paper_id stringlengths 10 10 | paper_url stringlengths 37 80 | title stringlengths 4 518 | abstract stringlengths 3 7.27k | arxiv_id stringlengths 9 16 ⌀ | url_abs stringlengths 18 601 | url_pdf stringlengths 21 601 | aspect_tasks sequence | aspect_methods sequence | aspect_datasets sequence | task_embeddings sequence | method_embeddings sequence | dataset_embeddings sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 ... | 1307.1058 | http://arxiv.org/abs/1307.1058v2 | http://arxiv.org/pdf/1307.1058v2.pdf | [] | [] | [] | [
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-0.319688290357... |
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... | 1909.09563 | https://arxiv.org/abs/1909.09563v1 | https://arxiv.org/pdf/1909.09563v1.pdf | [] | [] | [] | [
-0.5514116883277893,
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... | [
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-0.56718897819519... |
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... | 1705.09944 | http://arxiv.org/abs/1705.09944v1 | http://arxiv.org/pdf/1705.09944v1.pdf | [
"Data Augmentation"
] | [] | [] | [
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-0.6881751418113708,... | [
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-0.23089393973350... | [
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0.04882792383432388,
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-0.35294976830482483,
-0.3545811474... |
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.... | 1905.10107 | https://arxiv.org/abs/1905.10107v1 | https://arxiv.org/pdf/1905.10107v1.pdf | [
"Stereo Matching",
"Stereo Matching Hand"
] | [] | [] | [
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0.28188633918762207,
-0.4800775349140167,
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-0.576229810714721... | [
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0.026127878576517105,
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-0.428829640150... | [
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-0.3646036684513092,
-0.6171610951423645,
-0.6156133413314... |
_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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