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https://paperswithcode.com/paper/3d-surface-reconstruction-from-multi-date
|
3D Surface Reconstruction From Multi-Date Satellite Images
|
2102.02502
|
https://arxiv.org/abs/2102.02502v2
|
https://arxiv.org/pdf/2102.02502v2.pdf
|
https://github.com/SBCV/SatelliteSurfaceReconstruction
| true | true | true |
none
|
https://paperswithcode.com/paper/flexible-behavior-trees-in-search-of-the
|
Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics
|
2203.05389
|
https://arxiv.org/abs/2203.05389v1
|
https://arxiv.org/pdf/2203.05389v1.pdf
|
https://github.com/flexbe/flex_bt_turtlebot_demo
| true | true | false |
none
|
https://paperswithcode.com/paper/foreseeing-brain-graph-evolution-over-time
|
Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer
|
2009.11166
|
https://arxiv.org/abs/2009.11166v1
|
https://arxiv.org/pdf/2009.11166v1.pdf
|
https://github.com/basiralab/gGAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/define-delayed-feedback-based-immersive
|
DeFINE: Delayed Feedback based Immersive Navigation Environment for Studying Goal-Directed Human Navigation
|
2003.03133
|
https://arxiv.org/abs/2003.03133v2
|
https://arxiv.org/pdf/2003.03133v2.pdf
|
https://github.com/ktiwari9/define-VR
| true | false | false |
none
|
https://paperswithcode.com/paper/efficientnetv2-smaller-models-and-faster
|
EfficientNetV2: Smaller Models and Faster Training
|
2104.00298
|
https://arxiv.org/abs/2104.00298v3
|
https://arxiv.org/pdf/2104.00298v3.pdf
|
https://github.com/lukemelas/EfficientNet-PyTorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/ntire-2020-challenge-on-real-world-image
|
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
|
2005.01996
|
https://arxiv.org/abs/2005.01996v1
|
https://arxiv.org/pdf/2005.01996v1.pdf
|
https://github.com/ArchieMeng/realsr-ncnn-vulkan-python
| false | false | true |
none
|
https://paperswithcode.com/paper/scilla-a-smart-contract-intermediate-level
|
Scilla: a Smart Contract Intermediate-Level LAnguage
|
1801.00687
|
https://arxiv.org/abs/1801.00687v1
|
https://arxiv.org/pdf/1801.00687v1.pdf
|
https://github.com/pirapira/ethereum-formal-verification-overview
| true | true | true |
none
|
https://paperswithcode.com/paper/model-free-price-bounds-under-dynamic-option
|
Model-free price bounds under dynamic option trading
|
2101.01024
|
https://arxiv.org/abs/2101.01024v2
|
https://arxiv.org/pdf/2101.01024v2.pdf
|
https://github.com/juliansester/dynamic_option_trading
| true | true | true |
none
|
https://paperswithcode.com/paper/tensor-train-density-estimation
|
Tensor-Train Density Estimation
|
2108.00089
|
https://arxiv.org/abs/2108.00089v2
|
https://arxiv.org/pdf/2108.00089v2.pdf
|
https://github.com/stat-ml/TTDE
| true | true | true |
jax
|
https://paperswithcode.com/paper/sparse-svm-for-sufficient-data-reduction
|
Sparse SVM for Sufficient Data Reduction
|
2005.13771
|
https://arxiv.org/abs/2005.13771v4
|
https://arxiv.org/pdf/2005.13771v4.pdf
|
https://github.com/ShenglongZhou/NSSVM
| true | false | true |
none
|
https://paperswithcode.com/paper/group-fisher-pruning-for-practical-network
|
Group Fisher Pruning for Practical Network Compression
|
2108.00708
|
https://arxiv.org/abs/2108.00708v1
|
https://arxiv.org/pdf/2108.00708v1.pdf
|
https://github.com/jshilong/FisherPruning
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-general-framework-for-ensemble-distribution
|
A general framework for ensemble distribution distillation
|
2002.11531
|
https://arxiv.org/abs/2002.11531v2
|
https://arxiv.org/pdf/2002.11531v2.pdf
|
https://github.com/jackonelli/ensemble_distr_distillation
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/a-sketching-framework-for-reduced-data
|
A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar
|
2102.08732
|
https://arxiv.org/abs/2102.08732v4
|
https://arxiv.org/pdf/2102.08732v4.pdf
|
https://gitlab.com/Tachella/sketched_lidar
| true | true | true |
none
|
https://paperswithcode.com/paper/the-gaussian-neural-process
|
The Gaussian Neural Process
|
2101.03606
|
https://arxiv.org/abs/2101.03606v1
|
https://arxiv.org/pdf/2101.03606v1.pdf
|
https://github.com/wesselb/NeuralProcesses.jl
| true | true | false |
none
|
https://paperswithcode.com/paper/multiple-attribute-text-style-transfer
|
Multiple-Attribute Text Style Transfer
|
1811.00552
|
https://arxiv.org/abs/1811.00552v2
|
https://arxiv.org/pdf/1811.00552v2.pdf
|
https://github.com/facebookresearch/MultipleAttributeTextRewriting
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/community-detection-in-sparse-time-evolving
|
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
|
2006.04510
|
https://arxiv.org/abs/2006.04510v2
|
https://arxiv.org/pdf/2006.04510v2.pdf
|
https://github.com/lorenzodallamico/CoDeBetHe.jl
| true | false | false |
none
|
https://paperswithcode.com/paper/logic-consistency-text-generation-from
|
Logic-Consistency Text Generation from Semantic Parses
|
2108.00577
|
https://arxiv.org/abs/2108.00577v1
|
https://arxiv.org/pdf/2108.00577v1.pdf
|
https://github.com/Ciaranshu/relogic
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-unified-framework-for-spectral-clustering
|
A unified framework for spectral clustering in sparse graphs
|
2003.09198
|
https://arxiv.org/abs/2003.09198v2
|
https://arxiv.org/pdf/2003.09198v2.pdf
|
https://github.com/lorenzodallamico/CoDeBetHe.jl
| true | true | false |
none
|
https://paperswithcode.com/paper/3d-human-mesh-regression-with-dense-1
|
3D Human Mesh Regression with Dense Correspondence
|
2006.05734
|
https://arxiv.org/abs/2006.05734v2
|
https://arxiv.org/pdf/2006.05734v2.pdf
|
https://github.com/jiean001/models_m/tree/main/DecoMR
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/what-is-the-best-data-augmentation-approach
|
What is the best data augmentation for 3D brain tumor segmentation?
|
2010.13372
|
https://arxiv.org/abs/2010.13372v2
|
https://arxiv.org/pdf/2010.13372v2.pdf
|
https://github.com/mdciri/3D-augmentation-techniques
| true | true | true |
tf
|
https://paperswithcode.com/paper/distributed-multi-object-tracking-under
|
Distributed Multi-object Tracking under Limited Field of View Sensors
|
2012.12990
|
https://arxiv.org/abs/2012.12990v2
|
https://arxiv.org/pdf/2012.12990v2.pdf
|
https://github.com/AdelaideAuto-IDLab/Distributed-limitedFoV-MOT
| true | true | true |
none
|
https://paperswithcode.com/paper/automating-involutive-mcmc-using
|
Automating Involutive MCMC using Probabilistic and Differentiable Programming
|
2007.09871
|
https://arxiv.org/abs/2007.09871v2
|
https://arxiv.org/pdf/2007.09871v2.pdf
|
https://github.com/probcomp/GenTraceKernelDSL.jl
| false | false | true |
none
|
https://paperswithcode.com/paper/optimization-free-test-time-adaptation-for
|
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
|
2310.18562
|
https://arxiv.org/abs/2310.18562v2
|
https://arxiv.org/pdf/2310.18562v2.pdf
|
https://github.com/Claydon-Wang/OFTTA
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/generalized-universe-hierarchies-and-first
|
Generalized Universe Hierarchies and First-Class Universe Levels
|
2103.00223
|
https://arxiv.org/abs/2103.00223v4
|
https://arxiv.org/pdf/2103.00223v4.pdf
|
https://github.com/AndrasKovacs/universes
| true | true | true |
none
|
https://paperswithcode.com/paper/help-me-identify-is-an-llm-vqa-system-all-we
|
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
|
2410.13651
|
https://arxiv.org/abs/2410.13651v1
|
https://arxiv.org/pdf/2410.13651v1.pdf
|
https://github.com/shailaja183/objectconceptlearning
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/actioncomet-a-zero-shot-approach-to-learn
|
ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions
|
2410.13662
|
https://arxiv.org/abs/2410.13662v1
|
https://arxiv.org/pdf/2410.13662v1.pdf
|
https://github.com/shailaja183/actionconceptlearning
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/leaf-simulating-large-energy-aware-fog
|
LEAF: Simulating Large Energy-Aware Fog Computing Environments
|
2103.01170
|
https://arxiv.org/abs/2103.01170v1
|
https://arxiv.org/pdf/2103.01170v1.pdf
|
https://github.com/dos-group/leaf
| true | true | true |
none
|
https://paperswithcode.com/paper/towards-a-quality-metric-for-dense-light
|
Towards a quality metric for dense light fields
|
1704.07576
|
http://arxiv.org/abs/1704.07576v1
|
http://arxiv.org/pdf/1704.07576v1.pdf
|
https://github.com/mantiuk/pwcmp
| true | true | false |
none
|
https://paperswithcode.com/paper/resa-recurrent-feature-shift-aggregator-for
|
RESA: Recurrent Feature-Shift Aggregator for Lane Detection
|
2008.13719
|
https://arxiv.org/abs/2008.13719v2
|
https://arxiv.org/pdf/2008.13719v2.pdf
|
https://github.com/ZJULearning/resa
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/exploiting-emotions-for-fake-news-detection
|
Mining Dual Emotion for Fake News Detection
|
1903.01728
|
https://arxiv.org/abs/1903.01728v4
|
https://arxiv.org/pdf/1903.01728v4.pdf
|
https://github.com/RMSnow/WWW2021
| true | false | true |
tf
|
https://paperswithcode.com/paper/warm-up-cold-start-advertisements-improving
|
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
|
1904.11547
|
http://arxiv.org/abs/1904.11547v1
|
http://arxiv.org/pdf/1904.11547v1.pdf
|
https://github.com/Feiyang/MetaEmbedding
| true | false | false |
tf
|
https://paperswithcode.com/paper/sequential-place-learning-heuristic-free-high
|
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
|
2103.02074
|
https://arxiv.org/abs/2103.02074v1
|
https://arxiv.org/pdf/2103.02074v1.pdf
|
https://github.com/mchancan/deepseqslam
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/fastcat-fast-cone-beam-ct-cbct-simulation
|
FastCAT: Fast Cone Beam CT (CBCT) Simulation
|
2011.04736
|
https://arxiv.org/abs/2011.04736v2
|
https://arxiv.org/pdf/2011.04736v2.pdf
|
https://github.com/jerichooconnell/fastCAT
| true | true | false |
none
|
https://paperswithcode.com/paper/ensemble-based-learning-of-turbulence-model
|
Ensemble Kalman method for learning turbulence models from indirect observation data
|
2202.05122
|
https://arxiv.org/abs/2202.05122v4
|
https://arxiv.org/pdf/2202.05122v4.pdf
|
https://github.com/xiaoh/DAFI
| true | true | false |
none
|
https://paperswithcode.com/paper/understanding-the-role-of-momentum-in-non
|
Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization
|
2010.00406
|
https://arxiv.org/abs/2010.00406v4
|
https://arxiv.org/pdf/2010.00406v4.pdf
|
https://github.com/facebookresearch/madgrad
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/preference-based-learning-for-user-guided-hzd
|
Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
|
2011.05424
|
https://arxiv.org/abs/2011.05424v2
|
https://arxiv.org/pdf/2011.05424v2.pdf
|
https://github.com/maegant/ICRA2021-LearningHZD
| true | true | false |
none
|
https://paperswithcode.com/paper/adaptivity-without-compromise-a-momentumized
|
Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization
|
2101.11075
|
https://arxiv.org/abs/2101.11075v3
|
https://arxiv.org/pdf/2101.11075v3.pdf
|
https://github.com/facebookresearch/madgrad
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/room-classification-on-floor-plan-graphs
|
Room Classification on Floor Plan Graphs using Graph Neural Networks
|
2108.05947
|
https://arxiv.org/abs/2108.05947v1
|
https://arxiv.org/pdf/2108.05947v1.pdf
|
https://github.com/abpaudel/floorplan-graph
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/a-deep-perceptual-metric-for-3d-point-clouds
|
A deep perceptual metric for 3D point clouds
|
2102.12839
|
https://arxiv.org/abs/2102.12839v1
|
https://arxiv.org/pdf/2102.12839v1.pdf
|
https://github.com/mauriceqch/2021_pc_perceptual_loss
| true | true | true |
tf
|
https://paperswithcode.com/paper/hamiltonian-simulation-with-random-inputs
|
Hamiltonian simulation with random inputs
|
2111.04773
|
https://arxiv.org/abs/2111.04773v1
|
https://arxiv.org/pdf/2111.04773v1.pdf
|
https://github.com/zhaoqthu/hamiltonian-simulation-with-random-inputs
| true | true | false |
none
|
https://paperswithcode.com/paper/layer-2-atomic-cross-blockchain-function
|
Layer 2 Atomic Cross-Blockchain Function Calls
|
2005.09790
|
https://arxiv.org/abs/2005.09790v5
|
https://arxiv.org/pdf/2005.09790v5.pdf
|
https://github.com/ConsenSys/gpact
| false | false | true |
none
|
https://paperswithcode.com/paper/quantifying-covid-19-enforced-global-changes
|
Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing
|
2101.03523
|
https://arxiv.org/abs/2101.03523v3
|
https://arxiv.org/pdf/2101.03523v3.pdf
|
https://github.com/manmeet3591/gee_lockdown
| true | true | false |
none
|
https://paperswithcode.com/paper/visual-field-prediction-using-recurrent
|
Visual Field Prediction using Recurrent Neural Network
| null |
https://www.nature.com/articles/s41598-019-44852-6
|
https://www.nature.com/articles/s41598-019-44852-6.pdf
|
https://github.com/mohaEs/VFPrediction
| false | false | false |
tf
|
https://paperswithcode.com/paper/a-crash-course-on-reinforcement-learning
|
A Crash Course on Reinforcement Learning
|
2103.04910
|
https://arxiv.org/abs/2103.04910v1
|
https://arxiv.org/pdf/2103.04910v1.pdf
|
https://github.com/FarnazAdib/Crash_course_on_RL
| true | true | true |
tf
|
https://paperswithcode.com/paper/end-to-end-human-object-interaction-detection
|
End-to-End Human Object Interaction Detection with HOI Transformer
|
2103.04503
|
https://arxiv.org/abs/2103.04503v1
|
https://arxiv.org/pdf/2103.04503v1.pdf
|
https://github.com/bbepoch/HoiTransformer
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/towards-high-fidelity-face-relighting-with
|
Towards High Fidelity Face Relighting with Realistic Shadows
|
2104.00825
|
https://arxiv.org/abs/2104.00825v2
|
https://arxiv.org/pdf/2104.00825v2.pdf
|
https://github.com/andrewhou1/Shadow-Mask-Face-Relighting
| true | true | false |
tf
|
https://paperswithcode.com/paper/sdan-squared-deformable-alignment-network-for
|
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
|
2104.00848
|
https://arxiv.org/abs/2104.00848v2
|
https://arxiv.org/pdf/2104.00848v2.pdf
|
https://github.com/MKFMIKU/SDAN
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/lightningdot-pre-training-visual-semantic
|
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
|
2103.08784
|
https://arxiv.org/abs/2103.08784v2
|
https://arxiv.org/pdf/2103.08784v2.pdf
|
https://github.com/intersun/LightningDOT
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/bottom-up-and-top-down-reasoning-with
|
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
|
1507.05699
|
http://arxiv.org/abs/1507.05699v5
|
http://arxiv.org/pdf/1507.05699v5.pdf
|
https://github.com/peiyunh/rg-mpii
| true | true | false |
none
|
https://paperswithcode.com/paper/this-item-is-a-glaxefw-and-this-is-a-glaxuzb
|
Compositionality Through Language Transmission, using Artificial Neural Networks
|
2101.11739
|
https://arxiv.org/abs/2101.11739v2
|
https://arxiv.org/pdf/2101.11739v2.pdf
|
https://github.com/asappresearch/neural-ilm
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-signal-centric-perspective-on-the-evolution
|
A Signal-Centric Perspective on the Evolution of Symbolic Communication
|
2103.16882
|
https://arxiv.org/abs/2103.16882v1
|
https://arxiv.org/pdf/2103.16882v1.pdf
|
https://github.com/FraLotito/evol-signal-comm
| true | true | false |
none
|
https://paperswithcode.com/paper/trees-forests-chickens-and-eggs-when-and-why
|
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
|
2103.16700
|
https://arxiv.org/abs/2103.16700v1
|
https://arxiv.org/pdf/2103.16700v1.pdf
|
https://github.com/syzhou5/TreeDepth
| true | true | false |
none
|
https://paperswithcode.com/paper/mean-shift-feature-transformer
|
Mean-Shift Feature Transformer
| null |
http://openaccess.thecvf.com//content/CVPR2024/html/Kobayashi_Mean-Shift_Feature_Transformer_CVPR_2024_paper.html
|
http://openaccess.thecvf.com//content/CVPR2024/papers/Kobayashi_Mean-Shift_Feature_Transformer_CVPR_2024_paper.pdf
|
https://github.com/tk1980/msftransformer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/mediapipe-hands-on-device-real-time-hand
|
MediaPipe Hands: On-device Real-time Hand Tracking
|
2006.10214
|
https://arxiv.org/abs/2006.10214v1
|
https://arxiv.org/pdf/2006.10214v1.pdf
|
https://github.com/vidursatija/BlazePalm
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/team-phoenix-at-wassa-2021-emotion-analysis
|
Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models
|
2103.06057
|
https://arxiv.org/abs/2103.06057v1
|
https://arxiv.org/pdf/2103.06057v1.pdf
|
https://github.com/yashbutala/WASSA
| true | true | false |
none
|
https://paperswithcode.com/paper/continuous-weight-balancing
|
Continuous Weight Balancing
|
2103.16591
|
https://arxiv.org/abs/2103.16591v1
|
https://arxiv.org/pdf/2103.16591v1.pdf
|
https://github.com/Daniel-Wu/Continuous-Weight-Balancing
| true | true | false |
none
|
https://paperswithcode.com/paper/tanksworld-a-multi-agent-environment-for-ai
|
TanksWorld: A Multi-Agent Environment for AI Safety Research
|
2002.11174
|
https://arxiv.org/abs/2002.11174v1
|
https://arxiv.org/pdf/2002.11174v1.pdf
|
https://github.com/cgrivera/ai-safety-challenge
| false | false | true |
tf
|
https://paperswithcode.com/paper/multi-scale-gcn-assisted-two-stage-network
|
Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images
|
2102.04799
|
https://arxiv.org/abs/2102.04799v1
|
https://arxiv.org/pdf/2102.04799v1.pdf
|
https://github.com/Jiaxuan-Li/MGU-Net
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/tlsan-time-aware-long-and-short-term-1
|
TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation
|
2103.08971
|
https://arxiv.org/abs/2103.08971v1
|
https://arxiv.org/pdf/2103.08971v1.pdf
|
https://github.com/TsingZ0/TLSAN
| true | true | false |
tf
|
https://paperswithcode.com/paper/determining-the-maximum-information-gain-and
|
Determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher information
|
2103.08973
|
https://arxiv.org/abs/2103.08973v3
|
https://arxiv.org/pdf/2103.08973v3.pdf
|
https://github.com/James-Durant/fisher-information
| true | true | true |
none
|
https://paperswithcode.com/paper/nonlinear-causal-discovery-via-kernel-anchor
|
Nonlinear Causal Discovery via Kernel Anchor Regression
|
2210.16775
|
https://arxiv.org/abs/2210.16775v1
|
https://arxiv.org/pdf/2210.16775v1.pdf
|
https://github.com/swq118/kernel-anchor-regression
| true | true | false |
none
|
https://paperswithcode.com/paper/cardiologist-level-arrhythmia-detection-with
|
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
|
1707.01836
|
http://arxiv.org/abs/1707.01836v1
|
http://arxiv.org/pdf/1707.01836v1.pdf
|
https://github.com/physhik/ecg-mit-bih
| false | false | true |
tf
|
https://paperswithcode.com/paper/predicting-pedestrian-crossing-intention-with
|
Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention
|
2104.05485
|
https://arxiv.org/abs/2104.05485v2
|
https://arxiv.org/pdf/2104.05485v2.pdf
|
https://github.com/ZeWang95/PedesPred
| false | false | true |
tf
|
https://paperswithcode.com/paper/real-time-safety-assessment-of-dynamic
|
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques
|
2304.12583
|
https://arxiv.org/abs/2304.12583v2
|
https://arxiv.org/pdf/2304.12583v2.pdf
|
https://github.com/thufdd/jiaolongdsms_datasets
| true | true | false |
none
|
https://paperswithcode.com/paper/ghum-ghuml-generative-3d-human-shape-and
|
GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models
| null |
http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_GHUM__GHUML_Generative_3D_Human_Shape_and_Articulated_Pose_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_GHUM__GHUML_Generative_3D_Human_Shape_and_Articulated_Pose_CVPR_2020_paper.pdf
|
https://github.com/google-research/google-research/tree/master/ghum
| true | false | false |
tf
|
https://paperswithcode.com/paper/implicit-normalizing-flows-1
|
Implicit Normalizing Flows
|
2103.09527
|
https://arxiv.org/abs/2103.09527v1
|
https://arxiv.org/pdf/2103.09527v1.pdf
|
https://github.com/thu-ml/implicit-normalizing-flows
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/hinglishnlp-at-semeval-2020-task-9-fine-tuned
|
HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection
| null |
https://aclanthology.org/2020.semeval-1.119
|
https://aclanthology.org/2020.semeval-1.119.pdf
|
https://github.com/NirantK/Hinglish
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/gated-multimodal-units-for-information-fusion
|
Gated Multimodal Units for Information Fusion
|
1702.01992
|
http://arxiv.org/abs/1702.01992v1
|
http://arxiv.org/pdf/1702.01992v1.pdf
|
https://github.com/IsaacRodgz/multimodal-transformers-movies
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/non-convex-optimization-for-self-calibration
|
Non-convex optimization for self-calibration of direction-dependent effects in radio interferometric imaging
|
1701.03689
|
http://arxiv.org/abs/1701.03689v2
|
http://arxiv.org/pdf/1701.03689v2.pdf
|
https://github.com/basp-group/SARA-CALIB-realdata
| false | false | true |
none
|
https://paperswithcode.com/paper/robust-mpc-for-linear-systems-with-parametric
|
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
|
2007.00930
|
https://arxiv.org/abs/2007.00930v6
|
https://arxiv.org/pdf/2007.00930v6.pdf
|
https://github.com/monimoyb/RMPC_MixedUncertainty
| true | true | true |
none
|
https://paperswithcode.com/paper/safely-learning-to-control-the-constrained
|
Safely Learning to Control the Constrained Linear Quadratic Regulator
|
1809.10121
|
https://arxiv.org/abs/1809.10121v2
|
https://arxiv.org/pdf/1809.10121v2.pdf
|
https://github.com/monimoyb/RMPC_MixedUncertainty
| false | false | true |
none
|
https://paperswithcode.com/paper/discovering-influential-factors-in
|
Discovering Influential Factors in Variational Autoencoder
|
1809.01804
|
http://arxiv.org/abs/1809.01804v2
|
http://arxiv.org/pdf/1809.01804v2.pdf
|
https://github.com/647LiuSQ/Discovering-influential-factors-in-variational-autoencoders
| true | false | false |
tf
|
https://paperswithcode.com/paper/affordance-transfer-learning-for-human-object
|
Affordance Transfer Learning for Human-Object Interaction Detection
|
2104.02867
|
https://arxiv.org/abs/2104.02867v2
|
https://arxiv.org/pdf/2104.02867v2.pdf
|
https://github.com/zhihou7/HOI-CL-OneStage
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/visual-compositional-learning-for-human
|
Visual Compositional Learning for Human-Object Interaction Detection
|
2007.12407
|
https://arxiv.org/abs/2007.12407v2
|
https://arxiv.org/pdf/2007.12407v2.pdf
|
https://github.com/zhihou7/HOI-CL-OneStage
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/evars-gpr-event-triggered-augmented-refitting
|
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
|
2107.02463
|
https://arxiv.org/abs/2107.02463v1
|
https://arxiv.org/pdf/2107.02463v1.pdf
|
https://github.com/grimmlab/evars-gpr
| true | true | false |
none
|
https://paperswithcode.com/paper/relational-gating-for-what-if-reasoning
|
Relational Gating for "What If" Reasoning
|
2105.13449
|
https://arxiv.org/abs/2105.13449v1
|
https://arxiv.org/pdf/2105.13449v1.pdf
|
https://github.com/HLR/RGN
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/data-centric-semi-supervised-learning
|
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
|
2110.03006
|
https://arxiv.org/abs/2110.03006v4
|
https://arxiv.org/pdf/2110.03006v4.pdf
|
https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/linguistic-structures-as-weak-supervision-for
|
Linguistic Structures as Weak Supervision for Visual Scene Graph Generation
|
2105.13994
|
https://arxiv.org/abs/2105.13994v1
|
https://arxiv.org/pdf/2105.13994v1.pdf
|
https://github.com/yekeren/WSSGG
| true | true | false |
tf
|
https://paperswithcode.com/paper/comprehensive-study-how-the-context
|
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?
|
2105.03571
|
https://arxiv.org/abs/2105.03571v2
|
https://arxiv.org/pdf/2105.03571v2.pdf
|
https://github.com/yangpuhai/Granularity-in-DST
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/computing-periodic-points-on-veech-surfaces
|
Computing Periodic Points on Veech Surfaces
|
2112.02698
|
https://arxiv.org/abs/2112.02698v2
|
https://arxiv.org/pdf/2112.02698v2.pdf
|
https://github.com/sfreedman67/bowman
| true | true | false |
none
|
https://paperswithcode.com/paper/overt-an-algorithm-for-safety-verification-of
|
OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems
|
2108.01220
|
https://arxiv.org/abs/2108.01220v1
|
https://arxiv.org/pdf/2108.01220v1.pdf
|
https://github.com/sisl/OVERTVerify.jl
| true | true | true |
none
|
https://paperswithcode.com/paper/boosting-weakly-supervised-object-detection-1
|
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
|
2108.01499
|
https://arxiv.org/abs/2108.01499v1
|
https://arxiv.org/pdf/2108.01499v1.pdf
|
https://github.com/DongSky/lbba_boosted_wsod
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/enhancement-of-a-state-of-the-art-rl-based
|
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars
|
2112.02628
|
https://arxiv.org/abs/2112.02628v2
|
https://arxiv.org/pdf/2112.02628v2.pdf
|
https://github.com/lisifra96/improved_rl_algorithm_mmimo_radar
| true | true | false |
none
|
https://paperswithcode.com/paper/dialogue-summarization-with-supporting
|
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
|
2108.01268
|
https://arxiv.org/abs/2108.01268v1
|
https://arxiv.org/pdf/2108.01268v1.pdf
|
https://github.com/Chen-Wang-CUHK/DialSum-with-SUFM-and-FR
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/breast-cancer-image-classification-on-wsi
|
Breast cancer image classification on WSI with spatial correlations
| null |
https://www.researchgate.net/publication/332790422_Breast_Cancer_Image_Classification_on_WSI_with_Spatial_Correlations?_sg=y0w4GzUN4IH2Q8xVra4yYZwWptcdTEsbyVxAuDTNmT5f5gvlcbpGKI5Ccj-DAgjHtPHJ6NWxpVbPwiUkosEDaaAsKRu_sBOrPQytuF_O.m_8mNN692O90t0LPpasc9b9qVfIP8Wz6jkFgn9Ld5bRPPoMuIK6lEQ93j9hlKrwJRk1folGO0m1Ix7961QKt7g
|
https://www.researchgate.net/publication/332790422_Breast_Cancer_Image_Classification_on_WSI_with_Spatial_Correlations?_sg=y0w4GzUN4IH2Q8xVra4yYZwWptcdTEsbyVxAuDTNmT5f5gvlcbpGKI5Ccj-DAgjHtPHJ6NWxpVbPwiUkosEDaaAsKRu_sBOrPQytuF_O.m_8mNN692O90t0LPpasc9b9qVfIP8Wz6jkFgn9Ld5bRPPoMuIK6lEQ93j9hlKrwJRk1folGO0m1Ix7961QKt7g
|
https://github.com/dong100136/Breast-Cancer-Image-Classification-On-WSI-With-Spatial-Correlations
| false | false | false |
tf
|
https://paperswithcode.com/paper/meta-pu-an-arbitrary-scale-upsampling-network
|
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
|
2102.04317
|
https://arxiv.org/abs/2102.04317v1
|
https://arxiv.org/pdf/2102.04317v1.pdf
|
https://github.com/pleaseconnectwifi/Meta-PU
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/adapting-membership-inference-attacks-to-gnn
|
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
|
2110.08760
|
https://arxiv.org/abs/2110.08760v1
|
https://arxiv.org/pdf/2110.08760v1.pdf
|
https://github.com/trustworthygnn/mia-gnn
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-character-level-decoder-without-explicit
|
A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation
|
1603.06147
|
http://arxiv.org/abs/1603.06147v4
|
http://arxiv.org/pdf/1603.06147v4.pdf
|
https://github.com/nyu-dl/dl4mt-cdec
| false | false | true |
none
|
https://paperswithcode.com/paper/voicefixer-a-unified-framework-for-high
|
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
|
2204.05841
|
https://arxiv.org/abs/2204.05841v2
|
https://arxiv.org/pdf/2204.05841v2.pdf
|
https://github.com/haoheliu/voicefixer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/learning-generalized-spoof-cues-for-face-anti
|
Learning Generalized Spoof Cues for Face Anti-spoofing
|
2005.03922
|
https://arxiv.org/abs/2005.03922v1
|
https://arxiv.org/pdf/2005.03922v1.pdf
|
https://github.com/mortezagolzan/Face-Anti-Spoofing
| false | false | true |
paddle
|
https://paperswithcode.com/paper/detecting-fast-radio-bursts-in-the-milky-way
|
Detecting Fast Radio Bursts in the Milky Way
|
2112.02233
|
https://arxiv.org/abs/2112.02233v1
|
https://arxiv.org/pdf/2112.02233v1.pdf
|
https://github.com/cmlflynn/milkyway-frbs
| true | true | false |
none
|
https://paperswithcode.com/paper/imagenet-21k-pretraining-for-the-masses
|
ImageNet-21K Pretraining for the Masses
|
2104.10972
|
https://arxiv.org/abs/2104.10972v4
|
https://arxiv.org/pdf/2104.10972v4.pdf
|
https://github.com/Alibaba-MIIL/ImageNet21K
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/lifting-monocular-events-to-3d-human-poses
|
Lifting Monocular Events to 3D Human Poses
|
2104.10609
|
https://arxiv.org/abs/2104.10609v1
|
https://arxiv.org/pdf/2104.10609v1.pdf
|
https://github.com/IIT-PAVIS/lifting_events_to_3d_hpe
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/on-convergence-rates-of-adaptive-ensemble
|
On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems
|
2104.10895
|
https://arxiv.org/abs/2104.10895v5
|
https://arxiv.org/pdf/2104.10895v5.pdf
|
https://github.com/FabianKP/adaptive_eki
| true | true | false |
none
|
https://paperswithcode.com/paper/gender-lost-in-translation-how-bridging-the
|
Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation
|
2305.16935
|
https://arxiv.org/abs/2305.16935v1
|
https://arxiv.org/pdf/2305.16935v1.pdf
|
https://github.com/lenacabrera/gb_mnmt
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/novel-models-for-multiple-dependent
|
Novel Models for Multiple Dependent Heteroskedastic Time Series
|
2310.17760
|
https://arxiv.org/abs/2310.17760v1
|
https://arxiv.org/pdf/2310.17760v1.pdf
|
https://github.com/13204942/stat40710
| true | true | false |
none
|
https://paperswithcode.com/paper/kids-1000-methodology-modelling-and-inference
|
KiDS-1000 Methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis
|
2007.01844
|
https://arxiv.org/abs/2007.01844v2
|
https://arxiv.org/pdf/2007.01844v2.pdf
|
https://github.com/kids-wl/cat_to_obs_k1000_p1
| false | false | true |
none
|
https://paperswithcode.com/paper/kids-1000-cosmology-multi-probe-weak
|
KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints
|
2007.15632
|
https://arxiv.org/abs/2007.15632v2
|
https://arxiv.org/pdf/2007.15632v2.pdf
|
https://github.com/kids-wl/cat_to_obs_k1000_p1
| false | false | true |
none
|
https://paperswithcode.com/paper/diverse-beam-search-decoding-diverse
|
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
|
1610.02424
|
http://arxiv.org/abs/1610.02424v2
|
http://arxiv.org/pdf/1610.02424v2.pdf
|
https://github.com/StatNLP/ada4asr
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-convnet-for-the-2020s
|
A ConvNet for the 2020s
|
2201.03545
|
https://arxiv.org/abs/2201.03545v2
|
https://arxiv.org/pdf/2201.03545v2.pdf
|
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt
| false | false | false |
paddle
|
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