Rupture Dynamics. https://t.co/OG0LS93W0l
Github

This repository contains python codes to classify earthquake rupture based on random forest and neural network.

Repository: machine_learning_earthquake_rupture
User: msahamed
Language: Jupyter Notebook
Stargazers: 0
Subscribers: 1
Forks: 4
Open Issues: 0
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Authors: 2
Total Words: 9626
Unqiue Words: 2640

2.191 Mikeys
#4. A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
One of the key drawbacks of 3D convolutional neural networks for segmentation is their memory footprint, which necessitates compromises in the network architecture in order to fit into a given memory budget. Motivated by the RevNet for image classification, we propose a partially reversible U-Net architecture that reduces memory consumption substantially. The reversible architecture allows us to exactly recover each layer's outputs from the subsequent layer's ones, eliminating the need to store activations for backpropagation. This alleviates the biggest memory bottleneck and enables very deep (theoretically infinitely deep) 3D architectures. On the BraTS challenge dataset, we demonstrate substantial memory savings. We further show that the freed memory can be used for processing the whole field-of-view (FOV) instead of patches. Increasing network depth led to higher segmentation accuracy while growing the memory footprint only by a very small fraction, thanks to the partially reversible architecture.
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c_f_baumgartner: More exciting work from @bmic_eth accepted to @miccai2019! Master student Robin Bruegger used reversible units to develop a class of extremely memory-efficient 3D segmentation networks of almost unlimited depth. Pre-print: https://t.co/YrOAYVF8UP Code: https://t.co/6ThhPGHnNK https://t.co/Nz9gQSDfiP
c_f_baumgartner: The non-PDF link is here https://t.co/n9cZe5h6qO for those who prefer this.
arxiv_cscv: A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation https://t.co/OZkxjfL2Se
Github

Framework for creating (partially) reversible neural networks with PyTorch

Repository: RevTorch
User: RobinBruegger
Language: Python
Stargazers: 0
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Forks: 0
Open Issues: 0
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Authors: 3
Total Words: 3227
Unqiue Words: 1230

2.191 Mikeys
#5. Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses
Danilo Vasconcellos Vargas, Shashank Kotyan
In adversarial machine learning, there are a huge number of attacks of various types which makes the evaluation of robustness for new models and defenses a daunting task. To make matters worse, there is an inherent bias in attacks and defenses. Here, we organize the problems faced (model dependence, insufficient evaluation, unreliable adversarial samples and perturbation dependent results) and propose a dual quality assessment method together with the concept of robustness levels to tackle them. We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness. Moreover, we show that robustness to $L_0$ and $L_\infty$ attacks differ greatly and therefore duality should be taken into account for a correct assessment. Interestingly, a by-product of the assessment proposed is a...
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BrundageBot: Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses. Danilo Vasconcellos Vargas and Shashank Kotyan https://t.co/QyKqJQPapE
Memoirs: Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses. https://t.co/E1OfzzS2Xl
onNewHorizons: It is hard to evaluate the robustness of DNNs. Too many attacking methods and different results based on different amount of perturbation. We proposed a model agnostic dual assessment and the concept of robustness levels to enable this to happen. Today on https://t.co/2u0XptIbnF https://t.co/CA7nU8k7Ez
onNewHorizons: It's hard to evaluate the robustness of DNNs. Each attacking method will output a different amount of perturbation and accuracy while many won't work for hybrid models. Proposed Solution: a model agnostic dual assessment and robustness levels. Today on https://t.co/r2FZcxVotw #ML https://t.co/C0UW35lJON
arxivml: "Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks… https://t.co/zYpQf34h44
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Total Words: 0
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2.174 Mikeys
#6. Deep Learning Development Environment in Virtual Reality
Kevin C. VanHorn, Meyer Zinn, Murat Can Cobanoglu
Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep learning more accessible and intuitive, we have built a virtual reality-based DL development environment. Within our environment, the user can move tangible objects to construct a neural network only using their hands. Our software automatically translates these configurations into a trainable model and then reports its resulting accuracy on a test dataset in real-time. Furthermore, we have enriched the virtual objects with visualizations of the model's components such that users can achieve insight about the DL models that they are developing. With this approach, we bridge the gap between professionals in different fields of expertise while offering a novel perspective for model analysis and data interaction. We...
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BrundageBot: Deep Learning Development Environment in Virtual Reality. Kevin C. VanHorn, Meyer Zinn, and Murat Can Cobanoglu https://t.co/COIYv3pIwf
arxivml: "Deep Learning Development Environment in Virtual Reality", Kevin C. VanHorn, Meyer Zinn, Murat Can Cobanoglu https://t.co/0SB2uEn4Ea
Memoirs: Deep Learning Development Environment in Virtual Reality. https://t.co/MTWZgVPcf3
colourspeak: RT @yapp1e: Deep Learning Development Environment in Virtual Reality. (arXiv:1906.05925v1 [cs.LG]) https://t.co/fBqSFhQ9Rg Virtual reality…
FScarfato: RT @yapp1e: Deep Learning Development Environment in Virtual Reality. (arXiv:1906.05925v1 [cs.LG]) https://t.co/fBqSFhQ9Rg Virtual reality…
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Authors: 3
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2.168 Mikeys
#7. Towards Compact and Robust Deep Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana
Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several recent works attempt to mitigate these overheads by designing compact networks using pruning of connections. However, we observe that most of the existing strategies to design compact networks fail to preserve network robustness against adversarial examples. In this work, we rigorously study the extension of network pruning strategies to preserve both benign accuracy and robustness of a network. Starting with a formal definition of the pruning procedure, including pre-training, weights pruning, and fine-tuning, we propose a new pruning method that can create compact networks while preserving both benign accuracy and robustness. Our method is based on two main insights: (1) we ensure that the training objectives of the pre-training and fine-tuning steps match the training objective of the desired robust model (e.g., adversarial...
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BrundageBot: Towards Compact and Robust Deep Neural Networks. Vikash Sehwag, Shiqi Wang, Prateek Mittal, and Suman Jana https://t.co/MOoqvfqQ5P
arxivml: "Towards Compact and Robust Deep Neural Networks", Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana https://t.co/BeJPnhuqBn
StatsPapers: Towards Compact and Robust Deep Neural Networks. https://t.co/IhcCJLX5J2
arxiv_cscv: Towards Compact and Robust Deep Neural Networks https://t.co/uniKUL70Vy
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Authors: 4
Total Words: 7662
Unqiue Words: 2085

2.167 Mikeys
#8. Galaxy-Galaxy Lensing in HSC: Validation Tests and the Impact of Heterogeneous Spectroscopic Training Sets
Joshua S. Speagle, Alexie Leauthaud, Song Huang, Christopher P. Bradshaw, Felipe Ardila, Peter L. Capak, Daniel J. Eisenstein, Daniel C. Masters, Rachel Mandelbaum, Surhud More, Melanie Simet, Cristóbal Sifón
Although photometric redshifts (photo-z's) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and color. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and color, and illustrate that the underlying redshift distribution at fixed color can evolve strongly as a function of magnitude. We then test the robustness of the galaxy-galaxy lensing signal in 120 deg$^2$ of HSC-SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST.
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A photometric redshift monstrosity

Repository: frankenz
User: joshspeagle
Language: Python
Stargazers: 9
Subscribers: 8
Forks: 2
Open Issues: 0
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Authors: 12
Total Words: 14516
Unqiue Words: 3508

2.152 Mikeys
#9. NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, Tim Rocktäschel
Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use a Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific rules from training data. We evaluate the proposed system on two different...
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BrundageBot: NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language. Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel https://t.co/IIktK7e24C
Github

Neural Logic Reasoning for Question Answering

Repository: nlprolog
User: leonweber
Language: Python
Stargazers: 1
Subscribers: 1
Forks: 0
Open Issues: 0
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Sample Sizes : None.
Authors: 5
Total Words: 7223
Unqiue Words: 2473

2.148 Mikeys
#10. Fast Radio Bursts: An Extragalactic Enigma
James M. Cordes, Shami Chatterjee
We summarize our understanding of millisecond radio bursts from an extragalactic population of sources. FRBs occur at an extraordinary rate, thousands per day over the entire sky with radiation energy densities at the source about ten billion times larger than those from Galactic pulsars. We survey FRB phenomenology, source models and host galaxies, coherent radiation models, and the role of plasma propagation effects in burst detection. The FRB field is guaranteed to be exciting: new telescopes will expand the sample from the current $\sim 80$ unique burst sources (and a few secure localizations and redshifts) to thousands, with burst localizations that enable host-galaxy redshifts emerging directly from interferometric surveys. * FRBs are now established as an extragalactic phenomenon. * Only a few sources are known to repeat. Despite the failure to redetect other FRBs, they are not inconsistent with all being repeaters. * FRB sources may be new, exotic kinds of objects or known types in extreme circumstances. Many...
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higgsinocat: Fast Radio Bursts: An Extragalactic Enigma. (arXiv:1906.05878v1 [astro-ph.HE]) relevance:72% https://t.co/F9aPx1Pnqu #darkmatter https://t.co/ufcgUQkd8q
StarshipBuilder: Fast Radio Bursts: An Extragalactic Enigma https://t.co/tHpcFt6f6s
scimichael: Fast Radio Bursts: An Extragalactic Enigma https://t.co/E1x85qW0gG
anomalistnews: RT @StarshipBuilder: Fast Radio Bursts: An Extragalactic Enigma https://t.co/tHpcFt6f6s
KlbTheScientist: RT @StarshipBuilder: Fast Radio Bursts: An Extragalactic Enigma https://t.co/tHpcFt6f6s
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Assert is a website where the best academic papers on arXiv (computer science, math, physics), bioRxiv (biology), BITSS (reproducibility), EarthArXiv (earth science), engrXiv (engineering), LawArXiv (law), PsyArXiv (psychology), SocArXiv (social science), and SportRxiv (sport research) bubble to the top each day.

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