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https://paperswithcode.com/paper/review-highlights-opinion-mining-on-reviews-a
|
Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction
| null |
https://dl.acm.org/citation.cfm?id=3158385
|
http://vixra.org/pdf/1910.0514v1.pdf
|
https://github.com/yardstick17/AspectBasedSentimentAnalysis
| false | false | false |
none
|
https://paperswithcode.com/paper/temporally-coherent-gans-for-video-super
|
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
|
1811.09393
|
https://arxiv.org/abs/1811.09393v4
|
https://arxiv.org/pdf/1811.09393v4.pdf
|
https://github.com/GitHubXlong/TecoGAN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/ckmeans-and-fckmeans-two-deterministic
|
CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance
|
2304.09989
|
https://arxiv.org/abs/2304.09989v2
|
https://arxiv.org/pdf/2304.09989v2.pdf
|
https://github.com/Layebuniv/fckmeans
| true | false | false |
none
|
https://paperswithcode.com/paper/1-ogc-the-first-open-gravitational-wave
|
1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data
|
1811.01921
|
http://arxiv.org/abs/1811.01921v2
|
http://arxiv.org/pdf/1811.01921v2.pdf
|
https://github.com/gwastro/1-ogc
| true | true | true |
none
|
https://paperswithcode.com/paper/modeling-the-dynamics-of-online-learning
|
Modeling the Dynamics of Online Learning Activity
|
1610.05775
|
http://arxiv.org/abs/1610.05775v1
|
http://arxiv.org/pdf/1610.05775v1.pdf
|
https://github.com/Networks-Learning/hdhp.py
| true | true | true |
none
|
https://paperswithcode.com/paper/hierarchical-density-order-embeddings
|
Hierarchical Density Order Embeddings
|
1804.09843
|
http://arxiv.org/abs/1804.09843v1
|
http://arxiv.org/pdf/1804.09843v1.pdf
|
https://github.com/benathi/density-order-emb
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/kernalised-multi-resolution-convnet-for
|
Kernalised Multi-resolution Convnet for Visual Tracking
|
1708.00577
|
http://arxiv.org/abs/1708.00577v1
|
http://arxiv.org/pdf/1708.00577v1.pdf
|
https://github.com/stevenwudi/KMC_cvprw_2017
| true | true | false |
tf
|
https://paperswithcode.com/paper/crowdsourcing-lightweight-pyramids-for-manual
|
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
|
1904.05929
|
http://arxiv.org/abs/1904.05929v1
|
http://arxiv.org/pdf/1904.05929v1.pdf
|
https://github.com/OriShapira/LitePyramids
| true | true | false |
none
|
https://paperswithcode.com/paper/spatiotemporal-residual-networks-for-video
|
Spatiotemporal Residual Networks for Video Action Recognition
|
1611.02155
|
http://arxiv.org/abs/1611.02155v1
|
http://arxiv.org/pdf/1611.02155v1.pdf
|
https://github.com/feichtenhofer/st-resnet
| true | true | false |
none
|
https://paperswithcode.com/paper/generative-partition-networks-for-multi
|
Generative Partition Networks for Multi-Person Pose Estimation
|
1705.07422
|
http://arxiv.org/abs/1705.07422v2
|
http://arxiv.org/pdf/1705.07422v2.pdf
|
https://github.com/NieXC/pytorch-ppn
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/traffic-graph-convolutional-recurrent-neural
|
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
|
1802.07007
|
https://arxiv.org/abs/1802.07007v3
|
https://arxiv.org/pdf/1802.07007v3.pdf
|
https://github.com/zhiyongc/Seattle-Loop-Data
| true | true | false |
none
|
https://paperswithcode.com/paper/surfacenet-an-end-to-end-3d-neural-network
|
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
|
1708.01749
|
http://arxiv.org/abs/1708.01749v1
|
http://arxiv.org/pdf/1708.01749v1.pdf
|
https://github.com/mjiUST/SurfaceNet
| true | true | false |
none
|
https://paperswithcode.com/paper/geometric-adaptive-monte-carlo-in-random
|
Geometric adaptive Monte Carlo in random environment
|
1608.07986
|
https://arxiv.org/abs/1608.07986v4
|
https://arxiv.org/pdf/1608.07986v4.pdf
|
https://github.com/scidom/MAMALASampler.jl
| true | true | false |
none
|
https://paperswithcode.com/paper/random-directions-stochastic-approximation
|
Random directions stochastic approximation with deterministic perturbations
|
1808.02871
|
http://arxiv.org/abs/1808.02871v2
|
http://arxiv.org/pdf/1808.02871v2.pdf
|
https://github.com/prashla/RDSA
| true | true | false |
none
|
https://paperswithcode.com/paper/manifoldnet-a-deep-network-framework-for
|
ManifoldNet: A Deep Network Framework for Manifold-valued Data
|
1809.06211
|
http://arxiv.org/abs/1809.06211v3
|
http://arxiv.org/pdf/1809.06211v3.pdf
|
https://github.com/jjbouza/manifold-net
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/show-and-tell-a-neural-image-caption
|
Show and Tell: A Neural Image Caption Generator
|
1411.4555
|
http://arxiv.org/abs/1411.4555v2
|
http://arxiv.org/pdf/1411.4555v2.pdf
|
https://github.com/kirbiyik/caption-it
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/disentangling-factors-of-variation-with-cycle
|
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
|
1804.10469
|
http://arxiv.org/abs/1804.10469v1
|
http://arxiv.org/pdf/1804.10469v1.pdf
|
https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/disentangling-factors-of-variation-in-deep
|
Disentangling factors of variation in deep representations using adversarial training
|
1611.03383
|
http://arxiv.org/abs/1611.03383v1
|
http://arxiv.org/pdf/1611.03383v1.pdf
|
https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical
|
U-Net: Convolutional Networks for Biomedical Image Segmentation
|
1505.04597
|
http://arxiv.org/abs/1505.04597v1
|
http://arxiv.org/pdf/1505.04597v1.pdf
|
https://github.com/neshitov/UNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/audino-a-modern-annotation-tool-for-audio-and
|
audino: A Modern Annotation Tool for Audio and Speech
|
2006.05236
|
https://arxiv.org/abs/2006.05236v2
|
https://arxiv.org/pdf/2006.05236v2.pdf
|
https://github.com/midas-research/audino
| true | true | true |
none
|
https://paperswithcode.com/paper/robust-adversarial-reinforcement-learning
|
Robust Adversarial Reinforcement Learning
|
1703.02702
|
http://arxiv.org/abs/1703.02702v1
|
http://arxiv.org/pdf/1703.02702v1.pdf
|
https://github.com/davidsonic/robust-grasp
| false | false | true |
tf
|
https://paperswithcode.com/paper/chainercv-a-library-for-deep-learning-in
|
ChainerCV: a Library for Deep Learning in Computer Vision
|
1708.08169
|
http://arxiv.org/abs/1708.08169v1
|
http://arxiv.org/pdf/1708.08169v1.pdf
|
https://github.com/chainer/chainercv
| false | false | true |
none
|
https://paperswithcode.com/paper/denoising-diffusion-probabilistic-models
|
Denoising Diffusion Probabilistic Models
|
2006.11239
|
https://arxiv.org/abs/2006.11239v2
|
https://arxiv.org/pdf/2006.11239v2.pdf
|
https://github.com/sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/vo-tranh-eternal-pulse-o-lumina-genesis
|
Vô Tranh Eternal Pulse Ω – Lumina Genesis
| null |
https://zenodo.org/records/15132859
|
https://zenodo.org/records/15132859/files/WHITEPAPER.pdf
|
https://github.com/vinhatson/The-Last---Lumina-genesis
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/variational-dropout-sparsifies-deep-neural
|
Variational Dropout Sparsifies Deep Neural Networks
|
1701.05369
|
http://arxiv.org/abs/1701.05369v3
|
http://arxiv.org/pdf/1701.05369v3.pdf
|
https://github.com/ars-ashuha/sparse-vd-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-from-simulated-and-unsupervised
|
Learning from Simulated and Unsupervised Images through Adversarial Training
|
1612.07828
|
http://arxiv.org/abs/1612.07828v2
|
http://arxiv.org/pdf/1612.07828v2.pdf
|
https://github.com/rickyhan/SimGAN-Captcha
| false | false | true |
tf
|
https://paperswithcode.com/paper/savoias-a-diverse-multi-category-visual
|
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
|
1810.01771
|
http://arxiv.org/abs/1810.01771v1
|
http://arxiv.org/pdf/1810.01771v1.pdf
|
https://github.com/esaraee/Savoias-Dataset
| true | true | false |
none
|
https://paperswithcode.com/paper/depth-map-prediction-from-a-single-image
|
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
|
1406.2283
|
http://arxiv.org/abs/1406.2283v1
|
http://arxiv.org/pdf/1406.2283v1.pdf
|
https://github.com/MasazI/cnn_depth_tensorflow
| false | false | true |
tf
|
https://paperswithcode.com/paper/word-embeddings-for-the-analysis-of
|
Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora
| null |
https://www.cambridge.org/core/journals/political-analysis/article/abs/word-embeddings-for-the-analysis-of-ideological-placement-in-parliamentary-corpora/017F0CEA9B3DB6E1B94AC36A509A8A7B
|
https://ludovicrheault.weebly.com/uploads/3/9/4/0/39408253/rheaultcochrane2019_pa.pdf
|
https://github.com/lrheault/partyembed
| true | true | false |
none
|
https://paperswithcode.com/paper/how-emotional-are-you-neural-architectures
|
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs
| null |
https://aclanthology.org/C18-1247
|
https://aclanthology.org/C18-1247.pdf
|
https://github.com/Pranav-Goel/Neural_Emotion_Intensity_Prediction
| true | true | false |
tf
|
https://paperswithcode.com/paper/afet-automatic-fine-grained-entity-typing-by
|
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding
| null |
https://aclanthology.org/D16-1144
|
https://aclanthology.org/D16-1144.pdf
|
https://github.com/shanzhenren/AFET
| true | true | false |
none
|
https://paperswithcode.com/paper/the-signature-of-large-scale-turbulence
|
The signature of large scale turbulence driving on the structure of the interstellar medium
|
2206.00451
|
https://arxiv.org/abs/2206.00451v1
|
https://arxiv.org/pdf/2206.00451v1.pdf
|
https://bitbucket.org/rteyssie/ramses
| true | false | false |
none
|
https://paperswithcode.com/paper/the-fermilab-muon-g-2-straw-tracking
|
The Fermilab Muon $g-2$ straw tracking detectors, internal tracker alignment, and the muon EDM measurement
|
1909.12900
|
https://arxiv.org/abs/1909.12900v2
|
https://arxiv.org/pdf/1909.12900v2.pdf
|
https://github.com/glukicov/alignTrack
| false | false | true |
none
|
https://paperswithcode.com/paper/two-local-models-for-neural-constituent
|
Two Local Models for Neural Constituent Parsing
|
1808.04850
|
http://arxiv.org/abs/1808.04850v2
|
http://arxiv.org/pdf/1808.04850v2.pdf
|
https://github.com/zeeeyang/two-local-neural-conparsers
| true | true | false |
none
|
https://paperswithcode.com/paper/learning-deep-features-for-discriminative
|
Learning Deep Features for Discriminative Localization
|
1512.04150
|
http://arxiv.org/abs/1512.04150v1
|
http://arxiv.org/pdf/1512.04150v1.pdf
|
https://github.com/tensorpack/tensorpack/tree/master/examples/Saliency
| false | false | false |
tf
|
https://paperswithcode.com/paper/a-fast-and-scalable-joint-estimator-for-1
|
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
|
1806.00548
|
http://arxiv.org/abs/1806.00548v4
|
http://arxiv.org/pdf/1806.00548v4.pdf
|
https://github.com/QData/JEEK
| true | false | true |
none
|
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using
|
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
|
1703.10593
|
https://arxiv.org/abs/1703.10593v7
|
https://arxiv.org/pdf/1703.10593v7.pdf
|
https://github.com/WeiYangze/hibernate-demo
| false | false | true |
tf
|
https://paperswithcode.com/paper/neural-machine-translation-of-rare-words-with
|
Neural Machine Translation of Rare Words with Subword Units
|
1508.07909
|
http://arxiv.org/abs/1508.07909v5
|
http://arxiv.org/pdf/1508.07909v5.pdf
|
https://github.com/simonjisu/NMT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/breaking-the-nonsmooth-barrier-a-scalable
|
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
|
1707.06468
|
http://arxiv.org/abs/1707.06468v3
|
http://arxiv.org/pdf/1707.06468v3.pdf
|
https://github.com/fabianp/ProxASAGA
| true | true | true |
none
|
https://paperswithcode.com/paper/underground-root-tuber-sensing-via-a-wi-fi
|
Underground Root Tuber Sensing via a Wi-Fi Mesh Network
| null |
https://dl.acm.org/doi/10.1145/3715014.3724365
|
https://dl.acm.org/doi/pdf/10.1145/3715014.3724365
|
https://github.com/Data-driven-RTI/undergroud_sensing_wifi_csi
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/like-what-you-like-knowledge-distill-via
|
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
|
1707.01219
|
http://arxiv.org/abs/1707.01219v2
|
http://arxiv.org/pdf/1707.01219v2.pdf
|
https://github.com/TuSimple/neuron-selectivity-transfer
| false | false | true |
tf
|
https://paperswithcode.com/paper/reassessing-the-goals-of-grammatical-error
|
Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
| null |
https://aclanthology.org/Q16-1013
|
https://aclanthology.org/Q16-1013.pdf
|
https://github.com/keisks/reassess-gec
| true | true | false |
none
|
https://paperswithcode.com/paper/ctcmodel-a-keras-model-for-connectionist
|
CTCModel: a Keras Model for Connectionist Temporal Classification
|
1901.07957
|
http://arxiv.org/abs/1901.07957v1
|
http://arxiv.org/pdf/1901.07957v1.pdf
|
https://github.com/cyprienruffino/CTCModel
| false | false | true |
tf
|
https://paperswithcode.com/paper/alternating-direction-graph-matching
|
Alternating Direction Graph Matching
|
1611.07583
|
http://arxiv.org/abs/1611.07583v4
|
http://arxiv.org/pdf/1611.07583v4.pdf
|
https://github.com/netw0rkf10w/adgm
| false | false | false |
none
|
https://paperswithcode.com/paper/efficient-neural-architecture-search-via-1
|
Efficient Neural Architecture Search via Parameter Sharing
|
1802.03268
|
http://arxiv.org/abs/1802.03268v2
|
http://arxiv.org/pdf/1802.03268v2.pdf
|
https://github.com/Ezereal/enas
| false | false | true |
tf
|
https://paperswithcode.com/paper/forgetting-to-learn-logic-programs
|
Forgetting to learn logic programs
|
1911.06643
|
https://arxiv.org/abs/1911.06643v1
|
https://arxiv.org/pdf/1911.06643v1.pdf
|
https://github.com/metagol/metagol
| true | true | false |
none
|
https://paperswithcode.com/paper/yolo9000-better-faster-stronger
|
YOLO9000: Better, Faster, Stronger
|
1612.08242
|
http://arxiv.org/abs/1612.08242v1
|
http://arxiv.org/pdf/1612.08242v1.pdf
|
https://github.com/gpandu/Object-detection
| false | false | true |
tf
|
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection
|
Focal Loss for Dense Object Detection
|
1708.02002
|
http://arxiv.org/abs/1708.02002v2
|
http://arxiv.org/pdf/1708.02002v2.pdf
|
https://github.com/neshitov/UNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unsupervised-adaptation-learning-for
|
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution
| null |
http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.pdf
|
https://github.com/JiangtaoNie/UAL
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/fast-mser
|
Fast MSER
| null |
http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Fast_MSER_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Fast_MSER_CVPR_2020_paper.pdf
|
https://github.com/mmmn143/fast-mser
| true | true | false |
none
|
https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
|
Aggregated Residual Transformations for Deep Neural Networks
|
1611.05431
|
http://arxiv.org/abs/1611.05431v2
|
http://arxiv.org/pdf/1611.05431v2.pdf
|
https://github.com/TuSimple/resnet.mxnet
| false | false | true |
tf
|
https://paperswithcode.com/paper/backpack-packing-more-into-backprop
|
BackPACK: Packing more into backprop
|
1912.10985
|
https://arxiv.org/abs/1912.10985v2
|
https://arxiv.org/pdf/1912.10985v2.pdf
|
https://github.com/f-dangel/backpack
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/linear-colour-segmentation-revisited
|
Linear colour segmentation revisited
|
1901.00534
|
http://arxiv.org/abs/1901.00534v1
|
http://arxiv.org/pdf/1901.00534v1.pdf
|
https://github.com/visillect/colorsegdataset
| true | true | false |
none
|
https://paperswithcode.com/paper/guided-image-generation-with-conditional
|
Guided Image Generation with Conditional Invertible Neural Networks
|
1907.02392
|
https://arxiv.org/abs/1907.02392v3
|
https://arxiv.org/pdf/1907.02392v3.pdf
|
https://github.com/5yearsKim/Conditional-Normalizing-Flow
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/glow-generative-flow-with-invertible-1x1
|
Glow: Generative Flow with Invertible 1x1 Convolutions
|
1807.03039
|
http://arxiv.org/abs/1807.03039v2
|
http://arxiv.org/pdf/1807.03039v2.pdf
|
https://github.com/5yearsKim/Conditional-Normalizing-Flow
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/the-simple-essence-of-automatic
|
The simple essence of automatic differentiation
|
1804.00746
|
http://arxiv.org/abs/1804.00746v2
|
http://arxiv.org/pdf/1804.00746v2.pdf
|
https://github.com/conal/essence-of-ad
| false | false | true |
none
|
https://paperswithcode.com/paper/an-ensemble-model-of-word-based-and-character
|
An Ensemble Model of Word-based and Character-based Models for Japanese and Chinese Input Method
| null |
https://aclanthology.org/W12-4802
|
https://aclanthology.org/W12-4802.pdf
|
https://github.com/nokuno/jsc
| true | true | false |
none
|
https://paperswithcode.com/paper/meta-transfer-networks-for-zero-shot-learning
|
Episode-based Prototype Generating Network for Zero-Shot Learning
|
1909.03360
|
https://arxiv.org/abs/1909.03360v2
|
https://arxiv.org/pdf/1909.03360v2.pdf
|
https://github.com/yunlongyu/EPGN
| true | true | false |
tf
|
https://paperswithcode.com/paper/spin-orientations-of-merging-black-holes
|
Spin orientations of merging black holes formed from the evolution of stellar binaries
|
1808.02491
|
http://arxiv.org/abs/1808.02491v1
|
http://arxiv.org/pdf/1808.02491v1.pdf
|
https://github.com/dgerosa/spops
| true | true | false |
none
|
https://paperswithcode.com/paper/constrained-size-tensorflow-models-for
|
Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge
|
1808.06739
|
http://arxiv.org/abs/1808.06739v3
|
http://arxiv.org/pdf/1808.06739v3.pdf
|
https://github.com/boliu61/youtube-8m
| true | true | true |
tf
|
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style
|
A Neural Algorithm of Artistic Style
|
1508.06576
|
http://arxiv.org/abs/1508.06576v2
|
http://arxiv.org/pdf/1508.06576v2.pdf
|
https://github.com/Gaurav927/Neural_Style_Transfer
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/easy-transfer-learning-by-exploiting-intra
|
Easy Transfer Learning By Exploiting Intra-domain Structures
|
1904.01376
|
http://arxiv.org/abs/1904.01376v2
|
http://arxiv.org/pdf/1904.01376v2.pdf
|
https://github.com/jindongwang/transferlearning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/convex-space-learning-for-tabular-synthetic
|
Convex space learning for tabular synthetic data generation
|
2407.09789
|
https://arxiv.org/abs/2407.09789v1
|
https://arxiv.org/pdf/2407.09789v1.pdf
|
https://github.com/manjunath-mahendra/NextConvGeN
| true | false | false |
tf
|
https://paperswithcode.com/paper/giraffe-using-deep-reinforcement-learning-to
|
Giraffe: Using Deep Reinforcement Learning to Play Chess
|
1509.01549
|
http://arxiv.org/abs/1509.01549v2
|
http://arxiv.org/pdf/1509.01549v2.pdf
|
https://github.com/saikrishna-1996/deep_pepper_chess
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/13013666
|
Zero-Shot Learning Through Cross-Modal Transfer
|
1301.3666
|
http://arxiv.org/abs/1301.3666v2
|
http://arxiv.org/pdf/1301.3666v2.pdf
|
https://github.com/mganjoo/zslearning
| false | false | false |
none
|
https://paperswithcode.com/paper/semantic-image-synthesis-via-adversarial
|
Semantic Image Synthesis via Adversarial Learning
|
1707.06873
|
http://arxiv.org/abs/1707.06873v1
|
http://arxiv.org/pdf/1707.06873v1.pdf
|
https://github.com/vtddggg/BilinearGAN_for_LBIE
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/composition-based-crystal-materials-symmetry
|
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
|
2105.07303
|
https://arxiv.org/abs/2105.07303v1
|
https://arxiv.org/pdf/2105.07303v1.pdf
|
https://github.com/Yuxinya/SG_predict
| true | true | false |
none
|
https://paperswithcode.com/paper/generative-adversarial-text-to-image
|
Generative Adversarial Text to Image Synthesis
|
1605.05396
|
http://arxiv.org/abs/1605.05396v2
|
http://arxiv.org/pdf/1605.05396v2.pdf
|
https://github.com/vtddggg/BilinearGAN_for_LBIE
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization
|
mixup: Beyond Empirical Risk Minimization
|
1710.09412
|
http://arxiv.org/abs/1710.09412v2
|
http://arxiv.org/pdf/1710.09412v2.pdf
|
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
| false | false | true |
none
|
https://paperswithcode.com/paper/metasci-scalable-and-adaptive-reconstruction
|
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
|
2103.01786
|
https://arxiv.org/abs/2103.01786v1
|
https://arxiv.org/pdf/2103.01786v1.pdf
|
https://github.com/xyvirtualgroup/MetaSCI-CVPR2021
| true | true | false |
tf
|
https://paperswithcode.com/paper/squeeze-and-excitation-networks
|
Squeeze-and-Excitation Networks
|
1709.01507
|
https://arxiv.org/abs/1709.01507v4
|
https://arxiv.org/pdf/1709.01507v4.pdf
|
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
| false | false | true |
none
|
https://paperswithcode.com/paper/open3d-a-modern-library-for-3d-data
|
Open3D: A Modern Library for 3D Data Processing
|
1801.09847
|
http://arxiv.org/abs/1801.09847v1
|
http://arxiv.org/pdf/1801.09847v1.pdf
|
https://github.com/IntelVCL/Open3D
| false | false | true |
tf
|
https://paperswithcode.com/paper/trainable-frontend-for-robust-and-far-field
|
Trainable Frontend For Robust and Far-Field Keyword Spotting
|
1607.05666
|
http://arxiv.org/abs/1607.05666v1
|
http://arxiv.org/pdf/1607.05666v1.pdf
|
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
| false | false | true |
none
|
https://paperswithcode.com/paper/xdeepfm-combining-explicit-and-implicit
|
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
|
1803.05170
|
http://arxiv.org/abs/1803.05170v3
|
http://arxiv.org/pdf/1803.05170v3.pdf
|
https://github.com/bettenW/Tencent2019_Finals_Rank1st
| false | false | true |
tf
|
https://paperswithcode.com/paper/real-time-localization-and-tracking-of
|
Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV
|
1712.01491
|
http://arxiv.org/abs/1712.01491v4
|
http://arxiv.org/pdf/1712.01491v4.pdf
|
https://github.com/AdelaideAuto-IDLab/TrackerBots/tree/master/JoFR_2019
| true | false | false |
none
|
https://paperswithcode.com/paper/adversarial-autoencoders
|
Adversarial Autoencoders
|
1511.05644
|
http://arxiv.org/abs/1511.05644v2
|
http://arxiv.org/pdf/1511.05644v2.pdf
|
https://github.com/santi-pdp/pase
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/accelerating-deep-unsupervised-domain
|
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
|
1904.02654
|
http://arxiv.org/abs/1904.02654v1
|
http://arxiv.org/pdf/1904.02654v1.pdf
|
https://github.com/jindongwang/transferlearning
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object
|
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
|
1506.01497
|
http://arxiv.org/abs/1506.01497v3
|
http://arxiv.org/pdf/1506.01497v3.pdf
|
https://github.com/zacks417/faster-rcnn-tf
| false | false | true |
tf
|
https://paperswithcode.com/paper/stein-variational-gradient-descent-a-general
|
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
|
1608.04471
|
https://arxiv.org/abs/1608.04471v3
|
https://arxiv.org/pdf/1608.04471v3.pdf
|
https://github.com/activatedgeek/stein-gradient
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection
|
Focal Loss for Dense Object Detection
|
1708.02002
|
http://arxiv.org/abs/1708.02002v2
|
http://arxiv.org/pdf/1708.02002v2.pdf
|
https://github.com/vantupham/darknet
| false | false | true |
none
|
https://paperswithcode.com/paper/context-aware-attentive-knowledge-tracing
|
Context-Aware Attentive Knowledge Tracing
|
2007.12324
|
https://arxiv.org/abs/2007.12324v1
|
https://arxiv.org/pdf/2007.12324v1.pdf
|
https://github.com/arghosh/AKT
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/efficient-estimation-of-word-representations
|
Efficient Estimation of Word Representations in Vector Space
|
1301.3781
|
http://arxiv.org/abs/1301.3781v3
|
http://arxiv.org/pdf/1301.3781v3.pdf
|
https://github.com/rohith2506/word_embeddings
| false | false | true |
none
|
https://paperswithcode.com/paper/compressing-physical-properties-of-atomic
|
Compressing physical properties of atomic species for improving predictive chemistry
|
1811.00123
|
http://arxiv.org/abs/1811.00123v1
|
http://arxiv.org/pdf/1811.00123v1.pdf
|
https://github.com/jeherr/element-encoder
| false | false | true |
tf
|
https://paperswithcode.com/paper/contour-knowledge-transfer-for-salient-object
|
Contour Knowledge Transfer for Salient Object Detection
| null |
http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf
|
https://github.com/lixin666/C2SNet
| true | true | false |
none
|
https://paperswithcode.com/paper/universal-language-model-fine-tuning-for-text
|
Universal Language Model Fine-tuning for Text Classification
|
1801.06146
|
http://arxiv.org/abs/1801.06146v5
|
http://arxiv.org/pdf/1801.06146v5.pdf
|
https://github.com/comicencyclo/TransferLearning_DiscriminativeFineTuning
| false | false | true |
none
|
https://paperswithcode.com/paper/restoring-negative-information-in-few-shot
|
Restoring Negative Information in Few-Shot Object Detection
|
2010.11714
|
https://arxiv.org/abs/2010.11714v2
|
https://arxiv.org/pdf/2010.11714v2.pdf
|
https://github.com/yang-yk/NP-RepMet
| true | true | false |
mxnet
|
https://paperswithcode.com/paper/predictive-entropy-search-for-bayesian
|
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
|
1502.05312
|
http://arxiv.org/abs/1502.05312v2
|
http://arxiv.org/pdf/1502.05312v2.pdf
|
https://github.com/chongkewu/PESC-HPC
| false | false | true |
none
|
https://paperswithcode.com/paper/real-time-air-pollution-prediction-model
|
Real-time Air Pollution prediction model based on Spatiotemporal Big data
|
1805.00432
|
http://arxiv.org/abs/1805.00432v3
|
http://arxiv.org/pdf/1805.00432v3.pdf
|
https://github.com/vanduc103/air_analysis_v1
| true | true | false |
tf
|
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection
|
Focal Loss for Dense Object Detection
|
1708.02002
|
http://arxiv.org/abs/1708.02002v2
|
http://arxiv.org/pdf/1708.02002v2.pdf
|
https://github.com/yhenon/pytorch-retinanet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-spatiotemporal-features-with-3d
|
Learning Spatiotemporal Features with 3D Convolutional Networks
|
1412.0767
|
http://arxiv.org/abs/1412.0767v4
|
http://arxiv.org/pdf/1412.0767v4.pdf
|
https://github.com/AKASH2907/Content-based-Video-Recommendation
| false | false | true |
tf
|
https://paperswithcode.com/paper/increasingly-packing-multiple-facial
|
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
| null |
https://dl.acm.org/doi/10.1145/3323873.3325053
|
https://dl.acm.org/doi/pdf/10.1145/3323873.3325053
|
https://github.com/ivclab/CPG
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/integralaction-pose-driven-feature
|
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
|
2007.06317
|
https://arxiv.org/abs/2007.06317v2
|
https://arxiv.org/pdf/2007.06317v2.pdf
|
https://github.com/arunos728/arunos728.github.io
| false | false | true |
none
|
https://paperswithcode.com/paper/fully-convolutional-pixel-adaptive-image
|
Fully Convolutional Pixel Adaptive Image Denoiser
|
1807.07569
|
https://arxiv.org/abs/1807.07569v4
|
https://arxiv.org/pdf/1807.07569v4.pdf
|
https://github.com/csm9493/FC-AIDE
| false | false | true |
tf
|
https://paperswithcode.com/paper/joint-3d-face-reconstruction-and-dense
|
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
|
1803.07835
|
http://arxiv.org/abs/1803.07835v1
|
http://arxiv.org/pdf/1803.07835v1.pdf
|
https://github.com/jimmy0087/faceai-master
| false | false | true |
tf
|
https://paperswithcode.com/paper/implicit-self-consistent-description-of
|
Implicit self-consistent description of electrolyte in plane-wave density-functional theory
|
1601.03346
|
http://arxiv.org/abs/1601.03346v1
|
http://arxiv.org/pdf/1601.03346v1.pdf
|
https://github.com/henniggroup/VASPsol
| true | true | true |
none
|
https://paperswithcode.com/paper/news-headline-grouping-as-a-challenging-nlu-1
|
News Headline Grouping as a Challenging NLU Task
|
2105.05391
|
https://arxiv.org/abs/2105.05391v1
|
https://arxiv.org/pdf/2105.05391v1.pdf
|
https://github.com/tingofurro/headline_grouping
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/end-to-end-learning-of-lda-by-mirror-descent
|
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
|
1508.03398
|
http://arxiv.org/abs/1508.03398v2
|
http://arxiv.org/pdf/1508.03398v2.pdf
|
https://github.com/jvking/bp-lda
| true | true | false |
none
|
https://paperswithcode.com/paper/an-optimization-approach-to-learning-falling
|
An Optimization Approach to Learning Falling Rule Lists
|
1710.02572
|
http://arxiv.org/abs/1710.02572v3
|
http://arxiv.org/pdf/1710.02572v3.pdf
|
https://github.com/cfchen-duke/FRLOptimization
| true | true | false |
none
|
https://paperswithcode.com/paper/dueling-network-architectures-for-deep
|
Dueling Network Architectures for Deep Reinforcement Learning
|
1511.06581
|
http://arxiv.org/abs/1511.06581v3
|
http://arxiv.org/pdf/1511.06581v3.pdf
|
https://github.com/wtingda/DeepRLBreakout
| false | false | true |
tf
|
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement
|
Asynchronous Methods for Deep Reinforcement Learning
|
1602.01783
|
http://arxiv.org/abs/1602.01783v2
|
http://arxiv.org/pdf/1602.01783v2.pdf
|
https://github.com/wtingda/DeepRLBreakout
| false | false | true |
tf
|
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