Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/rthn-a-rnn-transformer-hierarchical-network
RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
1906.01236
https://arxiv.org/abs/1906.01236v1
https://arxiv.org/pdf/1906.01236v1.pdf
https://github.com/NUSTM/RTHN
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/dstein64/pastiche
false
false
true
pytorch
https://paperswithcode.com/paper/report-of-the-iau-commission-4-working-group
Report of the IAU Commission 4 Working Group on Standardizing Access to Ephemerides and File Format Specification
1507.04291
http://arxiv.org/abs/1507.04291v1
http://arxiv.org/pdf/1507.04291v1.pdf
https://github.com/plb97/jpl
false
false
true
none
https://paperswithcode.com/paper/paying-attention-to-multi-word-expressions-in
Paying Attention to Multi-Word Expressions in Neural Machine Translation
1710.06313
https://arxiv.org/abs/1710.06313v2
https://arxiv.org/pdf/1710.06313v2.pdf
https://github.com/M4t1ss/MWE-Tools
true
true
false
none
https://paperswithcode.com/paper/learning-to-refer-informatively-by-amortizing
Learning to refer informatively by amortizing pragmatic reasoning
2006.00418
https://arxiv.org/abs/2006.00418v1
https://arxiv.org/pdf/2006.00418v1.pdf
https://github.com/juliaiwhite/amortized-rsa
true
true
true
pytorch
https://paperswithcode.com/paper/learning-energy-based-inpainting-for-optical
Learning Energy Based Inpainting for Optical Flow
1811.03721
http://arxiv.org/abs/1811.03721v1
http://arxiv.org/pdf/1811.03721v1.pdf
https://github.com/vogechri/CustomNetworkLayers
true
true
false
pytorch
https://paperswithcode.com/paper/exploiting-explicit-paths-for-multi-hop
Exploiting Explicit Paths for Multi-hop Reading Comprehension
1811.01127
https://arxiv.org/abs/1811.01127v2
https://arxiv.org/pdf/1811.01127v2.pdf
https://github.com/allenai/PathNet
true
true
true
none
https://paperswithcode.com/paper/qatm-quality-aware-template-matching-for-deep
QATM: Quality-Aware Template Matching For Deep Learning
1903.07254
http://arxiv.org/abs/1903.07254v2
http://arxiv.org/pdf/1903.07254v2.pdf
https://github.com/kamata1729/QATM_pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/meta-learning-curiosity-algorithms-1
Meta-learning curiosity algorithms
2003.05325
https://arxiv.org/abs/2003.05325v1
https://arxiv.org/pdf/2003.05325v1.pdf
https://github.com/mfranzs/meta-learning-curiosity-algorithms
true
true
false
tf
https://paperswithcode.com/paper/batch-normalization-orthogonalizes
Batch Normalization Orthogonalizes Representations in Deep Random Networks
2106.03970
https://arxiv.org/abs/2106.03970v1
https://arxiv.org/pdf/2106.03970v1.pdf
https://github.com/hadidaneshmand/batchnorm21
true
true
false
none
https://paperswithcode.com/paper/uncovering-divergent-linguistic-information
Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation
1809.02094
http://arxiv.org/abs/1809.02094v1
http://arxiv.org/pdf/1809.02094v1.pdf
https://github.com/artetxem/uncovec
true
true
true
pytorch
https://paperswithcode.com/paper/dualsmoke-sketch-based-smoke-illustration
DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Model
2208.10906
https://arxiv.org/abs/2208.10906v1
https://arxiv.org/pdf/2208.10906v1.pdf
https://github.com/shasph/dualsmoke
true
true
false
pytorch
https://paperswithcode.com/paper/pc-darts-partial-channel-connections-for
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
1907.05737
https://arxiv.org/abs/1907.05737v4
https://arxiv.org/pdf/1907.05737v4.pdf
https://github.com/yuhuixu1993/PC-DARTS
true
true
true
pytorch
https://paperswithcode.com/paper/variational-reasoning-for-question-answering
Variational Reasoning for Question Answering with Knowledge Graph
1709.04071
http://arxiv.org/abs/1709.04071v5
http://arxiv.org/pdf/1709.04071v5.pdf
https://github.com/yuyuz/Variational-Reasoning-Networks
false
false
false
none
https://paperswithcode.com/paper/arcface-additive-angular-margin-loss-for-deep
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
1801.07698
https://arxiv.org/abs/1801.07698v4
https://arxiv.org/pdf/1801.07698v4.pdf
https://github.com/rikichou/face_recognition_insight_face
false
false
true
mxnet
https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object
You Only Look Once: Unified, Real-Time Object Detection
1506.02640
http://arxiv.org/abs/1506.02640v5
http://arxiv.org/pdf/1506.02640v5.pdf
https://github.com/ankitAMD/Darkflow-object-detection
false
false
true
tf
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/ankitAMD/Darkflow-object-detection
false
false
true
tf
https://paperswithcode.com/paper/on-the-sample-complexity-of-graphical-model
On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes
1701.04724
https://arxiv.org/abs/1701.04724v5
https://arxiv.org/pdf/1701.04724v5.pdf
https://github.com/alexjungaalto/ResearchPublic
true
true
false
none
https://paperswithcode.com/paper/global-diagnostics-of-ionospheric-absorption
Global diagnostics of ionospheric absorption during X-ray solar flares based on 8-20MHz noise measured by over-the-horizon radars
1812.08878
http://arxiv.org/abs/1812.08878v1
http://arxiv.org/pdf/1812.08878v1.pdf
https://github.com/berng/sdGsRange
false
false
true
none
https://paperswithcode.com/paper/a-joint-detection-classification-model-for
A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data
1610.01797
http://arxiv.org/abs/1610.01797v1
http://arxiv.org/pdf/1610.01797v1.pdf
https://github.com/qiuqiangkong/audio_tagging_jdc
true
false
true
none
https://paperswithcode.com/paper/yolact-real-time-instance-segmentation
YOLACT: Real-time Instance Segmentation
1904.02689
https://arxiv.org/abs/1904.02689v2
https://arxiv.org/pdf/1904.02689v2.pdf
https://github.com/IntelligenceDatum/ICCV2019_Model_Compression
false
false
true
none
https://paperswithcode.com/paper/efficientad-accurate-visual-anomaly-detection
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
2303.14535
https://arxiv.org/abs/2303.14535v3
https://arxiv.org/pdf/2303.14535v3.pdf
https://github.com/danielsoy/EfficientAD
false
false
false
pytorch
https://paperswithcode.com/paper/subword-semantic-hashing-for-intent
Subword Semantic Hashing for Intent Classification on Small Datasets
1810.07150
https://arxiv.org/abs/1810.07150v3
https://arxiv.org/pdf/1810.07150v3.pdf
https://github.com/martinambition/IntentClassificationBenchmark
false
false
true
tf
https://paperswithcode.com/paper/progressive-pose-attention-transfer-for
Progressive Pose Attention Transfer for Person Image Generation
1904.03349
https://arxiv.org/abs/1904.03349v3
https://arxiv.org/pdf/1904.03349v3.pdf
https://github.com/zsypotter/pose_transfer_keypoint
false
false
true
pytorch
https://paperswithcode.com/paper/natural-language-inference-with-hierarchical
Sentence Embeddings in NLI with Iterative Refinement Encoders
1808.08762
https://arxiv.org/abs/1808.08762v2
https://arxiv.org/pdf/1808.08762v2.pdf
https://github.com/Helsinki-NLP/HBMP
true
true
true
pytorch
https://paperswithcode.com/paper/imagechd-a-3d-computed-tomography-image
ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease
2101.10799
https://arxiv.org/abs/2101.10799v2
https://arxiv.org/pdf/2101.10799v2.pdf
https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease
true
true
false
none
https://paperswithcode.com/paper/ampersand-argument-mining-for-persuasive-1
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
2004.14677
https://arxiv.org/abs/2004.14677v1
https://arxiv.org/pdf/2004.14677v1.pdf
https://github.com/tuhinjubcse/AMPERSAND-EMNLP2019
true
true
false
pytorch
https://paperswithcode.com/paper/end-to-end-learning-of-motion-representation
End-to-End Learning of Motion Representation for Video Understanding
1804.00413
http://arxiv.org/abs/1804.00413v1
http://arxiv.org/pdf/1804.00413v1.pdf
https://github.com/LijieFan/tvnet
false
false
false
tf
https://paperswithcode.com/paper/spike-and-slab-variable-selection-frequentist
Spike and slab variable selection: Frequentist and Bayesian strategies
math/0505633
https://arxiv.org/abs/math/0505633v1
https://arxiv.org/pdf/math/0505633v1.pdf
https://github.com/jskracht/MultiTimeModeling
false
false
true
tf
https://paperswithcode.com/paper/subword-semantic-hashing-for-intent
Subword Semantic Hashing for Intent Classification on Small Datasets
1810.07150
https://arxiv.org/abs/1810.07150v3
https://arxiv.org/pdf/1810.07150v3.pdf
https://github.com/kumar-shridhar/Know-Your-Intent
true
true
true
none
https://paperswithcode.com/paper/cellulyzer-automated-analysis-and-interactive
Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes
1703.02611
http://arxiv.org/abs/1703.02611v1
http://arxiv.org/pdf/1703.02611v1.pdf
https://github.com/nurlicht/CellulyzerDemo
true
true
true
none
https://paperswithcode.com/paper/seeing-things-from-a-different
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims
null
https://aclanthology.org/N19-1053
https://aclanthology.org/N19-1053.pdf
https://github.com/CogComp/perspectrum
true
true
false
none
https://paperswithcode.com/paper/multiple-context-features-in-siamese-networks
Multiple Context Features in Siamese Networks for Visual Object Tracking
null
http://thoth.inrialpes.fr/people/hmorimit/publications.php
http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Morimitsu_Multiple_Context_Features_in_Siamese_Networks_for_Visual_Object_Tracking_ECCVW_2018_paper.pdf
https://github.com/hmorimitsu/siam-mcf
false
false
false
tf
https://paperswithcode.com/paper/neural-fine-grained-entity-type
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
1803.03378
http://arxiv.org/abs/1803.03378v2
http://arxiv.org/pdf/1803.03378v2.pdf
https://github.com/billy-inn/NFETC
true
true
true
tf
https://paperswithcode.com/paper/getting-to-know-low-light-images-with-the
Getting to Know Low-light Images with The Exclusively Dark Dataset
1805.11227
http://arxiv.org/abs/1805.11227v1
http://arxiv.org/pdf/1805.11227v1.pdf
https://github.com/cs-chan/Exclusively-Dark-Image-Dataset
true
true
true
none
https://paperswithcode.com/paper/design-of-optical-neural-networks-with
Design of optical neural networks with component imprecisions
2001.01681
https://arxiv.org/abs/2001.01681v1
https://arxiv.org/pdf/2001.01681v1.pdf
https://github.com/mike-fang/imprecise_optical_neural_network
true
true
false
pytorch
https://paperswithcode.com/paper/artifact-disentanglement-network-for
Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
1906.01806
https://arxiv.org/abs/1906.01806v5
https://arxiv.org/pdf/1906.01806v5.pdf
https://github.com/liaohaofu/adn
true
true
true
pytorch
https://paperswithcode.com/paper/east-an-efficient-and-accurate-scene-text
EAST: An Efficient and Accurate Scene Text Detector
1704.03155
http://arxiv.org/abs/1704.03155v2
http://arxiv.org/pdf/1704.03155v2.pdf
https://github.com/Chris10M/RFB-Text-Detection
false
false
true
tf
https://paperswithcode.com/paper/receptive-field-block-net-for-accurate-and
Receptive Field Block Net for Accurate and Fast Object Detection
1711.07767
http://arxiv.org/abs/1711.07767v3
http://arxiv.org/pdf/1711.07767v3.pdf
https://github.com/Chris10M/RFB-Text-Detection
false
false
true
tf
https://paperswithcode.com/paper/end-to-end-object-detection-with-transformers
End-to-End Object Detection with Transformers
2005.12872
https://arxiv.org/abs/2005.12872v3
https://arxiv.org/pdf/2005.12872v3.pdf
https://github.com/tahmid0007/DETR_FineTune
false
false
true
pytorch
https://paperswithcode.com/paper/recurrent-neural-networks-in-linguistic
Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate
1807.04783
https://arxiv.org/abs/1807.04783v2
https://arxiv.org/pdf/1807.04783v2.pdf
https://github.com/ckirov/RevisitPinkerAndPrince
true
true
false
none
https://paperswithcode.com/paper/neural-module-networks
Neural Module Networks
1511.02799
http://arxiv.org/abs/1511.02799v4
http://arxiv.org/pdf/1511.02799v4.pdf
https://github.com/jacobandreas/nmn2
false
false
true
none
https://paperswithcode.com/paper/what-does-my-qa-model-know-devising
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
1912.13337
https://arxiv.org/abs/1912.13337v2
https://arxiv.org/pdf/1912.13337v2.pdf
https://github.com/allenai/semantic_fragments
true
true
true
none
https://paperswithcode.com/paper/a-new-primal-dual-algorithm-for-minimizing
A new primal-dual algorithm for minimizing the sum of three functions with a linear operator
1611.09805
http://arxiv.org/abs/1611.09805v4
http://arxiv.org/pdf/1611.09805v4.pdf
https://github.com/mingyan08/PD3O
true
true
false
none
https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object
You Only Look Once: Unified, Real-Time Object Detection
1506.02640
http://arxiv.org/abs/1506.02640v5
http://arxiv.org/pdf/1506.02640v5.pdf
https://github.com/abajaj945/Ship-Detection-using-Tensorflow
false
false
true
tf
https://paperswithcode.com/paper/lane-detection-and-classification-using
Lane Detection and Classification using Cascaded CNNs
1907.01294
https://arxiv.org/abs/1907.01294v2
https://arxiv.org/pdf/1907.01294v2.pdf
https://github.com/fabvio/Cascade-LD
true
true
true
pytorch
https://paperswithcode.com/paper/learning-fine-grained-image-similarity-with
Learning Fine-grained Image Similarity with Deep Ranking
1404.4661
http://arxiv.org/abs/1404.4661v1
http://arxiv.org/pdf/1404.4661v1.pdf
https://github.com/ArkinDharawat/DeepImageRanking
false
false
false
pytorch
https://paperswithcode.com/paper/a-strong-baseline-and-batch-normalization
A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
1906.08332
https://arxiv.org/abs/1906.08332v2
https://arxiv.org/pdf/1906.08332v2.pdf
https://github.com/NIRVANALAN/magnifiernet_reid
false
false
true
pytorch
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/gary-kaitung/data-science-portfolio
false
false
true
tf
https://paperswithcode.com/paper/once-for-all-train-one-network-and-specialize
Once-for-All: Train One Network and Specialize it for Efficient Deployment
1908.09791
https://arxiv.org/abs/1908.09791v5
https://arxiv.org/pdf/1908.09791v5.pdf
https://github.com/MaximIntegratedAI/ai8x-training
false
false
true
pytorch
https://paperswithcode.com/paper/a-sensitivity-analysis-of-and-practitioners
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
1510.03820
http://arxiv.org/abs/1510.03820v4
http://arxiv.org/pdf/1510.03820v4.pdf
https://github.com/jeonghunyoon/Text-classification-tensorflow
false
false
true
tf
https://paperswithcode.com/paper/mixture-based-multiple-imputation-models-for
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
1908.04209
https://arxiv.org/abs/1908.04209v3
https://arxiv.org/pdf/1908.04209v3.pdf
https://github.com/y-xue/MixMI
true
true
true
none
https://paperswithcode.com/paper/rule-applicability-on-rdf-triplestore-schemas
Rule Applicability on RDF Triplestore Schemas
1907.01627
https://arxiv.org/abs/1907.01627v1
https://arxiv.org/pdf/1907.01627v1.pdf
https://github.com/paolo7/ap2
true
true
false
none
https://paperswithcode.com/paper/reasoning-and-generalization-in-rl-a-tool-use
Reasoning and Generalization in RL: A Tool Use Perspective
1907.02050
https://arxiv.org/abs/1907.02050v1
https://arxiv.org/pdf/1907.02050v1.pdf
https://github.com/fomorians/gym_tool_use
true
true
false
none
https://paperswithcode.com/paper/an-attention-mechanism-for-musical-instrument
An Attention Mechanism for Musical Instrument Recognition
1907.04294
https://arxiv.org/abs/1907.04294v1
https://arxiv.org/pdf/1907.04294v1.pdf
https://github.com/SiddGururani/AttentionMIC
true
true
false
pytorch
https://paperswithcode.com/paper/a-mixture-of-experts-model-for-antonym
A Mixture-of-Experts Model for Antonym-Synonym Discrimination
null
https://aclanthology.org/2021.acl-short.71
https://aclanthology.org/2021.acl-short.71.pdf
https://github.com/zengnan1997/moe-asd
true
true
false
pytorch
https://paperswithcode.com/paper/learning-to-learn-stochastic-gradient-descent
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
1903.10399
http://arxiv.org/abs/1903.10399v1
http://arxiv.org/pdf/1903.10399v1.pdf
https://github.com/prolearner/onlineLTL
true
true
true
none
https://paperswithcode.com/paper/errudite-scalable-reproducible-and-testable
Errudite: Scalable, Reproducible, and Testable Error Analysis
null
https://aclanthology.org/P19-1073
https://aclanthology.org/P19-1073.pdf
https://github.com/uwdata/errudite
true
true
false
none
https://paperswithcode.com/paper/deep-knowledge-tracing
Deep Knowledge Tracing
1506.05908
http://arxiv.org/abs/1506.05908v1
http://arxiv.org/pdf/1506.05908v1.pdf
https://github.com/jarviszhb/KnowledgeTracing
false
false
true
pytorch
https://paperswithcode.com/paper/well-read-students-learn-better-the-impact-of
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
1908.08962
https://arxiv.org/abs/1908.08962v2
https://arxiv.org/pdf/1908.08962v2.pdf
https://github.com/google-research/bleurt
false
false
true
tf
https://paperswithcode.com/paper/towards-digital-retina-in-smart-cities-a
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
1907.13368
https://arxiv.org/abs/1907.13368v1
https://arxiv.org/pdf/1907.13368v1.pdf
https://github.com/PKU-IMRE/Retina
true
true
false
none
https://paperswithcode.com/paper/iterative-machine-teaching
Iterative Machine Teaching
1705.10470
http://arxiv.org/abs/1705.10470v3
http://arxiv.org/pdf/1705.10470v3.pdf
https://github.com/bariqi/Iterative-Mahine-Teaching-AML-Class
false
false
true
none
https://paperswithcode.com/paper/bmn-boundary-matching-network-for-temporal
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
1907.09702
https://arxiv.org/abs/1907.09702v1
https://arxiv.org/pdf/1907.09702v1.pdf
https://github.com/xlliu7/BMN-Boundary-Matching-Network
false
false
true
pytorch
https://paperswithcode.com/paper/memory-semantization-through-perturbed-and
Learning cortical representations through perturbed and adversarial dreaming
2109.04261
https://arxiv.org/abs/2109.04261v3
https://arxiv.org/pdf/2109.04261v3.pdf
https://github.com/NicoZenith/PAD
true
false
true
pytorch
https://paperswithcode.com/paper/sheaves-cosheaves-and-applications
Sheaves, Cosheaves and Applications
1303.3255
http://arxiv.org/abs/1303.3255v2
http://arxiv.org/pdf/1303.3255v2.pdf
https://github.com/lkastner/cellularSheaves
false
false
true
none
https://paperswithcode.com/paper/probo-a-framework-for-using-probabilistic
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
1901.11515
https://arxiv.org/abs/1901.11515v2
https://arxiv.org/pdf/1901.11515v2.pdf
https://github.com/willieneis/ProBO
true
true
true
none
https://paperswithcode.com/paper/projective-latent-space-decluttering
Projective Latent Interventions for Understanding and Fine-tuning Classifiers
2006.12902
https://arxiv.org/abs/2006.12902v2
https://arxiv.org/pdf/2006.12902v2.pdf
https://github.com/einbandi/latent-projective-interventions
false
false
true
pytorch
https://paperswithcode.com/paper/attention-is-all-you-need
Attention Is All You Need
1706.03762
https://arxiv.org/abs/1706.03762v7
https://arxiv.org/pdf/1706.03762v7.pdf
https://github.com/sarthaxxxxx/Attention-is-all-you-need
false
false
true
pytorch
https://paperswithcode.com/paper/laplacian-steered-neural-style-transfer
Laplacian-Steered Neural Style Transfer
1707.01253
http://arxiv.org/abs/1707.01253v2
http://arxiv.org/pdf/1707.01253v2.pdf
https://github.com/samwatts98/Fast-Neural-Style-Transfer-with-Laplacian-Loss-TensorFlow-1.13
false
false
true
tf
https://paperswithcode.com/paper/phrase-based-neural-unsupervised-machine
Phrase-Based & Neural Unsupervised Machine Translation
1804.07755
http://arxiv.org/abs/1804.07755v2
http://arxiv.org/pdf/1804.07755v2.pdf
https://github.com/keleog/PidginUNMT
false
false
true
pytorch
https://paperswithcode.com/paper/loss-in-translation-learning-bilingual-word
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
1804.07745
http://arxiv.org/abs/1804.07745v3
http://arxiv.org/pdf/1804.07745v3.pdf
https://github.com/keleog/PidginUNMT
false
false
true
pytorch
https://paperswithcode.com/paper/unsupervised-machine-translation-using
Unsupervised Machine Translation Using Monolingual Corpora Only
1711.00043
http://arxiv.org/abs/1711.00043v2
http://arxiv.org/pdf/1711.00043v2.pdf
https://github.com/keleog/PidginUNMT
false
false
true
pytorch
https://paperswithcode.com/paper/pfld-a-practical-facial-landmark-detector
PFLD: A Practical Facial Landmark Detector
1902.10859
http://arxiv.org/abs/1902.10859v2
http://arxiv.org/pdf/1902.10859v2.pdf
https://github.com/guoqiangqi/PFLD
false
false
true
tf
https://paperswithcode.com/paper/viewcrafter-taming-video-diffusion-models-for
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
2409.02048
https://arxiv.org/abs/2409.02048v1
https://arxiv.org/pdf/2409.02048v1.pdf
https://github.com/drexubery/viewcrafter
false
false
true
pytorch
https://paperswithcode.com/paper/explicable-reward-design-for-reinforcement
Explicable Reward Design for Reinforcement Learning Agents
null
http://proceedings.neurips.cc/paper/2021/hash/a7f0d2b95c60161b3f3c82f764b1d1c9-Abstract.html
http://proceedings.neurips.cc/paper/2021/file/a7f0d2b95c60161b3f3c82f764b1d1c9-Paper.pdf
https://github.com/adishs/neurips2021_explicable-reward-design_code
true
true
false
none
https://paperswithcode.com/paper/parameterized-quantum-circuits-as-machine
Parameterized quantum circuits as machine learning models
1906.07682
https://arxiv.org/abs/1906.07682v2
https://arxiv.org/pdf/1906.07682v2.pdf
https://github.com/UnofficialJuliaMirrorSnapshots/Yao.jl-5872b779-8223-5990-8dd0-5abbb0748c8c
false
false
true
none
https://paperswithcode.com/paper/explanations-based-on-the-missing-towards
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
1802.07623
http://arxiv.org/abs/1802.07623v2
http://arxiv.org/pdf/1802.07623v2.pdf
https://github.com/SeldonIO/alibi
false
false
false
tf
https://paperswithcode.com/paper/pelee-a-real-time-object-detection-system-on-1
Pelee: A Real-Time Object Detection System on Mobile Devices
null
http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices
http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices.pdf
https://github.com/ginn24/Pelee-TensorRT
false
false
false
none
https://paperswithcode.com/paper/pelee-a-real-time-object-detection-system-on
Pelee: A Real-Time Object Detection System on Mobile Devices
1804.06882
http://arxiv.org/abs/1804.06882v3
http://arxiv.org/pdf/1804.06882v3.pdf
https://github.com/ginn24/Pelee-TensorRT
false
false
false
none
https://paperswithcode.com/paper/artistic-style-transfer-for-videos-and
Artistic style transfer for videos and spherical images
1708.04538
http://arxiv.org/abs/1708.04538v3
http://arxiv.org/pdf/1708.04538v3.pdf
https://github.com/TanguyJeanneau/white-mirror
false
false
true
pytorch
https://paperswithcode.com/paper/residual-non-local-attention-networks-for-1
Residual Non-local Attention Networks for Image Restoration
1903.10082
http://arxiv.org/abs/1903.10082v1
http://arxiv.org/pdf/1903.10082v1.pdf
https://github.com/bruinxiong/RNAN
false
false
false
pytorch
https://paperswithcode.com/paper/in-conclusion-not-repetition-comprehensive
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes
1909.10852
https://arxiv.org/abs/1909.10852v2
https://arxiv.org/pdf/1909.10852v2.pdf
https://github.com/thinkwee/DPP_CNN_Summarization
true
true
true
pytorch
https://paperswithcode.com/paper/polsar-image-classification-based-on-dilated
PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain
1909.10783
https://arxiv.org/abs/1909.10783v2
https://arxiv.org/pdf/1909.10783v2.pdf
https://github.com/PROoshio/CRPM-Net
true
true
true
tf
https://paperswithcode.com/paper/on-the-automatic-generation-of-medical
On the Automatic Generation of Medical Imaging Reports
1711.08195
http://arxiv.org/abs/1711.08195v3
http://arxiv.org/pdf/1711.08195v3.pdf
https://github.com/Pillercottrer/radcap_project
false
false
true
pytorch
https://paperswithcode.com/paper/towards-accurate-scene-text-recognition-with
Towards Accurate Scene Text Recognition with Semantic Reasoning Networks
2003.12294
https://arxiv.org/abs/2003.12294v1
https://arxiv.org/pdf/2003.12294v1.pdf
https://github.com/Media-Smart/vedastr
false
false
false
pytorch
https://paperswithcode.com/paper/tagging-and-parsing-of-multidomain
Tagging and parsing of multidomain collections
null
http://www.dialog-21.ru/media/4961/sorokinaaplusetal-162.pdf
http://www.dialog-21.ru/media/4961/sorokinaaplusetal-162.pdf
https://github.com/AlexeySorokin/GramEval2020
false
true
false
none
https://paperswithcode.com/paper/intriguing-properties-of-neural-networks
Intriguing properties of neural networks
1312.6199
http://arxiv.org/abs/1312.6199v4
http://arxiv.org/pdf/1312.6199v4.pdf
https://github.com/huanzhang12/CROWN-Robustness-Certification
false
false
true
tf
https://paperswithcode.com/paper/image-to-image-translation-with-conditional
Image-to-Image Translation with Conditional Adversarial Networks
1611.07004
http://arxiv.org/abs/1611.07004v3
http://arxiv.org/pdf/1611.07004v3.pdf
https://github.com/miguel-rodrigo/dot-csv-pix2pix
false
false
true
tf
https://paperswithcode.com/paper/understanding-the-interactions-of-workloads
Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study
1902.07609
https://arxiv.org/abs/1902.07609v4
https://arxiv.org/pdf/1902.07609v4.pdf
https://github.com/CMU-SAFARI/GPGPUSim-Ramulator
true
true
true
none
https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
1905.11946
https://arxiv.org/abs/1905.11946v5
https://arxiv.org/pdf/1905.11946v5.pdf
https://github.com/narumiruna/efficientnet-pytorch
false
false
true
pytorch
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/tensorpack/tensorpack/tree/master/examples/A3C-Gym
false
false
true
tf
https://paperswithcode.com/paper/anmm-ranking-short-answer-texts-with
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
1801.01641
https://arxiv.org/abs/1801.01641v2
https://arxiv.org/pdf/1801.01641v2.pdf
https://github.com/yangliuy/aNMM-CIKM16
false
false
false
tf
https://paperswithcode.com/paper/tree-boosted-varying-coefficient-models
Tree Boosted Varying Coefficient Models
1904.01058
https://arxiv.org/abs/1904.01058v1
https://arxiv.org/pdf/1904.01058v1.pdf
https://github.com/siriuz42/treeboostVCM
false
false
true
none
https://paperswithcode.com/paper/adaboost-neural-network-and-cyclopean-view
Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment
null
https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment
https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment
https://github.com/o-messai/3DBIQA-AdaBoost
false
false
false
none
https://paperswithcode.com/paper/implicit-competitive-regularization-in-gans-1
Implicit competitive regularization in GANs
1910.05852
https://arxiv.org/abs/1910.05852v4
https://arxiv.org/pdf/1910.05852v4.pdf
https://github.com/devzhk/Implicit-Competitive-Regularization
false
false
true
pytorch
https://paperswithcode.com/paper/treelets-an-adaptive-multi-scale-basis-for
Treelets--An adaptive multi-scale basis for sparse unordered data
0707.0481
http://arxiv.org/abs/0707.0481v3
http://arxiv.org/pdf/0707.0481v3.pdf
https://github.com/hedixia/kernel_treelets
false
false
true
none
https://paperswithcode.com/paper/dynamic-computational-time-for-visual
Dynamic Computational Time for Visual Attention
1703.10332
http://arxiv.org/abs/1703.10332v3
http://arxiv.org/pdf/1703.10332v3.pdf
https://github.com/baidu-research/DT-RAM
true
true
true
torch
https://paperswithcode.com/paper/taper-time-aware-patient-ehr-representation
TAPER: Time-Aware Patient EHR Representation
1908.03971
https://arxiv.org/abs/1908.03971v4
https://arxiv.org/pdf/1908.03971v4.pdf
https://github.com/sajaddarabi/TAPER
true
true
true
pytorch
https://paperswithcode.com/paper/side-window-filtering
Side Window Filtering
1905.07177
https://arxiv.org/abs/1905.07177v1
https://arxiv.org/pdf/1905.07177v1.pdf
https://github.com/wang-kangkang/SideWindowFilter-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/support-vector-guided-softmax-loss-for-face
Support Vector Guided Softmax Loss for Face Recognition
1812.11317
http://arxiv.org/abs/1812.11317v1
http://arxiv.org/pdf/1812.11317v1.pdf
https://github.com/SevenZhan/Pytorch
false
false
true
pytorch