paper_url
stringlengths 36
81
| paper_title
stringlengths 1
242
⌀ | paper_arxiv_id
stringlengths 9
16
⌀ | paper_url_abs
stringlengths 18
314
| paper_url_pdf
stringlengths 21
935
⌀ | repo_url
stringlengths 26
200
| is_official
bool 2
classes | mentioned_in_paper
bool 2
classes | mentioned_in_github
bool 2
classes | framework
stringclasses 9
values |
|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/rosetta-large-scale-system-for-text-detection
|
Rosetta: Large scale system for text detection and recognition in images
|
1910.05085
|
https://arxiv.org/abs/1910.05085v1
|
https://arxiv.org/pdf/1910.05085v1.pdf
|
https://github.com/Media-Smart/vedastr
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/scde-sentence-cloze-dataset-with-high-quality
|
SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations
|
2004.12934
|
https://arxiv.org/abs/2004.12934v1
|
https://arxiv.org/pdf/2004.12934v1.pdf
|
https://github.com/shawnkx/SCDE
| true | true | true |
pytorch
|
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/aquib1011/Neural-Style-Transfer
| false | false | true |
tf
|
https://paperswithcode.com/paper/adding-gradient-noise-improves-learning-for
|
Adding Gradient Noise Improves Learning for Very Deep Networks
|
1511.06807
|
http://arxiv.org/abs/1511.06807v1
|
http://arxiv.org/pdf/1511.06807v1.pdf
|
https://github.com/ketranm/neuralHMM
| false | false | true |
torch
|
https://paperswithcode.com/paper/faster-policy-learning-with-continuous-time
|
Faster Policy Learning with Continuous-Time Gradients
|
2012.06684
|
https://arxiv.org/abs/2012.06684v2
|
https://arxiv.org/pdf/2012.06684v2.pdf
|
https://github.com/samuela/ctpg
| true | true | true |
none
|
https://paperswithcode.com/paper/fully-dynamic-space-efficient-dictionaries
|
Fully-Dynamic Space-Efficient Dictionaries and Filters with Constant Number of Memory Accesses
|
1911.05060
|
https://arxiv.org/abs/1911.05060v1
|
https://arxiv.org/pdf/1911.05060v1.pdf
|
https://github.com/TomerEven/Pocket_Dictionary
| false | false | true |
none
|
https://paperswithcode.com/paper/lightseq-a-high-performance-inference-library
|
LightSeq: A High Performance Inference Library for Transformers
|
2010.13887
|
https://arxiv.org/abs/2010.13887v4
|
https://arxiv.org/pdf/2010.13887v4.pdf
|
https://github.com/bytedance/lightseq
| true | true | false |
tf
|
https://paperswithcode.com/paper/efficient-simple-and-automated-negative
|
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
|
2010.14227
|
https://arxiv.org/abs/2010.14227v2
|
https://arxiv.org/pdf/2010.14227v2.pdf
|
https://github.com/AutoML-4Paradigm/NSCaching
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/continuous-regularized-wasserstein
|
Continuous Regularized Wasserstein Barycenters
|
2008.12534
|
https://arxiv.org/abs/2008.12534v2
|
https://arxiv.org/pdf/2008.12534v2.pdf
|
https://github.com/lingxiaoli94/CWB
| true | true | true |
tf
|
https://paperswithcode.com/paper/sparse-transition-matrix-estimation-for-high
|
Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models
|
1604.04002
|
https://arxiv.org/abs/1604.04002v5
|
https://arxiv.org/pdf/1604.04002v5.pdf
|
https://github.com/UBCDingXin/TVVAR
| true | false | false |
none
|
https://paperswithcode.com/paper/policy-information-capacity-information
|
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
|
2103.12726
|
https://arxiv.org/abs/2103.12726v2
|
https://arxiv.org/pdf/2103.12726v2.pdf
|
https://github.com/frt03/pic
| true | true | true |
none
|
https://paperswithcode.com/paper/resnest-split-attention-networks
|
ResNeSt: Split-Attention Networks
|
2004.08955
|
https://arxiv.org/abs/2004.08955v2
|
https://arxiv.org/pdf/2004.08955v2.pdf
|
https://github.com/STomoya/ResNeSt
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/the-n-tuple-bandit-evolutionary-algorithm-for
|
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
|
1802.05991
|
http://arxiv.org/abs/1802.05991v2
|
http://arxiv.org/pdf/1802.05991v2.pdf
|
https://github.com/two2tee/WorldModelPlanning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
|
YOLOv4: Optimal Speed and Accuracy of Object Detection
|
2004.10934
|
https://arxiv.org/abs/2004.10934v1
|
https://arxiv.org/pdf/2004.10934v1.pdf
|
https://github.com/wuzhihao7788/yolodet-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/few-cost-salient-object-detection-with-1
|
Few-Cost Salient Object Detection with Adversarial-Paced Learning
|
2104.01928
|
https://arxiv.org/abs/2104.01928v1
|
https://arxiv.org/pdf/2104.01928v1.pdf
|
https://github.com/hb-stone/FC-SOD
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/an-entropic-associative-memory
|
An Entropic Associative Memory
|
2009.13058
|
https://arxiv.org/abs/2009.13058v1
|
https://arxiv.org/pdf/2009.13058v1.pdf
|
https://github.com/LA-Pineda/Associative-Memory-Experiments
| true | true | true |
tf
|
https://paperswithcode.com/paper/type-b-reflexivization-as-an-unambiguous
|
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
|
2009.11982
|
https://arxiv.org/abs/2009.11982v2
|
https://arxiv.org/pdf/2009.11982v2.pdf
|
https://github.com/anavaleriagonzalez/ABC-dataset
| true | true | false |
none
|
https://paperswithcode.com/paper/a-deep-neural-network-tool-for-automatic
|
A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
|
2009.09900
|
https://arxiv.org/abs/2009.09900v1
|
https://arxiv.org/pdf/2009.09900v1.pdf
|
https://github.com/nih-fmrif/MLT_Body_Part_Segmentation
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/the-importance-of-pessimism-in-fixed-dataset
|
The Importance of Pessimism in Fixed-Dataset Policy Optimization
|
2009.06799
|
https://arxiv.org/abs/2009.06799v3
|
https://arxiv.org/pdf/2009.06799v3.pdf
|
https://github.com/jbuckman/tiopifdpo
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/the-1st-tiny-object-detection-challenge
|
The 1st Tiny Object Detection Challenge:Methods and Results
|
2009.07506
|
https://arxiv.org/abs/2009.07506v2
|
https://arxiv.org/pdf/2009.07506v2.pdf
|
https://github.com/ucas-vg/TinyBenchmark
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/jax-md-a-framework-for-differentiable-physics
|
JAX MD: A Framework for Differentiable Physics
| null |
http://proceedings.neurips.cc/paper/2020/hash/83d3d4b6c9579515e1679aca8cbc8033-Abstract.html
|
http://proceedings.neurips.cc/paper/2020/file/83d3d4b6c9579515e1679aca8cbc8033-Paper.pdf
|
https://github.com/google/jax-md
| true | true | false |
jax
|
https://paperswithcode.com/paper/training-sparse-neural-networks-using
|
Training Sparse Neural Networks using Compressed Sensing
|
2008.09661
|
https://arxiv.org/abs/2008.09661v2
|
https://arxiv.org/pdf/2008.09661v2.pdf
|
https://github.com/jwsiegel2510/xrda-sparse-training
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/the-effects-of-mild-over-parameterization-on
|
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks
|
2006.01005
|
https://arxiv.org/abs/2006.01005v2
|
https://arxiv.org/pdf/2006.01005v2.pdf
|
https://github.com/ItaySafran/Overparameterization
| true | true | true |
none
|
https://paperswithcode.com/paper/coolmomentum-a-method-for-stochastic
|
CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing
|
2005.14605
|
https://arxiv.org/abs/2005.14605v2
|
https://arxiv.org/pdf/2005.14605v2.pdf
|
https://github.com/borbysh/coolmomentum
| true | true | true |
tf
|
https://paperswithcode.com/paper/rethinking-performance-estimation-in-neural
|
Rethinking Performance Estimation in Neural Architecture Search
|
2005.09917
|
https://arxiv.org/abs/2005.09917v1
|
https://arxiv.org/pdf/2005.09917v1.pdf
|
https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/semi-supervised-medical-image-classification
|
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
|
2005.07377
|
https://arxiv.org/abs/2005.07377v1
|
https://arxiv.org/pdf/2005.07377v1.pdf
|
https://github.com/liuquande/SRC-MT
| false | true | false |
pytorch
|
https://paperswithcode.com/paper/enabling-language-models-to-fill-in-the
|
Enabling Language Models to Fill in the Blanks
|
2005.05339
|
https://arxiv.org/abs/2005.05339v2
|
https://arxiv.org/pdf/2005.05339v2.pdf
|
https://github.com/chrisdonahue/ilm
| true | true | true |
none
|
https://paperswithcode.com/paper/generalized-state-dependent-exploration-for
|
Smooth Exploration for Robotic Reinforcement Learning
|
2005.05719
|
https://arxiv.org/abs/2005.05719v2
|
https://arxiv.org/pdf/2005.05719v2.pdf
|
https://github.com/DLR-RM/stable-baselines3
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/learning-non-monotonic-automatic-post-editing
|
Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
|
2004.14120
|
https://arxiv.org/abs/2004.14120v2
|
https://arxiv.org/pdf/2004.14120v2.pdf
|
https://github.com/antoniogois/keystrokes_ape
| true | true | false |
none
|
https://paperswithcode.com/paper/think-locally-act-globally-federated-learning
|
Think Locally, Act Globally: Federated Learning with Local and Global Representations
|
2001.01523
|
https://arxiv.org/abs/2001.01523v3
|
https://arxiv.org/pdf/2001.01523v3.pdf
|
https://github.com/pliang279/LG-FedAvg
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/the-near-optimal-feasible-space-of-a
|
The Near-Optimal Feasible Space of a Renewable Power System Model
|
1910.01891
|
https://arxiv.org/abs/1910.01891v1
|
https://arxiv.org/pdf/1910.01891v1.pdf
|
https://github.com/PyPSA/pypsa-eur-mga
| true | true | true |
none
|
https://paperswithcode.com/paper/identifying-galaxies-quasars-and-stars-with
|
Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra
|
1909.10963
|
https://arxiv.org/abs/1909.10963v1
|
https://arxiv.org/pdf/1909.10963v1.pdf
|
https://github.com/informationcake/SDSS-ML
| true | true | true |
none
|
https://paperswithcode.com/paper/effective-approaches-to-attention-based
|
Effective Approaches to Attention-based Neural Machine Translation
|
1508.04025
|
http://arxiv.org/abs/1508.04025v5
|
http://arxiv.org/pdf/1508.04025v5.pdf
|
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/attention-based-models-for-speech-recognition
|
Attention-Based Models for Speech Recognition
|
1506.07503
|
http://arxiv.org/abs/1506.07503v1
|
http://arxiv.org/pdf/1506.07503v1.pdf
|
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/discovering-subdimensional-motifs-of
|
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series
|
1911.09218
|
https://arxiv.org/abs/1911.09218v1
|
https://arxiv.org/pdf/1911.09218v1.pdf
|
https://github.com/flash121123/CHIME
| true | true | false |
none
|
https://paperswithcode.com/paper/driver-identification-based-on-vehicle
|
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network
|
1911.08030
|
https://arxiv.org/abs/1911.08030v1
|
https://arxiv.org/pdf/1911.08030v1.pdf
|
https://github.com/Abeni18/Deep-LSTM-for-Driver-Identification-
| true | true | false |
tf
|
https://paperswithcode.com/paper/self-supervised-gan-analysis-and-improvement-1
|
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
|
1911.06997
|
https://arxiv.org/abs/1911.06997v2
|
https://arxiv.org/pdf/1911.06997v2.pdf
|
https://github.com/tntrung/msgan
| true | true | false |
tf
|
https://paperswithcode.com/paper/learning-to-optimize-in-swarms
|
Learning to Optimize in Swarms
|
1911.03787
|
https://arxiv.org/abs/1911.03787v2
|
https://arxiv.org/pdf/1911.03787v2.pdf
|
https://github.com/Shen-Lab/LOIS
| true | true | true |
tf
|
https://paperswithcode.com/paper/crown-conversational-passage-ranking-by
|
CROWN: Conversational Passage Ranking by Reasoning over Word Networks
|
1911.02850
|
https://arxiv.org/abs/1911.02850v3
|
https://arxiv.org/pdf/1911.02850v3.pdf
|
https://github.com/magkai/CROWN
| true | true | false |
none
|
https://paperswithcode.com/paper/flen-leveraging-field-for-scalable-ctr
|
FLEN: Leveraging Field for Scalable CTR Prediction
|
1911.04690
|
https://arxiv.org/abs/1911.04690v4
|
https://arxiv.org/pdf/1911.04690v4.pdf
|
https://github.com/aimetrics/jarvis
| true | true | true |
tf
|
https://paperswithcode.com/paper/listen-attend-and-spell
|
Listen, Attend and Spell
|
1508.01211
|
http://arxiv.org/abs/1508.01211v2
|
http://arxiv.org/pdf/1508.01211v2.pdf
|
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/augmenting-and-tuning-knowledge-graph
|
Augmenting and Tuning Knowledge Graph Embeddings
|
1907.01068
|
https://arxiv.org/abs/1907.01068v1
|
https://arxiv.org/pdf/1907.01068v1.pdf
|
https://github.com/mandt-lab/knowledge-graph-tuning
| true | false | false |
tf
|
https://paperswithcode.com/paper/neural-networks-hypersurfaces-and-radon
|
Neural Networks, Hypersurfaces, and Radon Transforms
|
1907.02220
|
https://arxiv.org/abs/1907.02220v1
|
https://arxiv.org/pdf/1907.02220v1.pdf
|
https://github.com/rohdelab/radon-neural-network
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/uncovering-the-semantics-of-wikipedia
|
Uncovering the Semantics of Wikipedia Categories
|
1906.12089
|
https://arxiv.org/abs/1906.12089v1
|
https://arxiv.org/pdf/1906.12089v1.pdf
|
https://github.com/nheist/Cat2Ax
| true | true | true |
none
|
https://paperswithcode.com/paper/signed-graph-attention-networks
|
Signed Graph Attention Networks
|
1906.10958
|
https://arxiv.org/abs/1906.10958v3
|
https://arxiv.org/pdf/1906.10958v3.pdf
|
https://github.com/huangjunjie95/SiGAT
| true | true | true |
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/michuanhaohao/reid-strong-baseline
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/be-consistent-improving-procedural-text-1
|
Be Consistent! Improving Procedural Text Comprehension using Label Consistency
|
1906.08942
|
https://arxiv.org/abs/1906.08942v1
|
https://arxiv.org/pdf/1906.08942v1.pdf
|
https://github.com/allenai/propara
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-general-methodology-to-assess-symbolic
|
Symbolic regression by uniform random global search
|
1906.07848
|
https://arxiv.org/abs/1906.07848v4
|
https://arxiv.org/pdf/1906.07848v4.pdf
|
https://github.com/pySRURGS/pySRURGS
| true | true | false |
none
|
https://paperswithcode.com/paper/attention-based-multi-input-deep-learning
|
Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors
|
1906.05168
|
https://arxiv.org/abs/1906.05168v3
|
https://arxiv.org/pdf/1906.05168v3.pdf
|
https://github.com/lehgtrung/egfr-att
| false | true | false |
pytorch
|
https://paperswithcode.com/paper/robust-quad-tree-based-registration-of-whole
|
Robust quad-tree based registration of whole slide images
| null |
https://openreview.net/forum?id=bR73MlqorwK
|
https://openreview.net/pdf?id=bR73MlqorwK
|
https://github.com/christianmarzahl/wsiregistration
| true | true | false |
none
|
https://paperswithcode.com/paper/deepcomplexmri-exploiting-deep-residual
|
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
|
1906.04359
|
https://arxiv.org/abs/1906.04359v2
|
https://arxiv.org/pdf/1906.04359v2.pdf
|
https://github.com/CedricChing/DeepMRI
| true | true | false |
tf
|
https://paperswithcode.com/paper/bags-an-automatic-homework-grading-system
|
BAGS: An automatic homework grading system using the pictures taken by smart phones
|
1906.03767
|
https://arxiv.org/abs/1906.03767v1
|
https://arxiv.org/pdf/1906.03767v1.pdf
|
https://github.com/boxfish-ai/BAGS
| true | false | true |
none
|
https://paperswithcode.com/paper/digging-into-self-supervised-monocular-depth
|
Digging Into Self-Supervised Monocular Depth Estimation
|
1806.01260
|
https://arxiv.org/abs/1806.01260v4
|
https://arxiv.org/pdf/1806.01260v4.pdf
|
https://github.com/jzwqaq/monodepth_jzw
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/190513128
|
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
|
1905.13128
|
https://arxiv.org/abs/1905.13128v2
|
https://arxiv.org/pdf/1905.13128v2.pdf
|
https://github.com/Oligou/dcPF
| true | true | false |
none
|
https://paperswithcode.com/paper/a-resource-allocation-based-approach-for
|
Mobility Offer Allocations in Corporate Settings
|
1810.05659
|
https://arxiv.org/abs/1810.05659v3
|
https://arxiv.org/pdf/1810.05659v3.pdf
|
https://github.com/dts-ait/seamless
| true | true | false |
none
|
https://paperswithcode.com/paper/integrability-of-geodesics-of-totally
|
Integrability of geodesics of totally geodesic metrics
|
1810.00962
|
http://arxiv.org/abs/1810.00962v1
|
http://arxiv.org/pdf/1810.00962v1.pdf
|
https://github.com/rkycia/GeodesicsIntegrability
| true | true | true |
none
|
https://paperswithcode.com/paper/unsupervised-controllable-text-formalization
|
Unsupervised Controllable Text Formalization
|
1809.04556
|
http://arxiv.org/abs/1809.04556v6
|
http://arxiv.org/pdf/1809.04556v6.pdf
|
https://github.com/parajain/uctf
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-unified-feature-disentangler-for-multi
|
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
|
1809.01361
|
http://arxiv.org/abs/1809.01361v3
|
http://arxiv.org/pdf/1809.01361v3.pdf
|
https://github.com/Alexander-H-Liu/UFDN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/mcrm-mother-compact-recurrent-memory
|
MCRM: Mother Compact Recurrent Memory
|
1808.02016
|
https://arxiv.org/abs/1808.02016v3
|
https://arxiv.org/pdf/1808.02016v3.pdf
|
https://github.com/abduallahmohamed/MCRM
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/detecting-core-periphery-structure-in-spatial
|
Detecting Core-Periphery Structure in Spatial Networks
|
1808.06544
|
http://arxiv.org/abs/1808.06544v1
|
http://arxiv.org/pdf/1808.06544v1.pdf
|
https://github.com/000Justin000/spatial_core_periphery
| true | true | true |
none
|
https://paperswithcode.com/paper/quantized-densely-connected-u-nets-for
|
Quantized Densely Connected U-Nets for Efficient Landmark Localization
|
1808.02194
|
http://arxiv.org/abs/1808.02194v2
|
http://arxiv.org/pdf/1808.02194v2.pdf
|
https://github.com/zhiqiangdon/CU-Net
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/news-article-teaser-tweets-and-how-to
|
News Article Teaser Tweets and How to Generate Them
|
1807.11535
|
http://arxiv.org/abs/1807.11535v2
|
http://arxiv.org/pdf/1807.11535v2.pdf
|
https://github.com/sanjeevkrn/teaser_collect
| true | true | false |
none
|
https://paperswithcode.com/paper/interpretable-charge-predictions-for-criminal
|
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
|
1802.08504
|
http://arxiv.org/abs/1802.08504v1
|
http://arxiv.org/pdf/1802.08504v1.pdf
|
https://github.com/oceanypt/Court-View-Gen
| true | true | false |
none
|
https://paperswithcode.com/paper/var-cnn-and-dynaflow-improved-attacks-and
|
Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning
|
1802.10215
|
https://arxiv.org/abs/1802.10215v2
|
https://arxiv.org/pdf/1802.10215v2.pdf
|
https://github.com/sanjit-bhat/Var-CNN
| true | true | true |
tf
|
https://paperswithcode.com/paper/bayesian-optimization-with-expensive
|
Bayesian Optimization with Expensive Integrands
|
1803.08661
|
http://arxiv.org/abs/1803.08661v1
|
http://arxiv.org/pdf/1803.08661v1.pdf
|
https://github.com/toscanosaul/bayesian_quadrature_optimization
| true | true | false |
none
|
https://paperswithcode.com/paper/a-new-solution-to-the-curved-ewald-sphere
|
A new solution to the curved Ewald sphere problem for 3D image reconstruction in electron microscopy
|
2101.11709
|
https://arxiv.org/abs/2101.11709v2
|
https://arxiv.org/pdf/2101.11709v2.pdf
|
https://gitlab.com/jpchen1/em-reconstruction-with-ewald
| true | false | false |
none
|
https://paperswithcode.com/paper/hate-lingo-a-target-based-linguistic-analysis
|
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
|
1804.04257
|
http://arxiv.org/abs/1804.04257v1
|
http://arxiv.org/pdf/1804.04257v1.pdf
|
https://github.com/mayelsherif/hate_speech_icwsm18
| true | true | false |
none
|
https://paperswithcode.com/paper/visualisation-and-diagnostic-classifiers
|
Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
|
1711.10203
|
http://arxiv.org/abs/1711.10203v2
|
http://arxiv.org/pdf/1711.10203v2.pdf
|
https://github.com/dieuwkehupkes/processing_arithmetics
| true | true | false |
none
|
https://paperswithcode.com/paper/deep-neural-network-based-subspace-learning
|
Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping
|
1804.08951
|
http://arxiv.org/abs/1804.08951v2
|
http://arxiv.org/pdf/1804.08951v2.pdf
|
https://github.com/liaopeiyuan/Subspace-Learning
| true | true | true |
none
|
https://paperswithcode.com/paper/multi-task-learning-for-argumentation-mining
|
Multi-Task Learning for Argumentation Mining in Low-Resource Settings
|
1804.04083
|
http://arxiv.org/abs/1804.04083v3
|
http://arxiv.org/pdf/1804.04083v3.pdf
|
https://github.com/UKPLab/naacl18-multitask_argument_mining
| true | true | false |
none
|
https://paperswithcode.com/paper/addressing-two-problems-in-deep-knowledge
|
Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization
|
1806.02180
|
http://arxiv.org/abs/1806.02180v1
|
http://arxiv.org/pdf/1806.02180v1.pdf
|
https://github.com/ckyeungac/deep-knowledge-tracing-plus
| true | true | true |
tf
|
https://paperswithcode.com/paper/deep-ordinal-regression-network-for-monocular
|
Deep Ordinal Regression Network for Monocular Depth Estimation
|
1806.02446
|
http://arxiv.org/abs/1806.02446v1
|
http://arxiv.org/pdf/1806.02446v1.pdf
|
https://github.com/hufu6371/DORN
| true | true | true |
none
|
https://paperswithcode.com/paper/analyzing-uncertainty-in-neural-machine
|
Analyzing Uncertainty in Neural Machine Translation
|
1803.00047
|
http://arxiv.org/abs/1803.00047v4
|
http://arxiv.org/pdf/1803.00047v4.pdf
|
https://github.com/facebookresearch/analyzing-uncertainty-nmt
| true | true | true |
none
|
https://paperswithcode.com/paper/learning-dynamics-of-linear-denoising
|
Learning Dynamics of Linear Denoising Autoencoders
|
1806.05413
|
http://arxiv.org/abs/1806.05413v2
|
http://arxiv.org/pdf/1806.05413v2.pdf
|
https://github.com/arnupretorius/lindaedynamics_icml2018
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-nice-mc-adversarial-training-for-mcmc
|
A-NICE-MC: Adversarial Training for MCMC
|
1706.07561
|
http://arxiv.org/abs/1706.07561v3
|
http://arxiv.org/pdf/1706.07561v3.pdf
|
https://github.com/ermongroup/a-nice-mc
| true | true | true |
tf
|
https://paperswithcode.com/paper/multi-task-learning-by-deep-collaboration-and
|
Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection
|
1711.00111
|
http://arxiv.org/abs/1711.00111v2
|
http://arxiv.org/pdf/1711.00111v2.pdf
|
https://github.com/ltrottier/deep-collaboration-network
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/expletives-in-universal-dependency-treebanks
|
Expletives in Universal Dependency Treebanks
| null |
https://aclanthology.org/W18-6003
|
https://aclanthology.org/W18-6003.pdf
|
https://github.com/gossebouma/expletives
| true | true | false |
none
|
https://paperswithcode.com/paper/kernel-conditional-moment-test-via-maximum
|
Kernel Conditional Moment Test via Maximum Moment Restriction
|
2002.09225
|
https://arxiv.org/abs/2002.09225v3
|
https://arxiv.org/pdf/2002.09225v3.pdf
|
https://github.com/krikamol/kcm-test
| true | true | false |
none
|
https://paperswithcode.com/paper/dont-paint-it-black-white-box-explanations
|
Evaluating Explanation Methods for Deep Learning in Security
|
1906.02108
|
https://arxiv.org/abs/1906.02108v4
|
https://arxiv.org/pdf/1906.02108v4.pdf
|
https://github.com/alewarne/Layerwise-Relevance-Propagation-for-LSTMs
| false | true | false |
tf
|
https://paperswithcode.com/paper/a-long-short-term-memory-embedding-for-hybrid
|
A long short-term memory embedding for hybrid uplifted reduced order models
|
1912.06756
|
https://arxiv.org/abs/1912.06756v1
|
https://arxiv.org/pdf/1912.06756v1.pdf
|
https://github.com/Shady-Ahmed/UROM
| true | false | false |
tf
|
https://paperswithcode.com/paper/generalization-of-machine-learning-for
|
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
|
2005.05847
|
https://arxiv.org/abs/2005.05847v2
|
https://arxiv.org/pdf/2005.05847v2.pdf
|
https://github.com/yuansuny/tsp
| true | true | false |
none
|
https://paperswithcode.com/paper/a-unified-end-to-end-framework-for-efficient
|
A Unified End-to-End Framework for Efficient Deep Image Compression
|
2002.03370
|
https://arxiv.org/abs/2002.03370v3
|
https://arxiv.org/pdf/2002.03370v3.pdf
|
https://github.com/liujiaheng/CompressionData
| true | true | false |
none
|
https://paperswithcode.com/paper/neural-architecture-design-for-gpu-efficient
|
Neural Architecture Design for GPU-Efficient Networks
|
2006.14090
|
https://arxiv.org/abs/2006.14090v4
|
https://arxiv.org/pdf/2006.14090v4.pdf
|
https://github.com/idstcv/GPU-Efficient-Networks
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/distributed-learning-without-distress-privacy
|
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
| null |
http://papers.nips.cc/paper/7871-distributed-learning-without-distress-privacy-preserving-empirical-risk-minimization
|
http://papers.nips.cc/paper/7871-distributed-learning-without-distress-privacy-preserving-empirical-risk-minimization.pdf
|
https://github.com/bargavj/distributedMachineLearning
| true | true | false |
none
|
https://paperswithcode.com/paper/convergence-of-learning-dynamics-in-1
|
Convergence of Learning Dynamics in Stackelberg Games
|
1906.01217
|
https://arxiv.org/abs/1906.01217v3
|
https://arxiv.org/pdf/1906.01217v3.pdf
|
https://github.com/fiezt/Stackelberg-Code
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/nonparallel-emotional-speech-conversion
|
Nonparallel Emotional Speech Conversion
|
1811.01174
|
https://arxiv.org/abs/1811.01174v3
|
https://arxiv.org/pdf/1811.01174v3.pdf
|
https://github.com/bottlecapper/EmoMUNIT
| true | false | false |
tf
|
https://paperswithcode.com/paper/qsystem-bitwise-representation-for-quantum
|
QSystem: bitwise representation for quantum circuit simulations
|
2004.03560
|
https://arxiv.org/abs/2004.03560v1
|
https://arxiv.org/pdf/2004.03560v1.pdf
|
https://gitlab.com/evandro-crr/qsystem
| true | true | true |
none
|
https://paperswithcode.com/paper/bayesian-sparsification-of-deep-c-valued
|
Bayesian Sparsification of Deep C-valued Networks
| null |
https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf
|
https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf
|
https://github.com/ivannz/cplxmodule
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/cupc-cuda-based-parallel-pc-algorithm-for
|
cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU
|
1812.08491
|
https://arxiv.org/abs/1812.08491v4
|
https://arxiv.org/pdf/1812.08491v4.pdf
|
https://github.com/LIS-Laboratory/cupc
| true | true | false |
none
|
https://paperswithcode.com/paper/non-autoregressive-translation-with
|
Non-autoregressive Translation with Disentangled Context Transformer
| null |
https://proceedings.icml.cc/static/paper_files/icml/2020/477-Paper.pdf
|
https://proceedings.icml.cc/static/paper_files/icml/2020/477-Paper.pdf
|
https://github.com/facebookresearch/DisCo
| true | true | false |
none
|
https://paperswithcode.com/paper/automated-diagnosis-of-covid-19-with-limited
|
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
|
2004.11676
|
https://arxiv.org/abs/2004.11676v5
|
https://arxiv.org/pdf/2004.11676v5.pdf
|
https://github.com/nspunn1993/COVID-19-PA-CXR-fused-dataset
| true | true | false |
none
|
https://paperswithcode.com/paper/paraphrase-generation-with-latent-bag-of-1
|
Paraphrase Generation with Latent Bag of Words
|
2001.01941
|
https://arxiv.org/abs/2001.01941v1
|
https://arxiv.org/pdf/2001.01941v1.pdf
|
https://github.com/FranxYao/Deep-Generative-Models-for-Natural-Language-Processing
| true | true | false |
tf
|
https://paperswithcode.com/paper/concurrent-robin-hood-hashing
|
Concurrent Robin Hood Hashing
|
1809.04339
|
https://arxiv.org/abs/1809.04339v2
|
https://arxiv.org/pdf/1809.04339v2.pdf
|
https://github.com/DaKellyFella/concurrent-robin-hood-hashing
| true | true | false |
none
|
https://paperswithcode.com/paper/mask-r-cnn
|
Mask R-CNN
|
1703.06870
|
http://arxiv.org/abs/1703.06870v3
|
http://arxiv.org/pdf/1703.06870v3.pdf
|
https://github.com/TejasBajania/Mtech_pro
| false | false | true |
none
|
https://paperswithcode.com/paper/hindsight-experience-replay
|
Hindsight Experience Replay
|
1707.01495
|
http://arxiv.org/abs/1707.01495v3
|
http://arxiv.org/pdf/1707.01495v3.pdf
|
https://github.com/DLR-RM/stable-baselines3
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/virtual-secure-platform-a-five-stage-pipeline
|
Virtual Secure Platform: A Five-Stage Pipeline Processor over TFHE
|
2010.09410
|
https://arxiv.org/abs/2010.09410v1
|
https://arxiv.org/pdf/2010.09410v1.pdf
|
https://github.com/virtualsecureplatform/kvsp
| true | true | true |
none
|
https://paperswithcode.com/paper/a-fixed-point-approach-to-barycenters-in
|
A fixed-point approach to barycenters in Wasserstein space
|
1511.05355
|
https://arxiv.org/abs/1511.05355v3
|
https://arxiv.org/pdf/1511.05355v3.pdf
|
https://github.com/lingxiaoli94/CWB
| false | false | true |
tf
|
https://paperswithcode.com/paper/nscaching-simple-and-efficient-negative
|
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
|
1812.06410
|
http://arxiv.org/abs/1812.06410v2
|
http://arxiv.org/pdf/1812.06410v2.pdf
|
https://github.com/AutoML-4Paradigm/NSCaching
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/centernet-object-detection-with-keypoint
|
CenterNet: Keypoint Triplets for Object Detection
|
1904.08189
|
http://arxiv.org/abs/1904.08189v3
|
http://arxiv.org/pdf/1904.08189v3.pdf
|
https://github.com/ximilar-com/xcenternet
| false | false | false |
tf
|
https://paperswithcode.com/paper/joint-parsing-and-generation-for-abstractive
|
Joint Parsing and Generation for Abstractive Summarization
|
1911.10389
|
https://arxiv.org/abs/1911.10389v1
|
https://arxiv.org/pdf/1911.10389v1.pdf
|
https://github.com/jiaruncao/BioCopyMechanism
| false | false | true |
none
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.