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https://paperswithcode.com/paper/on-the-interaction-effects-between-prediction
|
On the Interaction Effects Between Prediction and Clustering
|
1807.06713
|
http://arxiv.org/abs/1807.06713v2
|
http://arxiv.org/pdf/1807.06713v2.pdf
|
https://github.com/mbarnes1/B3
| false | false | true |
none
|
https://paperswithcode.com/paper/detecting-and-explaining-causes-from-text-for
|
Detecting and Explaining Causes From Text For a Time Series Event
|
1707.08852
|
http://arxiv.org/abs/1707.08852v1
|
http://arxiv.org/pdf/1707.08852v1.pdf
|
https://github.com/dykang/cgraph
| false | false | true |
none
|
https://paperswithcode.com/paper/dhsegment-a-generic-deep-learning-approach
|
dhSegment: A generic deep-learning approach for document segmentation
|
1804.10371
|
https://arxiv.org/abs/1804.10371v2
|
https://arxiv.org/pdf/1804.10371v2.pdf
|
https://github.com/dhlab-epfl/dhSegment
| true | true | true |
tf
|
https://paperswithcode.com/paper/on-the-convergence-of-the-sindy-algorithm
|
On the Convergence of the SINDy Algorithm
|
1805.06445
|
http://arxiv.org/abs/1805.06445v1
|
http://arxiv.org/pdf/1805.06445v1.pdf
|
https://github.com/linanzhang/SINDyConvergenceExamples
| true | true | true |
none
|
https://paperswithcode.com/paper/how-powerful-are-graph-neural-networks
|
How Powerful are Graph Neural Networks?
|
1810.00826
|
http://arxiv.org/abs/1810.00826v3
|
http://arxiv.org/pdf/1810.00826v3.pdf
|
https://github.com/calciver/Graph-Isomorphism-Networks
| false | false | true |
tf
|
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/prajwalgatti/DRL-Navigation
| false | false | true |
none
|
https://paperswithcode.com/paper/massive-dead-galaxies-at-z2-with-hst-grism
|
Massive Dead Galaxies at z~2 with HST Grism Spectroscopy I. Star Formation Histories and Metallicity Enrichment
|
1812.06980
|
https://arxiv.org/abs/1812.06980v2
|
https://arxiv.org/pdf/1812.06980v2.pdf
|
https://github.com/mtakahiro/gsf
| false | false | true |
none
|
https://paperswithcode.com/paper/versatile-verification-of-tree-ensembles
|
Versatile Verification of Tree Ensembles
|
2010.13880
|
https://arxiv.org/abs/2010.13880v2
|
https://arxiv.org/pdf/2010.13880v2.pdf
|
https://github.com/laudv/veritas
| true | false | true |
none
|
https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face
|
FaceNet: A Unified Embedding for Face Recognition and Clustering
|
1503.03832
|
http://arxiv.org/abs/1503.03832v3
|
http://arxiv.org/pdf/1503.03832v3.pdf
|
https://github.com/deepakks1995/inception
| false | false | true |
tf
|
https://paperswithcode.com/paper/localized-linear-regression-in-networked-data
|
Localized Linear Regression in Networked Data
|
1903.11178
|
https://arxiv.org/abs/1903.11178v2
|
https://arxiv.org/pdf/1903.11178v2.pdf
|
https://github.com/alexjungaalto/ResearchPublic
| true | true | false |
none
|
https://paperswithcode.com/paper/product-based-neural-networks-for-user
|
Product-based Neural Networks for User Response Prediction
|
1611.00144
|
http://arxiv.org/abs/1611.00144v1
|
http://arxiv.org/pdf/1611.00144v1.pdf
|
https://github.com/Atomu2014/product-nets-distributed
| false | false | true |
tf
|
https://paperswithcode.com/paper/domain-adapted-word-embeddings-for-improved
|
Domain Adapted Word Embeddings for Improved Sentiment Classification
|
1805.04576
|
http://arxiv.org/abs/1805.04576v1
|
http://arxiv.org/pdf/1805.04576v1.pdf
|
https://github.com/GallupGovt/multivac
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-new-approach-for-fine-tuning-sentence
|
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks
|
2410.13649
|
https://arxiv.org/abs/2410.13649v2
|
https://arxiv.org/pdf/2410.13649v2.pdf
|
https://github.com/slanglab-nu/autoencoder-oos
| true | true | false |
none
|
https://paperswithcode.com/paper/wasserstein-gan
|
Wasserstein GAN
|
1701.07875
|
http://arxiv.org/abs/1701.07875v3
|
http://arxiv.org/pdf/1701.07875v3.pdf
|
https://github.com/dabsdamoon/Anime-Colorization
| false | false | true |
mxnet
|
https://paperswithcode.com/paper/holoface-augmenting-human-to-human
|
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
|
1802.00278
|
http://arxiv.org/abs/1802.00278v1
|
http://arxiv.org/pdf/1802.00278v1.pdf
|
https://github.com/MarekKowalski/HoloFace
| true | true | true |
none
|
https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition
|
Adversarial Attacks and Defences Competition
|
1804.00097
|
http://arxiv.org/abs/1804.00097v1
|
http://arxiv.org/pdf/1804.00097v1.pdf
|
https://github.com/pfnet-research/nips17-adversarial-attack
| true | true | false |
tf
|
https://paperswithcode.com/paper/mcmc-using-hamiltonian-dynamics
|
MCMC using Hamiltonian dynamics
|
1206.1901
|
http://arxiv.org/abs/1206.1901v1
|
http://arxiv.org/pdf/1206.1901v1.pdf
|
https://github.com/ermongroup/a-nice-mc
| false | false | true |
tf
|
https://paperswithcode.com/paper/kafnets-kernel-based-non-parametric
|
Kafnets: kernel-based non-parametric activation functions for neural networks
|
1707.04035
|
http://arxiv.org/abs/1707.04035v2
|
http://arxiv.org/pdf/1707.04035v2.pdf
|
https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/photo-z-outlier-self-calibration-in-weak
|
Photo-z outlier self-calibration in weak lensing surveys
|
2007.12795
|
https://arxiv.org/abs/2007.12795v2
|
https://arxiv.org/pdf/2007.12795v2.pdf
|
https://github.com/EmmanuelSchaan/LaSSI
| true | true | false |
none
|
https://paperswithcode.com/paper/no-more-discrimination-cross-city-adaptation
|
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
|
1704.08509
|
http://arxiv.org/abs/1704.08509v1
|
http://arxiv.org/pdf/1704.08509v1.pdf
|
https://github.com/Sshanu/AdaptSegNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-to-adapt-structured-output-space-for
|
Learning to Adapt Structured Output Space for Semantic Segmentation
|
1802.10349
|
https://arxiv.org/abs/1802.10349v3
|
https://arxiv.org/pdf/1802.10349v3.pdf
|
https://github.com/Sshanu/AdaptSegNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/corpus-for-coreference-resolution-on
|
Corpus for Coreference Resolution on Scientific Papers
| null |
https://aclanthology.org/L14-1259
|
https://aclanthology.org/L14-1259.pdf
|
https://github.com/melsk125/SciCorefCorpus
| true | true | false |
none
|
https://paperswithcode.com/paper/cycle-in-cycle-generative-adversarial
|
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
|
1908.00999
|
https://arxiv.org/abs/1908.00999v3
|
https://arxiv.org/pdf/1908.00999v3.pdf
|
https://github.com/Ha0Tang/C2GAN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/macer-attack-free-and-scalable-robust-1
|
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
|
2001.02378
|
https://arxiv.org/abs/2001.02378v4
|
https://arxiv.org/pdf/2001.02378v4.pdf
|
https://github.com/RuntianZ/macer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/improved-analysis-of-the-subsampled
|
Improved analysis of the subsampled randomized Hadamard transform
|
1011.1595
|
http://arxiv.org/abs/1011.1595v4
|
http://arxiv.org/pdf/1011.1595v4.pdf
|
https://github.com/wushanshan/MatrixProductPCA
| false | false | true |
none
|
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/affinelayer/Pix2Pix-tensorflow
| false | false | true |
tf
|
https://paperswithcode.com/paper/fast-matrix-square-roots-with-applications-to
|
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
|
2006.11267
|
https://arxiv.org/abs/2006.11267v2
|
https://arxiv.org/pdf/2006.11267v2.pdf
|
https://github.com/gpleiss/ciq_experiments
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/neural-gpus-learn-algorithms
|
Neural GPUs Learn Algorithms
|
1511.08228
|
http://arxiv.org/abs/1511.08228v3
|
http://arxiv.org/pdf/1511.08228v3.pdf
|
https://github.com/openai/neural-gpu
| false | false | true |
tf
|
https://paperswithcode.com/paper/190500641
|
RetinaFace: Single-stage Dense Face Localisation in the Wild
|
1905.00641
|
https://arxiv.org/abs/1905.00641v2
|
https://arxiv.org/pdf/1905.00641v2.pdf
|
https://github.com/biubug6/Pytorch_Retinaface
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/cu-net-coupled-u-nets
|
CU-Net: Coupled U-Nets
|
1808.06521
|
http://arxiv.org/abs/1808.06521v1
|
http://arxiv.org/pdf/1808.06521v1.pdf
|
https://github.com/zhiqiangdon/CU-Net
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/few-shot-adversarial-learning-of-realistic
|
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
|
1905.08233
|
https://arxiv.org/abs/1905.08233v2
|
https://arxiv.org/pdf/1905.08233v2.pdf
|
https://github.com/shoutOutYangJie/Few-Shot-Adversarial-Learning-for-face-swap
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/a-local-block-coordinate-descent-algorithm
|
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
|
1811.00312
|
http://arxiv.org/abs/1811.00312v1
|
http://arxiv.org/pdf/1811.00312v1.pdf
|
https://github.com/EvZissel/LoBCoD
| false | false | true |
none
|
https://paperswithcode.com/paper/classification-with-costly-features-using
|
Classification with Costly Features using Deep Reinforcement Learning
|
1711.07364
|
http://arxiv.org/abs/1711.07364v2
|
http://arxiv.org/pdf/1711.07364v2.pdf
|
https://github.com/jaromiru/cwcf
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/dcfnet-discriminant-correlation-filters
|
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
|
1704.04057
|
http://arxiv.org/abs/1704.04057v1
|
http://arxiv.org/pdf/1704.04057v1.pdf
|
https://github.com/foolwood/DCFNet
| true | true | true |
none
|
https://paperswithcode.com/paper/rolling-horizon-evolutionary-algorithms-for
|
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
|
2003.12331
|
https://arxiv.org/abs/2003.12331v2
|
https://arxiv.org/pdf/2003.12331v2.pdf
|
https://github.com/two2tee/WorldModelPlanning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/towards-k-means-friendly-spaces-simultaneous
|
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
|
1610.04794
|
http://arxiv.org/abs/1610.04794v2
|
http://arxiv.org/pdf/1610.04794v2.pdf
|
https://github.com/MaziarMF/deep-k-means
| false | false | true |
tf
|
https://paperswithcode.com/paper/benchmarking-deep-reinforcement-learning-for
|
Benchmarking Deep Reinforcement Learning for Continuous Control
|
1604.06778
|
http://arxiv.org/abs/1604.06778v3
|
http://arxiv.org/pdf/1604.06778v3.pdf
|
https://github.com/cbfinn/maml_rl
| false | false | true |
tf
|
https://paperswithcode.com/paper/early-anomaly-detection-in-time-series-a
|
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
|
2010.11595
|
https://arxiv.org/abs/2010.11595v1
|
https://arxiv.org/pdf/2010.11595v1.pdf
|
https://github.com/vcerqueira/layered_learning_time_series
| true | true | false |
none
|
https://paperswithcode.com/paper/deep-spatio-temporal-residual-networks-for
|
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
|
1610.00081
|
http://arxiv.org/abs/1610.00081v2
|
http://arxiv.org/pdf/1610.00081v2.pdf
|
https://github.com/snehasinghania/STResNet
| false | false | true |
tf
|
https://paperswithcode.com/paper/swin-transformer-v2-scaling-up-capacity-and
|
Swin Transformer V2: Scaling Up Capacity and Resolution
|
2111.09883
|
https://arxiv.org/abs/2111.09883v2
|
https://arxiv.org/pdf/2111.09883v2.pdf
|
https://github.com/shinya7y/UniverseNet
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/practical-recommendations-for-gradient-based
|
Practical recommendations for gradient-based training of deep architectures
|
1206.5533
|
http://arxiv.org/abs/1206.5533v2
|
http://arxiv.org/pdf/1206.5533v2.pdf
|
https://github.com/Robinatp/Tensorflow_Model_Inception
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-statistical-extension-of-byte-pair-encoding
|
A Statistical Extension of Byte-Pair Encoding
| null |
https://aclanthology.org/2021.iwslt-1.31
|
https://aclanthology.org/2021.iwslt-1.31.pdf
|
https://github.com/amazon-research/statistical-byte-pair-encoding
| true | true | false |
none
|
https://paperswithcode.com/paper/a-survey-of-available-corpora-for-building
|
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
|
1512.05742
|
http://arxiv.org/abs/1512.05742v3
|
http://arxiv.org/pdf/1512.05742v3.pdf
|
https://github.com/hermesfeet/durga
| false | false | true |
none
|
https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q
|
Deep Reinforcement Learning with Double Q-learning
|
1509.06461
|
http://arxiv.org/abs/1509.06461v3
|
http://arxiv.org/pdf/1509.06461v3.pdf
|
https://github.com/FaboNo/DRLND
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/graphnvp-an-invertible-flow-model-for
|
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
|
1905.11600
|
https://arxiv.org/abs/1905.11600v1
|
https://arxiv.org/pdf/1905.11600v1.pdf
|
https://github.com/pfnet-research/graph-nvp
| false | false | true |
none
|
https://paperswithcode.com/paper/recent-trends-in-deep-learning-based-natural
|
Recent Trends in Deep Learning Based Natural Language Processing
|
1708.02709
|
http://arxiv.org/abs/1708.02709v8
|
http://arxiv.org/pdf/1708.02709v8.pdf
|
https://github.com/GallupGovt/multivac
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-to-see-in-the-dark
|
Learning to See in the Dark
|
1805.01934
|
http://arxiv.org/abs/1805.01934v1
|
http://arxiv.org/pdf/1805.01934v1.pdf
|
https://github.com/huyvnphan/Learning-To-See-In-The-Dark
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/linear-standard-model-extensions-in-the-smeft
|
Linear Standard Model extensions in the SMEFT at one loop and Tera-Z
|
2412.01759
|
https://arxiv.org/abs/2412.01759v1
|
https://arxiv.org/pdf/2412.01759v1.pdf
|
https://github.com/johngarg/MatchMakerParser
| true | false | true |
none
|
https://paperswithcode.com/paper/anonymity-for-practical-quantum-networks
|
Anonymity for practical quantum networks
|
1811.04729
|
http://arxiv.org/abs/1811.04729v1
|
http://arxiv.org/pdf/1811.04729v1.pdf
|
https://github.com/qis-unipr/qsip-practical-anonimity
| false | false | true |
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/hamzaMahdi/darknet
| 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/hamzaMahdi/darknet
| false | false | true |
tf
|
https://paperswithcode.com/paper/interpretable-multimodal-emotion-recognition
|
Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data
|
2208.11868
|
https://arxiv.org/abs/2208.11868v2
|
https://arxiv.org/pdf/2208.11868v2.pdf
|
https://github.com/mintelligence-group/speechimg_emorec
| true | true | false |
none
|
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/gokriznastic/torch-style-transfer
| false | false | true |
torch
|
https://paperswithcode.com/paper/mtfuzz-fuzzing-with-a-multi-task-neural
|
MTFuzz: Fuzzing with a Multi-Task Neural Network
|
2005.12392
|
https://arxiv.org/abs/2005.12392v1
|
https://arxiv.org/pdf/2005.12392v1.pdf
|
https://github.com/ARiSE-Lab/MTFuzz
| true | true | false |
tf
|
https://paperswithcode.com/paper/deep-single-view-3d-object-reconstruction
|
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
|
1809.03451
|
http://arxiv.org/abs/1809.03451v1
|
http://arxiv.org/pdf/1809.03451v1.pdf
|
https://github.com/qweas120/PSVH-3d-reconstruction
| false | false | true |
tf
|
https://paperswithcode.com/paper/going-retro-astonishingly-simple-yet
|
Going Retro: Astonishingly Simple Yet Effective Rule-based Prosody Modelling for Speech Synthesis Simulating Emotion Dimensions
|
2307.02132
|
https://arxiv.org/abs/2307.02132v1
|
https://arxiv.org/pdf/2307.02132v1.pdf
|
https://github.com/felixbur/syntact
| true | true | false |
none
|
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/natel9178/transformer-refork
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/localized-multiple-kernel-learning-for
|
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
|
1805.07892
|
http://arxiv.org/abs/1805.07892v4
|
http://arxiv.org/pdf/1805.07892v4.pdf
|
https://github.com/Chandan-IITI/LMKAD
| false | false | false |
none
|
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
|
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
|
1602.07261
|
http://arxiv.org/abs/1602.07261v2
|
http://arxiv.org/pdf/1602.07261v2.pdf
|
https://github.com/hujinxinb/face_detect
| false | false | true |
tf
|
https://paperswithcode.com/paper/naima-a-python-package-for-inference-of
|
naima: a Python package for inference of relativistic particle energy distributions from observed nonthermal spectra
|
1509.03319
|
https://arxiv.org/abs/1509.03319v1
|
https://arxiv.org/pdf/1509.03319v1.pdf
|
https://github.com/zblz/naima
| false | false | true |
none
|
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/AICIP-UTK/spacenet-round3
| false | false | true |
none
|
https://paperswithcode.com/paper/sequential-latent-knowledge-selection-for-1
|
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
|
2002.07510
|
https://arxiv.org/abs/2002.07510v2
|
https://arxiv.org/pdf/2002.07510v2.pdf
|
https://github.com/bckim92/sequential-knowledge-transformer
| true | false | false |
tf
|
https://paperswithcode.com/paper/rich-feature-hierarchies-for-accurate-object
|
Rich feature hierarchies for accurate object detection and semantic segmentation
|
1311.2524
|
http://arxiv.org/abs/1311.2524v5
|
http://arxiv.org/pdf/1311.2524v5.pdf
|
https://github.com/cftang0827/pedestrian_detection_ssdlite
| false | false | true |
tf
|
https://paperswithcode.com/paper/active-anomaly-detection-via-ensembles
|
Active Anomaly Detection via Ensembles
|
1809.06477
|
http://arxiv.org/abs/1809.06477v1
|
http://arxiv.org/pdf/1809.06477v1.pdf
|
https://github.com/freedombenLiu/ad_examples
| false | false | true |
tf
|
https://paperswithcode.com/paper/metaformer-is-actually-what-you-need-for
|
MetaFormer Is Actually What You Need for Vision
|
2111.11418
|
https://arxiv.org/abs/2111.11418v3
|
https://arxiv.org/pdf/2111.11418v3.pdf
|
https://github.com/shinya7y/UniverseNet
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/rethinking-supervised-learning-and
|
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
|
2009.09781
|
https://arxiv.org/abs/2009.09781v1
|
https://arxiv.org/pdf/2009.09781v1.pdf
|
https://github.com/cszmli/Rethink-RL-Sup
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/implementing-neural-turing-machines
|
Implementing Neural Turing Machines
|
1807.08518
|
http://arxiv.org/abs/1807.08518v3
|
http://arxiv.org/pdf/1807.08518v3.pdf
|
https://github.com/ajithcodesit/Neural_Turing_Machine
| false | false | true |
tf
|
https://paperswithcode.com/paper/neural-turing-machines
|
Neural Turing Machines
|
1410.5401
|
http://arxiv.org/abs/1410.5401v2
|
http://arxiv.org/pdf/1410.5401v2.pdf
|
https://github.com/ajithcodesit/Neural_Turing_Machine
| false | false | true |
tf
|
https://paperswithcode.com/paper/efficient-adaptive-mcmc-implementation-for
|
An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography
|
2106.00577
|
https://arxiv.org/abs/2106.00577v2
|
https://arxiv.org/pdf/2106.00577v2.pdf
|
https://github.com/tienmt/bqst
| true | true | false |
none
|
https://paperswithcode.com/paper/160701649
|
Randomized methods for matrix computations
|
1607.01649
|
http://arxiv.org/abs/1607.01649v3
|
http://arxiv.org/pdf/1607.01649v3.pdf
|
https://github.com/flame/randutv
| true | true | false |
none
|
https://paperswithcode.com/paper/debiasing-pre-trained-contextualised
|
Debiasing Pre-trained Contextualised Embeddings
|
2101.09523
|
https://arxiv.org/abs/2101.09523v1
|
https://arxiv.org/pdf/2101.09523v1.pdf
|
https://github.com/kanekomasahiro/context-debias
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/neuronal-circuit-policies
|
Neuronal Circuit Policies
|
1803.08554
|
http://arxiv.org/abs/1803.08554v1
|
http://arxiv.org/pdf/1803.08554v1.pdf
|
https://github.com/mlech26l/neuronal_circuit_policies
| true | true | false |
none
|
https://paperswithcode.com/paper/lstm-fully-convolutional-networks-for-time
|
LSTM Fully Convolutional Networks for Time Series Classification
|
1709.05206
|
http://arxiv.org/abs/1709.05206v1
|
http://arxiv.org/pdf/1709.05206v1.pdf
|
https://github.com/kmutya/Algorithms-for-Drowsy-Driver-Detection
| false | false | true |
none
|
https://paperswithcode.com/paper/assessing-the-ability-of-lstms-to-learn
|
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
|
1611.01368
|
http://arxiv.org/abs/1611.01368v1
|
http://arxiv.org/pdf/1611.01368v1.pdf
|
https://github.com/ketranm/fan_vs_rnn
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/are-red-roses-red-evaluating-consistency-of
|
Are Red Roses Red? Evaluating Consistency of Question-Answering Models
| null |
https://aclanthology.org/P19-1621
|
https://aclanthology.org/P19-1621.pdf
|
https://github.com/marcotcr/qa_consistency
| true | true | false |
tf
|
https://paperswithcode.com/paper/channel-wise-subband-input-for-better-voice
|
Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music
|
2008.05216
|
https://arxiv.org/abs/2008.05216v2
|
https://arxiv.org/pdf/2008.05216v2.pdf
|
https://github.com/haoheliu/Subband-Music-Separation
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/cogie-an-information-extraction-toolkit-for
|
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet
| null |
https://aclanthology.org/2021.acl-demo.11
|
https://aclanthology.org/2021.acl-demo.11.pdf
|
https://github.com/jinzhuoran/cogie
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector
|
SSD: Single Shot MultiBox Detector
|
1512.02325
|
http://arxiv.org/abs/1512.02325v5
|
http://arxiv.org/pdf/1512.02325v5.pdf
|
https://github.com/yuqj1990/face_train
| false | false | true |
tf
|
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/huan123/py-fatser-rcnn
| false | false | true |
tf
|
https://paperswithcode.com/paper/knowledge-based-automated-planning-with-three
|
Knowledge-based automated planning with three-dimensional generative adversarial networks
|
1812.09309
|
http://arxiv.org/abs/1812.09309v1
|
http://arxiv.org/pdf/1812.09309v1.pdf
|
https://github.com/rafidrm/gancer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/the-solovay-kitaev-algorithm
|
The Solovay-Kitaev algorithm
|
quant-ph/0505030
|
https://arxiv.org/abs/quant-ph/0505030v2
|
https://arxiv.org/pdf/quant-ph/0505030v2.pdf
|
https://github.com/vadym-kl/sqct
| false | false | true |
none
|
https://paperswithcode.com/paper/targeted-adversarial-examples-for-black-box
|
Targeted Adversarial Examples for Black Box Audio Systems
|
1805.07820
|
https://arxiv.org/abs/1805.07820v2
|
https://arxiv.org/pdf/1805.07820v2.pdf
|
https://github.com/rtaori/Black-Box-Audio
| true | true | true |
tf
|
https://paperswithcode.com/paper/two-sample-testing-for-event-impacts-in-time
|
Two-Sample Testing for Event Impacts in Time Series
|
2001.11930
|
https://arxiv.org/abs/2001.11930v1
|
https://arxiv.org/pdf/2001.11930v1.pdf
|
https://github.com/diozaka/eitest
| true | true | true |
none
|
https://paperswithcode.com/paper/fcsr-gan-joint-face-completion-and-super
|
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
|
1911.01045
|
https://arxiv.org/abs/1911.01045v1
|
https://arxiv.org/pdf/1911.01045v1.pdf
|
https://github.com/swordcheng/FCSR-GAN
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/soft-actor-critic-off-policy-maximum-entropy
|
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
|
1801.01290
|
http://arxiv.org/abs/1801.01290v2
|
http://arxiv.org/pdf/1801.01290v2.pdf
|
https://github.com/ccolas/rl_stats
| false | false | true |
none
|
https://paperswithcode.com/paper/clustering-in-partially-labeled-stochastic
|
Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization
|
1911.00958
|
https://arxiv.org/abs/1911.00958v2
|
https://arxiv.org/pdf/1911.00958v2.pdf
|
https://github.com/alexjungaalto/ResearchPublic
| true | true | false |
none
|
https://paperswithcode.com/paper/bayesian-entropy-estimation-for-binary-spike
|
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
| null |
http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge
|
http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge.pdf
|
https://github.com/pillowlab/CDMentropy
| true | true | false |
none
|
https://paperswithcode.com/paper/an-annotated-corpus-of-reference-resolution
|
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding
|
1911.07588
|
https://arxiv.org/abs/1911.07588v1
|
https://arxiv.org/pdf/1911.07588v1.pdf
|
https://github.com/Alab-NII/onecommon
| true | false | true |
tf
|
https://paperswithcode.com/paper/asgn-an-active-semi-supervised-graph-neural
|
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
|
2007.03196
|
https://arxiv.org/abs/2007.03196v1
|
https://arxiv.org/pdf/2007.03196v1.pdf
|
https://github.com/HaoZhongkai/AS_Molecule
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/complementary-similarity-learning-using
|
Complementary-Similarity Learning using Quadruplet Network
|
1908.09928
|
https://arxiv.org/abs/1908.09928v2
|
https://arxiv.org/pdf/1908.09928v2.pdf
|
https://github.com/mansimane/quadnet-comp-sim
| true | true | false |
tf
|
https://paperswithcode.com/paper/a-users-guide-to-carskit
|
A User's Guide to CARSKit
|
1511.03780
|
http://arxiv.org/abs/1511.03780v2
|
http://arxiv.org/pdf/1511.03780v2.pdf
|
https://github.com/sihcpro/TravelRecommendation
| false | false | true |
tf
|
https://paperswithcode.com/paper/the-exact-asymptotic-form-of-bayesian
|
The Exact Asymptotic Form of Bayesian Generalization Error in Latent Dirichlet Allocation
|
2008.01304
|
https://arxiv.org/abs/2008.01304v2
|
https://arxiv.org/pdf/2008.01304v2.pdf
|
https://github.com/chijan-nh/LearningCoefficient-RLCT-ofLDA-usingGS
| true | false | true |
none
|
https://paperswithcode.com/paper/learning-deep-representations-by-mutual
|
Learning deep representations by mutual information estimation and maximization
|
1808.06670
|
http://arxiv.org/abs/1808.06670v5
|
http://arxiv.org/pdf/1808.06670v5.pdf
|
https://github.com/createamind/DIM_Commented
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/blind-deblurring-using-gans
|
Blind Deblurring Using GANs
|
1907.11880
|
https://arxiv.org/abs/1907.11880v1
|
https://arxiv.org/pdf/1907.11880v1.pdf
|
https://github.com/lenka98/Bind-Deblurring-using-GANs
| true | true | true |
tf
|
https://paperswithcode.com/paper/single-pass-pca-of-matrix-products
|
Single Pass PCA of Matrix Products
|
1610.06656
|
http://arxiv.org/abs/1610.06656v2
|
http://arxiv.org/pdf/1610.06656v2.pdf
|
https://github.com/wushanshan/MatrixProductPCA
| true | true | true |
none
|
https://paperswithcode.com/paper/equalizing-gender-biases-in-neural-machine
|
Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques
|
1901.03116
|
https://arxiv.org/abs/1901.03116v2
|
https://arxiv.org/pdf/1901.03116v2.pdf
|
https://github.com/joelescudefont/genbiasmt
| true | true | true |
none
|
https://paperswithcode.com/paper/graph-attention-networks
|
Graph Attention Networks
|
1710.10903
|
http://arxiv.org/abs/1710.10903v3
|
http://arxiv.org/pdf/1710.10903v3.pdf
|
https://github.com/liu6zijian/simplified-gcn-model
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/efficient-semantic-scene-completion-network-1
|
Efficient Semantic Scene Completion Network with Spatial Group Convolution
|
1907.05091
|
https://arxiv.org/abs/1907.05091v1
|
https://arxiv.org/pdf/1907.05091v1.pdf
|
https://github.com/zjhthu/SGC-Release
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-natural-language-corpus-of-common-grounding
|
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
|
1907.03399
|
https://arxiv.org/abs/1907.03399v1
|
https://arxiv.org/pdf/1907.03399v1.pdf
|
https://github.com/Alab-NII/onecommon
| true | true | true |
tf
|
https://paperswithcode.com/paper/learnable-hollow-kernels-for-anatomical
|
LORCK: Learnable Object-Resembling Convolution Kernels
|
2007.05103
|
https://arxiv.org/abs/2007.05103v2
|
https://arxiv.org/pdf/2007.05103v2.pdf
|
https://github.com/cviaai/LEARNABLE-HOLLOW-KERNELS
| true | true | false |
pytorch
|
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