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https://paperswithcode.com/paper/learning-rich-features-at-high-speed-for
|
Learning Rich Features at High-Speed for Single-Shot Object Detection
| null |
http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Learning_Rich_Features_at_High-Speed_for_Single-Shot_Object_Detection_ICCV_2019_paper.html
|
http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Rich_Features_at_High-Speed_for_Single-Shot_Object_Detection_ICCV_2019_paper.pdf
|
https://github.com/vaesl/LRF-Net
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/f-gan-training-generative-neural-samplers
|
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
|
1606.00709
|
http://arxiv.org/abs/1606.00709v1
|
http://arxiv.org/pdf/1606.00709v1.pdf
|
https://github.com/mboudiaf/Mutual-Information-Variational-Bounds
| false | false | true |
tf
|
https://paperswithcode.com/paper/nltk-the-natural-language-toolkit
|
NLTK: The Natural Language Toolkit
|
cs/0205028
|
https://arxiv.org/abs/cs/0205028v1
|
https://arxiv.org/pdf/cs/0205028v1.pdf
|
https://github.com/napakalas/NLIMED
| false | false | true |
tf
|
https://paperswithcode.com/paper/an-architecture-combining-convolutional
|
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification
|
1712.03541
|
http://arxiv.org/abs/1712.03541v2
|
http://arxiv.org/pdf/1712.03541v2.pdf
|
https://github.com/da-moon/classifiers-monorepo
| false | false | true |
tf
|
https://paperswithcode.com/paper/deep-forest
|
Deep Forest
|
1702.08835
|
https://arxiv.org/abs/1702.08835v4
|
https://arxiv.org/pdf/1702.08835v4.pdf
|
https://github.com/da-moon/classifiers-monorepo
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-neural-network-architecture-combining-gated
|
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
|
1709.03082
|
http://arxiv.org/abs/1709.03082v8
|
http://arxiv.org/pdf/1709.03082v8.pdf
|
https://github.com/da-moon/classifiers-monorepo
| false | false | true |
tf
|
https://paperswithcode.com/paper/high-throughput-open-source-implementation-of
|
High Throughput Open-Source Implementation of Wi-Fi 6 and WiMAX LDPC Encoder and Decoder
|
2306.12063
|
https://arxiv.org/abs/2306.12063v1
|
https://arxiv.org/pdf/2306.12063v1.pdf
|
https://github.com/talenik/yaldpc
| true | true | false |
none
|
https://paperswithcode.com/paper/achieving-open-vocabulary-neural-machine
|
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
|
1604.00788
|
http://arxiv.org/abs/1604.00788v2
|
http://arxiv.org/pdf/1604.00788v2.pdf
|
https://github.com/yurayli/stanford-cs224n-sol
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/qubo-formulations-for-system-of-linear
|
QUBO formulations for numerical quantum computing
|
2106.10819
|
https://arxiv.org/abs/2106.10819v4
|
https://arxiv.org/pdf/2106.10819v4.pdf
|
https://github.com/ktfriends/QUBO/blob/main/Formulations.ipynb
| true | false | false |
none
|
https://paperswithcode.com/paper/continuous-dropout
|
Continuous Dropout
|
1911.12675
|
https://arxiv.org/abs/1911.12675v1
|
https://arxiv.org/pdf/1911.12675v1.pdf
|
https://github.com/jasonustc/caffe-multigpu
| true | true | false |
none
|
https://paperswithcode.com/paper/squeeze-and-excitation-networks
|
Squeeze-and-Excitation Networks
|
1709.01507
|
https://arxiv.org/abs/1709.01507v4
|
https://arxiv.org/pdf/1709.01507v4.pdf
|
https://github.com/Dycollapsar/Attention-Based-for-Medicalimaging
| false | false | true |
none
|
https://paperswithcode.com/paper/falcon-an-accurate-real-time-monitor-for
|
FALCON: An accurate real-time monitor for client-based mobile network data analytics
|
1907.10110
|
https://arxiv.org/abs/1907.10110v2
|
https://arxiv.org/pdf/1907.10110v2.pdf
|
https://github.com/falkenber9/falcon
| true | true | true |
none
|
https://paperswithcode.com/paper/wavelet-convolutional-neural-networks-for
|
Wavelet Convolutional Neural Networks for Texture Classification
|
1707.07394
|
http://arxiv.org/abs/1707.07394v1
|
http://arxiv.org/pdf/1707.07394v1.pdf
|
https://github.com/menon92/WaveletCNN
| false | false | false |
tf
|
https://paperswithcode.com/paper/joint-unsupervised-learning-of-optical-flow
|
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
|
1810.03654
|
http://arxiv.org/abs/1810.03654v1
|
http://arxiv.org/pdf/1810.03654v1.pdf
|
https://github.com/baidu-research/UnDepthflow
| true | true | true |
tf
|
https://paperswithcode.com/paper/rgtsvm-support-vector-machines-on-a-gpu-in-r
|
Rgtsvm: Support Vector Machines on a GPU in R
|
1706.05544
|
http://arxiv.org/abs/1706.05544v1
|
http://arxiv.org/pdf/1706.05544v1.pdf
|
https://github.com/Danko-Lab/Rgtsvm
| true | true | true |
none
|
https://paperswithcode.com/paper/the-cosmic-linear-anisotropy-solving-system-1
|
The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes
|
1104.2933
|
http://arxiv.org/abs/1104.2933v3
|
http://arxiv.org/pdf/1104.2933v3.pdf
|
https://github.com/PoulinV/class_interacting_neutrinos
| false | false | true |
none
|
https://paperswithcode.com/paper/rethinking-motion-deblurring-training-a
|
Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images
|
2209.12675
|
https://arxiv.org/abs/2209.12675v1
|
https://arxiv.org/pdf/2209.12675v1.pdf
|
https://github.com/guillermocarbajal/segmentationbaseddeblurringdataset
| true | true | false |
tf
|
https://paperswithcode.com/paper/3d-manhattan-room-layout-reconstruction-from
|
Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
|
1910.04099
|
https://arxiv.org/abs/1910.04099v3
|
https://arxiv.org/pdf/1910.04099v3.pdf
|
https://github.com/sunset1995/HorizonNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/toyadmos-a-dataset-of-miniature-machine
|
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
|
1908.03299
|
https://arxiv.org/abs/1908.03299v1
|
https://arxiv.org/pdf/1908.03299v1.pdf
|
https://github.com/YumaKoizumi/ToyADMOS-dataset
| true | true | true |
none
|
https://paperswithcode.com/paper/gnn-explainer-a-tool-for-post-hoc-explanation
|
GNNExplainer: Generating Explanations for Graph Neural Networks
|
1903.03894
|
https://arxiv.org/abs/1903.03894v4
|
https://arxiv.org/pdf/1903.03894v4.pdf
|
https://github.com/anshul3899/GNNExplainer-Experiments
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/collective-optimization-for-variational
|
Collective optimization for variational quantum eigensolvers
|
1910.14030
|
https://arxiv.org/abs/1910.14030v1
|
https://arxiv.org/pdf/1910.14030v1.pdf
|
https://github.com/QuContractor/VQE_tutorial
| false | false | true |
none
|
https://paperswithcode.com/paper/tha3aroon-at-nsurl-2019-task-8-semantic
|
Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
|
1912.12514
|
https://arxiv.org/abs/1912.12514v1
|
https://arxiv.org/pdf/1912.12514v1.pdf
|
https://github.com/AliOsm/semantic-question-similarity
| true | true | true |
none
|
https://paperswithcode.com/paper/physics-informed-deep-learning-part-ii-data
|
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
|
1711.10566
|
http://arxiv.org/abs/1711.10566v1
|
http://arxiv.org/pdf/1711.10566v1.pdf
|
https://github.com/pierremtb/PINNs-TF2.0
| false | false | true |
tf
|
https://paperswithcode.com/paper/adversarial-robustness-guarantees-for
|
Adversarial Robustness Guarantees for Gaussian Processes
|
2104.03180
|
https://arxiv.org/abs/2104.03180v1
|
https://arxiv.org/pdf/2104.03180v1.pdf
|
https://github.com/andreapatane/check-GPclass
| true | true | false |
none
|
https://paperswithcode.com/paper/planck-2015-results-xi-cmb-power-spectra
|
Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters
|
1507.02704
|
https://arxiv.org/abs/1507.02704v3
|
https://arxiv.org/pdf/1507.02704v3.pdf
|
https://github.com/heatherprince/cosmoped
| false | false | true |
none
|
https://paperswithcode.com/paper/field-aware-factorization-machines-in-a-real
|
Field-aware Factorization Machines in a Real-world Online Advertising System
|
1701.04099
|
http://arxiv.org/abs/1701.04099v3
|
http://arxiv.org/pdf/1701.04099v3.pdf
|
https://github.com/cpapadimitriou/Click-Through-Rate-prediction
| false | false | true |
none
|
https://paperswithcode.com/paper/glas-global-to-local-safe-autonomy-synthesis
|
GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
|
2002.11807
|
https://arxiv.org/abs/2002.11807v3
|
https://arxiv.org/pdf/2002.11807v3.pdf
|
https://github.com/bpriviere/glas
| true | true | true |
none
|
https://paperswithcode.com/paper/neural-machine-translation-by-jointly
|
Neural Machine Translation by Jointly Learning to Align and Translate
|
1409.0473
|
http://arxiv.org/abs/1409.0473v7
|
http://arxiv.org/pdf/1409.0473v7.pdf
|
https://github.com/yurayli/stanford-cs224n-sol
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/words-can-shift-dynamically-adjusting-word
|
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
|
1811.09362
|
http://arxiv.org/abs/1811.09362v2
|
http://arxiv.org/pdf/1811.09362v2.pdf
|
https://github.com/righ120/multimodal_nlp
| false | false | true |
none
|
https://paperswithcode.com/paper/a-guide-to-convolution-arithmetic-for-deep
|
A guide to convolution arithmetic for deep learning
|
1603.07285
|
http://arxiv.org/abs/1603.07285v2
|
http://arxiv.org/pdf/1603.07285v2.pdf
|
https://github.com/ryan-perk/olympic_mining
| false | false | true |
none
|
https://paperswithcode.com/paper/constructing-metropolis-hastings-proposals
|
Constructing Metropolis-Hastings proposals using damped BFGS updates
|
1801.01243
|
http://arxiv.org/abs/1801.01243v2
|
http://arxiv.org/pdf/1801.01243v2.pdf
|
https://github.com/compops/qnmh-sysid2018
| true | true | true |
none
|
https://paperswithcode.com/paper/ms-marco-a-human-generated-machine-reading
|
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
|
1611.09268
|
http://arxiv.org/abs/1611.09268v3
|
http://arxiv.org/pdf/1611.09268v3.pdf
|
https://github.com/microsoft/MSMARCO-OpenKP
| false | false | true |
none
|
https://paperswithcode.com/paper/variational-cross-domain-natural-language
|
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
|
1812.08879
|
http://arxiv.org/abs/1812.08879v1
|
http://arxiv.org/pdf/1812.08879v1.pdf
|
https://github.com/andy194673/nlg-scvae
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/large-scale-study-of-curiosity-driven
|
Large-Scale Study of Curiosity-Driven Learning
|
1808.04355
|
http://arxiv.org/abs/1808.04355v1
|
http://arxiv.org/pdf/1808.04355v1.pdf
|
https://github.com/SPark9625/Large-Scale-Study-of-Curiosity-Driven-Learning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unos-unified-unsupervised-optical-flow-and
|
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
| null |
http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.pdf
|
https://github.com/baidu-research/UnDepthflow
| false | false | false |
tf
|
https://paperswithcode.com/paper/microsoft-coco-common-objects-in-context
|
Microsoft COCO: Common Objects in Context
|
1405.0312
|
http://arxiv.org/abs/1405.0312v3
|
http://arxiv.org/pdf/1405.0312v3.pdf
|
https://github.com/vlcekl/n2n-tomo
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/geometric-learning-of-the-conformational
|
Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
|
2106.13277
|
https://arxiv.org/abs/2106.13277v1
|
https://arxiv.org/pdf/2106.13277v1.pdf
|
https://github.com/pnnl/mol_dgnn
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/asteroseismology-of-16000-kepler-red-giants
|
Asteroseismology of 16000 Kepler Red Giants: Global Oscillation Parameters, Masses, and Radii
|
1802.04455
|
http://arxiv.org/abs/1802.04455v2
|
http://arxiv.org/pdf/1802.04455v2.pdf
|
https://github.com/rodrigcd/Recurrent_parameter_estimation
| false | false | true |
tf
|
https://paperswithcode.com/paper/airsim-high-fidelity-visual-and-physical
|
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
|
1705.05065
|
http://arxiv.org/abs/1705.05065v2
|
http://arxiv.org/pdf/1705.05065v2.pdf
|
https://github.com/jgaleav/AirSim
| false | false | true |
tf
|
https://paperswithcode.com/paper/convolutional-neural-network-architecture-for
|
Convolutional neural network architecture for geometric matching
|
1703.05593
|
http://arxiv.org/abs/1703.05593v2
|
http://arxiv.org/pdf/1703.05593v2.pdf
|
https://github.com/Semanti1/cnngeometric_pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/lowest-dimensional-portals-to-su-n-exotics
|
Lowest Dimensional Portals to SU($N$) Exotics
|
2010.05827
|
http://arxiv.org/abs/2010.05827v1
|
http://arxiv.org/pdf/2010.05827v1.pdf
|
https://github.com/jaulbric/Tesselation
| true | true | false |
none
|
https://paperswithcode.com/paper/lowresourceeval-2019-a-shared-task-on
|
LowResourceEval-2019: a shared task on morphological analysis for low-resource languages
|
2001.11285
|
https://arxiv.org/abs/2001.11285v1
|
https://arxiv.org/pdf/2001.11285v1.pdf
|
https://github.com/lowresource-lang-eval/morphology_scripts
| true | true | false |
none
|
https://paperswithcode.com/paper/a-simple-dynamization-of-trapezoidal-point
|
A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions
|
1912.03389
|
https://arxiv.org/abs/1912.03389v1
|
https://arxiv.org/pdf/1912.03389v1.pdf
|
https://github.com/milutinB/dynamic_trapezoidal_map_impl
| true | true | true |
none
|
https://paperswithcode.com/paper/pointnet-deep-learning-on-point-sets-for-3d
|
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
|
1612.00593
|
http://arxiv.org/abs/1612.00593v2
|
http://arxiv.org/pdf/1612.00593v2.pdf
|
https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/rethinking-atrous-convolution-for-semantic
|
Rethinking Atrous Convolution for Semantic Image Segmentation
|
1706.05587
|
http://arxiv.org/abs/1706.05587v3
|
http://arxiv.org/pdf/1706.05587v3.pdf
|
https://github.com/giovanniguidi/deeplabV3_Pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/speeding-up-vp9-intra-encoder-with
|
Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction
|
1906.06476
|
https://arxiv.org/abs/1906.06476v2
|
https://arxiv.org/pdf/1906.06476v2.pdf
|
https://github.com/Somdyuti2/H-FCN
| true | true | true |
tf
|
https://paperswithcode.com/paper/encoder-decoder-with-atrous-separable
|
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
|
1802.02611
|
http://arxiv.org/abs/1802.02611v3
|
http://arxiv.org/pdf/1802.02611v3.pdf
|
https://github.com/giovanniguidi/deeplabV3_Pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/transform-invariant-convolutional-neural
|
Transform-Invariant Convolutional Neural Networks for Image Classification and Search
|
1912.01447
|
https://arxiv.org/abs/1912.01447v1
|
https://arxiv.org/pdf/1912.01447v1.pdf
|
https://github.com/jasonustc/caffe-multigpu
| true | true | false |
none
|
https://paperswithcode.com/paper/network-trimming-a-data-driven-neuron-pruning
|
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
|
1607.03250
|
http://arxiv.org/abs/1607.03250v1
|
http://arxiv.org/pdf/1607.03250v1.pdf
|
https://github.com/Mind23-2/MindCode-24
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/streaming-word-embeddings-with-the-space
|
Streaming Word Embeddings with the Space-Saving Algorithm
|
1704.07463
|
http://arxiv.org/abs/1704.07463v1
|
http://arxiv.org/pdf/1704.07463v1.pdf
|
https://github.com/cjmay/athena
| true | true | true |
none
|
https://paperswithcode.com/paper/sentence-bert-sentence-embeddings-using
|
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
1908.10084
|
https://arxiv.org/abs/1908.10084v1
|
https://arxiv.org/pdf/1908.10084v1.pdf
|
https://github.com/aneesha/SiameseBERT-Notebook
| false | false | true |
none
|
https://paperswithcode.com/paper/darts-differentiable-architecture-search
|
DARTS: Differentiable Architecture Search
|
1806.09055
|
http://arxiv.org/abs/1806.09055v2
|
http://arxiv.org/pdf/1806.09055v2.pdf
|
https://github.com/google-research/google-research/tree/master/enas_lm
| false | false | true |
tf
|
https://paperswithcode.com/paper/regularizing-and-optimizing-lstm-language
|
Regularizing and Optimizing LSTM Language Models
|
1708.02182
|
http://arxiv.org/abs/1708.02182v1
|
http://arxiv.org/pdf/1708.02182v1.pdf
|
https://github.com/google-research/google-research/tree/master/enas_lm
| false | false | true |
tf
|
https://paperswithcode.com/paper/mect-multi-metadata-embedding-based-cross
|
MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition
|
2107.05418
|
https://arxiv.org/abs/2107.05418v1
|
https://arxiv.org/pdf/2107.05418v1.pdf
|
https://github.com/CoderMusou/MECT4CNER
| true | true | true |
pytorch
|
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/vaibhavjindal/pix2pix-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/analyzing-machine-learning-workloads-using-a
|
Analyzing Machine Learning Workloads Using a Detailed GPU Simulator
|
1811.08933
|
http://arxiv.org/abs/1811.08933v1
|
http://arxiv.org/pdf/1811.08933v1.pdf
|
https://github.com/prdalmia/gpgpu-sim-tlb
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/pixel-wise-motion-deblurring-of-thermal
|
Pixel-Wise Motion Deblurring of Thermal Videos
|
2006.04973
|
https://arxiv.org/abs/2006.04973v1
|
https://arxiv.org/pdf/2006.04973v1.pdf
|
https://github.com/umautobots/pixelwise-deblurring
| false | false | true |
none
|
https://paperswithcode.com/paper/limitations-of-lazy-training-of-two-layers
|
Limitations of Lazy Training of Two-layers Neural Networks
|
1906.08899
|
https://arxiv.org/abs/1906.08899v1
|
https://arxiv.org/pdf/1906.08899v1.pdf
|
https://github.com/bGhorbani/Lazy-Training-Neural-Nets
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-multimodal-deep-learning-framework-for
|
A multimodal deep learning framework for scalable content based visual media retrieval
|
2105.08665
|
https://arxiv.org/abs/2105.08665v1
|
https://arxiv.org/pdf/2105.08665v1.pdf
|
https://github.com/ambareeshravi/media_retrieval
| true | true | true |
none
|
https://paperswithcode.com/paper/a-general-and-adaptive-robust-loss-function
|
A General and Adaptive Robust Loss Function
|
1701.03077
|
http://arxiv.org/abs/1701.03077v10
|
http://arxiv.org/pdf/1701.03077v10.pdf
|
https://github.com/jonbarron/robust_loss_pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/an-entropy-stable-discontinuous-galerkin
|
An entropy stable discontinuous Galerkin method for the two-layer shallow water equations on curvilinear meshes
|
2306.12699
|
https://arxiv.org/abs/2306.12699v1
|
https://arxiv.org/pdf/2306.12699v1.pdf
|
https://github.com/trixi-framework/paper-2023-es_two_layer
| true | true | false |
none
|
https://paperswithcode.com/paper/cullnet-calibrated-and-pose-aware-confidence
|
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
|
1909.13476
|
https://arxiv.org/abs/1909.13476v1
|
https://arxiv.org/pdf/1909.13476v1.pdf
|
https://github.com/kartikgupta-at-anu/CullNet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/semantic-image-synthesis-with-spatially
|
Semantic Image Synthesis with Spatially-Adaptive Normalization
|
1903.07291
|
https://arxiv.org/abs/1903.07291v2
|
https://arxiv.org/pdf/1903.07291v2.pdf
|
https://github.com/Kokonut133/frame2frame
| false | false | true |
tf
|
https://paperswithcode.com/paper/flashlight-cnn-image-denoising
|
Flashlight CNN Image Denoising
|
2003.00762
|
https://arxiv.org/abs/2003.00762v2
|
https://arxiv.org/pdf/2003.00762v2.pdf
|
https://github.com/binhpht/flashlightCNN
| true | true | true |
none
|
https://paperswithcode.com/paper/first-exit-time-analysis-of-stochastic
|
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
|
1906.09069
|
https://arxiv.org/abs/1906.09069v1
|
https://arxiv.org/pdf/1906.09069v1.pdf
|
https://github.com/umutsimsekli/sgd_first_exit_time
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/expressive-power-of-tensor-network-1
|
Expressive power of tensor-network factorizations for probabilistic modeling
| null |
http://papers.nips.cc/paper/8429-expressive-power-of-tensor-network-factorizations-for-probabilistic-modeling
|
http://papers.nips.cc/paper/8429-expressive-power-of-tensor-network-factorizations-for-probabilistic-modeling.pdf
|
https://github.com/glivan/tensor_networks_for_probabilistic_modeling
| true | true | false |
none
|
https://paperswithcode.com/paper/importance-resampling-for-off-policy
|
Importance Resampling for Off-policy Prediction
|
1906.04328
|
https://arxiv.org/abs/1906.04328v2
|
https://arxiv.org/pdf/1906.04328v2.pdf
|
https://github.com/mkschleg/Resampling.jl
| true | false | false |
none
|
https://paperswithcode.com/paper/metaquant-learning-to-quantize-by-learning-to
|
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
| null |
http://papers.nips.cc/paper/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization
|
http://papers.nips.cc/paper/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization.pdf
|
https://github.com/csyhhu/MetaQuant
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/phyre-a-new-benchmark-for-physical-reasoning
|
PHYRE: A New Benchmark for Physical Reasoning
|
1908.05656
|
https://arxiv.org/abs/1908.05656v1
|
https://arxiv.org/pdf/1908.05656v1.pdf
|
https://github.com/facebookresearch/phyre
| true | false | false |
none
|
https://paperswithcode.com/paper/towards-a-zero-one-law-for-entrywise-low-rank
|
Towards a Zero-One Law for Column Subset Selection
|
1811.01442
|
https://arxiv.org/abs/1811.01442v2
|
https://arxiv.org/pdf/1811.01442v2.pdf
|
https://github.com/zpl7840/general_loss_column_subset_selection
| true | false | false |
none
|
https://paperswithcode.com/paper/semantically-regularized-logic-graph
|
Embedding Symbolic Knowledge into Deep Networks
|
1909.01161
|
https://arxiv.org/abs/1909.01161v4
|
https://arxiv.org/pdf/1909.01161v4.pdf
|
https://github.com/ZiweiXU/LENSR
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/limitations-of-lazy-training-of-two-layers-1
|
Limitations of Lazy Training of Two-layers Neural Network
| null |
http://papers.nips.cc/paper/9111-limitations-of-lazy-training-of-two-layers-neural-network
|
http://papers.nips.cc/paper/9111-limitations-of-lazy-training-of-two-layers-neural-network.pdf
|
https://github.com/bGhorbani/Lazy-Training-Neural-Nets
| true | false | false |
tf
|
https://paperswithcode.com/paper/neural-discrete-representation-learning
|
Neural Discrete Representation Learning
|
1711.00937
|
http://arxiv.org/abs/1711.00937v2
|
http://arxiv.org/pdf/1711.00937v2.pdf
|
https://github.com/iomanker/VQVAE-TF2
| false | false | true |
tf
|
https://paperswithcode.com/paper/reinforcement-learning-with-convex
|
Reinforcement Learning with Convex Constraints
|
1906.09323
|
https://arxiv.org/abs/1906.09323v2
|
https://arxiv.org/pdf/1906.09323v2.pdf
|
https://github.com/xkianteb/ApproPO
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/surfing-iterative-optimization-over
|
Surfing: Iterative optimization over incrementally trained deep networks
|
1907.08653
|
https://arxiv.org/abs/1907.08653v1
|
https://arxiv.org/pdf/1907.08653v1.pdf
|
https://github.com/jdlafferty/surfing
| true | false | false |
tf
|
https://paperswithcode.com/paper/a-neurally-plausible-model-learns-successor
|
A neurally plausible model learns successor representations in partially observable environments
|
1906.09480
|
https://arxiv.org/abs/1906.09480v1
|
https://arxiv.org/pdf/1906.09480v1.pdf
|
https://github.com/evertes/distributional_SF
| true | false | false |
none
|
https://paperswithcode.com/paper/compositional-plan-vectors
|
Compositional Plan Vectors
| null |
http://papers.nips.cc/paper/9636-compositional-plan-vectors
|
http://papers.nips.cc/paper/9636-compositional-plan-vectors.pdf
|
https://github.com/cdevin/cpv
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/compiler-auto-vectorization-with-imitation
|
Compiler Auto-Vectorization with Imitation Learning
| null |
http://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning
|
http://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning.pdf
|
https://github.com/ithemal/vemal
| true | false | false |
none
|
https://paperswithcode.com/paper/integrating-semantics-and-neighborhood
|
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
|
2105.13066
|
https://arxiv.org/abs/2105.13066v1
|
https://arxiv.org/pdf/2105.13066v1.pdf
|
https://github.com/MindSpore-paper-code-3/code9/tree/main/snuh
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
|
Deep Residual Learning for Image Recognition
|
1512.03385
|
http://arxiv.org/abs/1512.03385v1
|
http://arxiv.org/pdf/1512.03385v1.pdf
|
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
| false | false | false |
none
|
https://paperswithcode.com/paper/an-information-theoretic-framework-for-the
|
An Information-theoretic Framework for the Lossy Compression of Link Streams
|
1807.06874
|
http://arxiv.org/abs/1807.06874v1
|
http://arxiv.org/pdf/1807.06874v1.pdf
|
https://github.com/Lamarche-Perrin/greedy-graph-compression
| false | false | true |
none
|
https://paperswithcode.com/paper/matrix-product-states-and-the-nonabelian
|
Matrix product states and the nonabelian rotor model
|
1507.06624
|
http://arxiv.org/abs/1507.06624v2
|
http://arxiv.org/pdf/1507.06624v2.pdf
|
https://github.com/amilsted/mps-rotors
| false | false | true |
none
|
https://paperswithcode.com/paper/capsules-with-inverted-dot-product-attention-1
|
Capsules with Inverted Dot-Product Attention Routing
|
2002.04764
|
https://arxiv.org/abs/2002.04764v2
|
https://arxiv.org/pdf/2002.04764v2.pdf
|
https://github.com/yaohungt/Capsules-Inverted-Attention-Routing
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/learning-to-predict-without-looking-ahead
|
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
|
1910.13038
|
https://arxiv.org/abs/1910.13038v2
|
https://arxiv.org/pdf/1910.13038v2.pdf
|
https://github.com/google/brain-tokyo-workshop
| false | false | true |
none
|
https://paperswithcode.com/paper/designing-network-design-spaces
|
Designing Network Design Spaces
|
2003.13678
|
https://arxiv.org/abs/2003.13678v1
|
https://arxiv.org/pdf/2003.13678v1.pdf
|
https://github.com/tuggeluk/pycls
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/stochastic-variational-video-prediction
|
Stochastic Variational Video Prediction
|
1710.11252
|
http://arxiv.org/abs/1710.11252v2
|
http://arxiv.org/pdf/1710.11252v2.pdf
|
https://github.com/StanfordVL/roboturk_real_dataset
| false | false | true |
tf
|
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence
|
Convolutional Neural Networks for Sentence Classification
|
1408.5882
|
http://arxiv.org/abs/1408.5882v2
|
http://arxiv.org/pdf/1408.5882v2.pdf
|
https://github.com/threelittlemonkeys/cnn-text-classification-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/super-low-resolution-rf-powered
|
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits
|
2003.08530
|
https://arxiv.org/abs/2003.08530v1
|
https://arxiv.org/pdf/2003.08530v1.pdf
|
https://github.com/AdelaideAuto-IDLab/ID-Sensor
| true | true | false |
tf
|
https://paperswithcode.com/paper/k-space-deep-learning-for-accelerated-mri
|
k-Space Deep Learning for Accelerated MRI
|
1805.03779
|
https://arxiv.org/abs/1805.03779v3
|
https://arxiv.org/pdf/1805.03779v3.pdf
|
https://github.com/hanyoseob/k-space-deep-learning
| false | false | true |
none
|
https://paperswithcode.com/paper/optimal-routing-for-constant-function-market
|
Optimal Routing for Constant Function Market Makers
|
2204.05238
|
https://arxiv.org/abs/2204.05238v1
|
https://arxiv.org/pdf/2204.05238v1.pdf
|
https://github.com/angeris/cfmm-routing-code
| true | true | false |
none
|
https://paperswithcode.com/paper/reachability-analysis-for-feed-forward-neural
|
Reachability Analysis for Feed-Forward Neural Networks using Face Lattices
|
2003.01226
|
https://arxiv.org/abs/2003.01226v1
|
https://arxiv.org/pdf/2003.01226v1.pdf
|
https://github.com/verivital/FaceLattice
| true | true | false |
none
|
https://paperswithcode.com/paper/deep-learning-with-convolutional-neural
|
Deep learning with convolutional neural networks for EEG decoding and visualization
|
1703.05051
|
http://arxiv.org/abs/1703.05051v5
|
http://arxiv.org/pdf/1703.05051v5.pdf
|
https://github.com/rczhen/Movement-Classification-based-on-Electroencephalography-EEG-Signals
| false | false | true |
none
|
https://paperswithcode.com/paper/factorization-tricks-for-lstm-networks
|
Factorization tricks for LSTM networks
|
1703.10722
|
http://arxiv.org/abs/1703.10722v3
|
http://arxiv.org/pdf/1703.10722v3.pdf
|
https://github.com/rdspring1/PyTorch_GBW_LM
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/contrastive-adaptation-network-for
|
Contrastive Adaptation Network for Unsupervised Domain Adaptation
|
1901.00976
|
http://arxiv.org/abs/1901.00976v2
|
http://arxiv.org/pdf/1901.00976v2.pdf
|
https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/learning-to-generalize-meta-learning-for
|
Learning to Generalize: Meta-Learning for Domain Generalization
|
1710.03463
|
http://arxiv.org/abs/1710.03463v1
|
http://arxiv.org/pdf/1710.03463v1.pdf
|
https://github.com/HAHA-DL/MLDG
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/kervolutional-neural-networks
|
Kervolutional Neural Networks
|
1904.03955
|
https://arxiv.org/abs/1904.03955v2
|
https://arxiv.org/pdf/1904.03955v2.pdf
|
https://github.com/ryanaleksander/kernel-convolution
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/probably-approximately-correct-vision-based
|
Probably Approximately Correct Vision-Based Planning using Motion Primitives
|
2002.12852
|
https://arxiv.org/abs/2002.12852v2
|
https://arxiv.org/pdf/2002.12852v2.pdf
|
https://github.com/irom-lab/PAC-Vision-Planning
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/practical-calibration-of-the-temperature
|
Practical calibration of the temperature parameter in Gibbs posteriors
|
2004.10522
|
https://arxiv.org/abs/2004.10522v1
|
https://arxiv.org/pdf/2004.10522v1.pdf
|
https://github.com/lucieperrotta/temperature_calibration
| true | true | true |
none
|
https://paperswithcode.com/paper/constraint-answer-set-programming-without
|
Constraint Answer Set Programming without Grounding
|
1804.11162
|
https://arxiv.org/abs/1804.11162v2
|
https://arxiv.org/pdf/1804.11162v2.pdf
|
https://github.com/sarat-chandra-varanasi/pysasp
| false | false | true |
none
|
https://paperswithcode.com/paper/definition-of-static-and-dynamic-load-models
|
Definition of Static and Dynamic Load Models for Grid Studies of Electric Vehicles Connected to Fast Charging Stations
|
2302.03943
|
https://arxiv.org/abs/2302.03943v1
|
https://arxiv.org/pdf/2302.03943v1.pdf
|
https://github.com/davide-del-giudice/electric_vehicle_models
| true | true | false |
none
|
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