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9gWe1QI8-1 | https://paperswithcode.com/paper/on-the-minimal-teaching-sets-of-two | On the minimal teaching sets of two-dimensional threshold functions | It is known that a minimal teaching set of any threshold function on the
twodimensional rectangular grid consists of 3 or 4 points. We derive exact
formulae for the numbers of functions corresponding to these values and further
refine them in the case of a minimal teaching set of size 3. We also prove that
the average cardinality of the minimal teaching sets of threshold functions is
asymptotically 7/2.
We further present corollaries of these results concerning some special
arrangements of lines in the plane. | 1307.1058 | http://arxiv.org/abs/1307.1058v2 | http://arxiv.org/pdf/1307.1058v2.pdf | [] | [] | [] | [
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M1kGtE6A1m | https://paperswithcode.com/paper/gradient-boost-with-convolution-neural | Gradient Boost with Convolution Neural Network for Stock Forecast | Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods. | 1909.09563 | https://arxiv.org/abs/1909.09563v1 | https://arxiv.org/pdf/1909.09563v1.pdf | [] | [] | [] | [
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yusO5UR4MN | https://paperswithcode.com/paper/learning-data-manifolds-with-a-cutting-plane | Learning Data Manifolds with a Cutting Plane Method | We consider the problem of classifying data manifolds where each manifold
represents invariances that are parameterized by continuous degrees of freedom.
Conventional data augmentation methods rely upon sampling large numbers of
training examples from these manifolds; instead, we propose an iterative
algorithm called M_{CP} based upon a cutting-plane approach that efficiently
solves a quadratic semi-infinite programming problem to find the maximum margin
solution. We provide a proof of convergence as well as a polynomial bound on
the number of iterations required for a desired tolerance in the objective
function. The efficiency and performance of M_{CP} are demonstrated in
high-dimensional simulations and on image manifolds generated from the ImageNet
dataset. Our results indicate that M_{CP} is able to rapidly learn good
classifiers and shows superior generalization performance compared with
conventional maximum margin methods using data augmentation methods. | 1705.09944 | http://arxiv.org/abs/1705.09944v1 | http://arxiv.org/pdf/1705.09944v1.pdf | [
"Data Augmentation"
] | [] | [] | [
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bsbiMfdWcf | https://paperswithcode.com/paper/guided-stereo-matching-1 | Guided Stereo Matching | Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM. | 1905.10107 | https://arxiv.org/abs/1905.10107v1 | https://arxiv.org/pdf/1905.10107v1.pdf | [
"Stereo Matching",
"Stereo Matching Hand"
] | [] | [] | [
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] |
_2ljQq7YEb | https://paperswithcode.com/paper/learning-physical-intuition-of-block-towers | Learning Physical Intuition of Block Towers by Example | "Wooden blocks are a common toy for infants, allowing them to develop motor\nskills and gain intuiti(...TRUNCATED) | 1603.01312 | http://arxiv.org/abs/1603.01312v1 | http://arxiv.org/pdf/1603.01312v1.pdf | [] | [] | [] | [-0.4735392928123474,-0.4355241358280182,-0.5722056031227112,1.298268437385559,0.8265211582183838,-0(...TRUNCATED) | [-0.3765108287334442,-0.249501571059227,-0.04062807187438011,0.9454397559165955,0.1896456927061081,-(...TRUNCATED) | [-0.2592306137084961,-0.10670139640569687,-0.05761387571692467,0.9718189239501953,0.2774321436882019(...TRUNCATED) |
7HTdD4Wl8_ | https://paperswithcode.com/paper/audio-visual-olfactory-resource-allocation | Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments | "Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from t(...TRUNCATED) | 2002.02671 | https://arxiv.org/abs/2002.02671v1 | https://arxiv.org/pdf/2002.02671v1.pdf | [] | [] | [] | [-0.0673661008477211,-0.45255544781684875,-0.458268940448761,1.1266506910324097,0.664571225643158,-0(...TRUNCATED) | [-0.19095627963542938,-0.3267354965209961,0.1686972975730896,0.8862221240997314,0.2320784479379654,-(...TRUNCATED) | [-0.2101309895515442,-0.3772193491458893,0.2526111900806427,0.8226816654205322,0.06347254663705826,-(...TRUNCATED) |
xeJi-WhMmM | https://paperswithcode.com/paper/david-dual-attentional-video-deblurring | DAVID: Dual-Attentional Video Deblurring | "Blind video deblurring restores sharp frames from a blurry sequence without any prior. It is a chal(...TRUNCATED) | 1912.03445 | https://arxiv.org/abs/1912.03445v1 | https://arxiv.org/pdf/1912.03445v1.pdf | [
"Deblurring"
] | [] | [] | [-0.14874623715877533,-0.5660415291786194,-0.3773545026779175,1.289333701133728,0.5193009376525879,-(...TRUNCATED) | [-0.5874375104904175,0.041729215532541275,0.0768548995256424,0.5997906923294067,0.11187782138586044,(...TRUNCATED) | [-0.04749281704425812,-0.2928813099861145,0.03079732321202755,0.8248049020767212,-0.0035559905227273(...TRUNCATED) |
MApq3NnxKg | https://paperswithcode.com/paper/adversarial-machine-learning-an | Adversarial Attacks and Defenses: An Interpretation Perspective | "Despite the recent advances in a wide spectrum of applications, machine learning models, especially(...TRUNCATED) | 2004.11488 | https://arxiv.org/abs/2004.11488v2 | https://arxiv.org/pdf/2004.11488v2.pdf | [
"Adversarial Attack",
"Adversarial Defense",
"Interpretable Machine Learning"
] | [] | [] | [-0.039302706718444824,-0.23968368768692017,-0.7941969037055969,0.7198454737663269,0.734379708766937(...TRUNCATED) | [-0.12523004412651062,-0.15125659108161926,-0.2114940583705902,0.6568752527236938,0.0324455015361309(...TRUNCATED) | [-0.08819390833377838,-0.2976553738117218,0.010734310373663902,0.781283974647522,0.09045805782079697(...TRUNCATED) |
c9wMXjAWKi | https://paperswithcode.com/paper/headless-horseman-adversarial-attacks-on | Headless Horseman: Adversarial Attacks on Transfer Learning Models | "Transfer learning facilitates the training of task-specific classifiers using pre-trained models as(...TRUNCATED) | 2004.09007 | https://arxiv.org/abs/2004.09007v1 | https://arxiv.org/pdf/2004.09007v1.pdf | [
"Adversarial Attack",
"Transfer Learning"
] | [] | [] | [0.13983863592147827,-0.26675838232040405,-0.7626726031303406,1.2334237098693848,0.7638939619064331,(...TRUNCATED) | [-0.032947737723588943,-0.30444493889808655,-0.038328416645526886,0.9820055365562439,0.1967691332101(...TRUNCATED) | [0.021084317937493324,-0.26999104022979736,0.015382998622953892,0.8221044540405273,0.169981181621551(...TRUNCATED) |
l-E3TWSB2x | https://paperswithcode.com/paper/on-the-performance-of-a-canonical-labeling | Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs | "Graph alignment in two correlated random graphs refers to the task of identifying the correspondenc(...TRUNCATED) | 1804.09758 | https://arxiv.org/abs/1804.09758v2 | https://arxiv.org/pdf/1804.09758v2.pdf | [
"Graph Matching"
] | [] | [] | [-0.14099949598312378,-0.1662614941596985,-0.9364451766014099,0.9985771775245667,0.6274507641792297,(...TRUNCATED) | [-0.20622140169143677,-0.1965688019990921,-0.2794179320335388,0.5042135715484619,0.02993128076195717(...TRUNCATED) | [-0.0034710713662207127,0.02021874114871025,-0.2111203968524933,0.7677950263023376,0.287794649600982(...TRUNCATED) |
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