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https://paperswithcode.com/paper/where-to-look-at-the-movies-analyzing-visual
|
Where to look at the movies : Analyzing visual attention to understand movie editing
|
2102.13378
|
https://arxiv.org/abs/2102.13378v1
|
https://arxiv.org/pdf/2102.13378v1.pdf
|
https://github.com/abruckert/eye_tracking_filmmaking
| true | true | false |
none
|
https://paperswithcode.com/paper/superaccurate-camera-calibration-via-inverse
|
Superaccurate Camera Calibration via Inverse Rendering
|
2003.09177
|
https://arxiv.org/abs/2003.09177v1
|
https://arxiv.org/pdf/2003.09177v1.pdf
|
https://github.com/MortenHannemose/pytorch-vfi-cft
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/soccernet-a-scalable-dataset-for-action
|
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
|
1804.04527
|
http://arxiv.org/abs/1804.04527v2
|
http://arxiv.org/pdf/1804.04527v2.pdf
|
https://github.com/SilvioGiancola/SoccerNet-code
| false | false | false |
tf
|
https://paperswithcode.com/paper/fast-and-robust-multiple-colorchecker
|
Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks
|
1810.08639
|
http://arxiv.org/abs/1810.08639v1
|
http://arxiv.org/pdf/1810.08639v1.pdf
|
https://github.com/pedrodiamel/colorchacker-detection
| true | true | true |
none
|
https://paperswithcode.com/paper/skip-thought-vectors
|
Skip-Thought Vectors
|
1506.06726
|
http://arxiv.org/abs/1506.06726v1
|
http://arxiv.org/pdf/1506.06726v1.pdf
|
https://github.com/facebookresearch/InferSent
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/vse-improving-visual-semantic-embeddings-with
|
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
|
1707.05612
|
http://arxiv.org/abs/1707.05612v4
|
http://arxiv.org/pdf/1707.05612v4.pdf
|
https://github.com/armandvilalta/Full-network-multimodal-embeddings
| false | false | true |
none
|
https://paperswithcode.com/paper/combining-monte-carlo-tree-search-and
|
Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
|
2304.12146
|
https://arxiv.org/abs/2304.12146v1
|
https://arxiv.org/pdf/2304.12146v1.pdf
|
https://github.com/cyril-grelier/gc_wvcp_mcts
| true | true | false |
none
|
https://paperswithcode.com/paper/decision-stream-cultivating-deep-decision
|
Decision Stream: Cultivating Deep Decision Trees
|
1704.07657
|
http://arxiv.org/abs/1704.07657v3
|
http://arxiv.org/pdf/1704.07657v3.pdf
|
https://github.com/aiff22/Decision-Stream
| true | true | true |
none
|
https://paperswithcode.com/paper/leverage-eye-movement-data-for-saliency
|
How is Gaze Influenced by Image Transformations? Dataset and Model
|
1905.06803
|
https://arxiv.org/abs/1905.06803v4
|
https://arxiv.org/pdf/1905.06803v4.pdf
|
https://github.com/CZHQuality/Sal-CFS-GAN
| true | true | false |
tf
|
https://paperswithcode.com/paper/addressee-and-response-selection-in-multi
|
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
|
1709.04005
|
http://arxiv.org/abs/1709.04005v2
|
http://arxiv.org/pdf/1709.04005v2.pdf
|
https://github.com/ryanzhumich/sirnn
| true | true | true |
none
|
https://paperswithcode.com/paper/finite-sample-learning-of-moving-targets
|
Finite sample learning of moving targets
|
2408.04406
|
https://arxiv.org/abs/2408.04406v2
|
https://arxiv.org/pdf/2408.04406v2.pdf
|
https://github.com/nikovert/finite-sample-learning-of-moving-targets
| false | false | false |
none
|
https://paperswithcode.com/paper/interpret-federated-learning-with-shapley
|
Interpret Federated Learning with Shapley Values
|
1905.04519
|
https://arxiv.org/abs/1905.04519v1
|
https://arxiv.org/pdf/1905.04519v1.pdf
|
https://github.com/crownpku/federated_shap
| true | true | true |
none
|
https://paperswithcode.com/paper/lenient-multi-agent-deep-reinforcement
|
Lenient Multi-Agent Deep Reinforcement Learning
|
1707.04402
|
http://arxiv.org/abs/1707.04402v2
|
http://arxiv.org/pdf/1707.04402v2.pdf
|
https://github.com/gjp1203/nui_in_madrl
| false | false | true |
none
|
https://paperswithcode.com/paper/hierarchical-cross-modal-talking-face
|
Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss
|
1905.03820
|
https://arxiv.org/abs/1905.03820v1
|
https://arxiv.org/pdf/1905.03820v1.pdf
|
https://github.com/lelechen63/ATVGnet
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/benchmarking-natural-language-understanding
|
Benchmarking Natural Language Understanding Services for building Conversational Agents
|
1903.05566
|
http://arxiv.org/abs/1903.05566v3
|
http://arxiv.org/pdf/1903.05566v3.pdf
|
https://github.com/lackel/hierarchical_weighted_scl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hdltex-hierarchical-deep-learning-for-text
|
HDLTex: Hierarchical Deep Learning for Text Classification
|
1709.08267
|
http://arxiv.org/abs/1709.08267v2
|
http://arxiv.org/pdf/1709.08267v2.pdf
|
https://github.com/lackel/hierarchical_weighted_scl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/syntaxsqlnet-syntax-tree-networks-for-complex
|
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
|
1810.05237
|
http://arxiv.org/abs/1810.05237v2
|
http://arxiv.org/pdf/1810.05237v2.pdf
|
https://github.com/heyanger/sqltools
| false | false | true |
none
|
https://paperswithcode.com/paper/missing-data-infill-with-automunge-1
|
Missing Data Infill with Automunge
|
2202.09484
|
https://arxiv.org/abs/2202.09484v1
|
https://arxiv.org/pdf/2202.09484v1.pdf
|
https://github.com/gatorwatt/Paper_Demonstrations/tree/main/Missing_Data_infill
| true | false | false |
none
|
https://paperswithcode.com/paper/temporal-attentive-alignment-for-video-domain
|
Temporal Attentive Alignment for Video Domain Adaptation
|
1905.10861
|
https://arxiv.org/abs/1905.10861v5
|
https://arxiv.org/pdf/1905.10861v5.pdf
|
https://github.com/olivesgatech/TA3N
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hybrid-reward-architecture-for-reinforcement
|
Hybrid Reward Architecture for Reinforcement Learning
|
1706.04208
|
http://arxiv.org/abs/1706.04208v2
|
http://arxiv.org/pdf/1706.04208v2.pdf
|
https://github.com/KhenNguyn/DoAn3-MachineLearning
| false | false | true |
tf
|
https://paperswithcode.com/paper/robustness-may-be-at-odds-with-accuracy
|
Robustness May Be at Odds with Accuracy
|
1805.12152
|
https://arxiv.org/abs/1805.12152v5
|
https://arxiv.org/pdf/1805.12152v5.pdf
|
https://github.com/louis2889184/pytorch-adversarial-training
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hierarchy-of-visual-words-a-learning-based
|
Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval
|
1908.02786
|
https://arxiv.org/abs/1908.02786v1
|
https://arxiv.org/pdf/1908.02786v1.pdf
|
https://github.com/Prograf-UFF/HoVW
| true | true | true |
none
|
https://paperswithcode.com/paper/deep-reinforcement-learning-from-human
|
Deep reinforcement learning from human preferences
|
1706.03741
|
https://arxiv.org/abs/1706.03741v4
|
https://arxiv.org/pdf/1706.03741v4.pdf
|
https://github.com/vcharvet/project-rl
| false | false | true |
tf
|
https://paperswithcode.com/paper/neural-motifs-scene-graph-parsing-with-global
|
Neural Motifs: Scene Graph Parsing with Global Context
|
1711.06640
|
http://arxiv.org/abs/1711.06640v2
|
http://arxiv.org/pdf/1711.06640v2.pdf
|
https://github.com/HCPLab-SYSU/KERN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/scene-relighting-with-illumination-estimation
|
Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme
|
2006.02333
|
https://arxiv.org/abs/2006.02333v1
|
https://arxiv.org/pdf/2006.02333v1.pdf
|
https://github.com/martin-ev/2DSceneRelighting
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/m-fuse-multi-frame-fusion-for-scene-flow
|
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
|
2207.05704
|
https://arxiv.org/abs/2207.05704v2
|
https://arxiv.org/pdf/2207.05704v2.pdf
|
https://github.com/cv-stuttgart/m-fuse
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic-1
|
Fully Convolutional Networks for Semantic Segmentation
|
1411.4038
|
http://arxiv.org/abs/1411.4038v2
|
http://arxiv.org/pdf/1411.4038v2.pdf
|
https://github.com/giovanniguidi/FCN-keras
| false | false | true |
none
|
https://paperswithcode.com/paper/spurious-local-minima-are-common-in-two-layer
|
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
|
1712.08968
|
http://arxiv.org/abs/1712.08968v3
|
http://arxiv.org/pdf/1712.08968v3.pdf
|
https://github.com/ItaySafran/OneLayerGDconvergence
| true | true | false |
none
|
https://paperswithcode.com/paper/co-trained-convolutional-neural-networks-for
|
Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI
| null |
https://www.ncbi.nlm.nih.gov/pubmed/28850876
|
https://www.ncbi.nlm.nih.gov/pubmed/28850876
|
https://github.com/Andysis/co-trained-CADx
| false | false | false |
none
|
https://paperswithcode.com/paper/improving-retinanet-for-ct-lesion-detection
|
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
|
1906.02283
|
https://arxiv.org/abs/1906.02283v1
|
https://arxiv.org/pdf/1906.02283v1.pdf
|
https://github.com/fizyr/keras-retinanet
| false | false | true |
tf
|
https://paperswithcode.com/paper/one-shot-video-object-segmentation
|
One-Shot Video Object Segmentation
|
1611.05198
|
http://arxiv.org/abs/1611.05198v4
|
http://arxiv.org/pdf/1611.05198v4.pdf
|
https://github.com/kmaninis/OSVOS-caffe
| false | false | false |
tf
|
https://paperswithcode.com/paper/non-turing-computations-via-malament-hogarth
|
Non-Turing computations via Malament-Hogarth space-times
|
gr-qc/0104023
|
https://arxiv.org/abs/gr-qc/0104023v2
|
https://arxiv.org/pdf/gr-qc/0104023v2.pdf
|
https://github.com/alexnieddu/Kerr-Black-Hole-Geodesics
| false | false | true |
none
|
https://paperswithcode.com/paper/a-chi-squared-time-frequency-discriminator
|
A chi-squared time-frequency discriminator for gravitational wave detection
|
gr-qc/0405045
|
https://arxiv.org/abs/gr-qc/0405045v2
|
https://arxiv.org/pdf/gr-qc/0405045v2.pdf
|
https://github.com/gwastro/1-ogc
| false | false | true |
none
|
https://paperswithcode.com/paper/quantum-associative-memory
|
Quantum Associative Memory
|
quant-ph/9807053
|
https://arxiv.org/abs/quant-ph/9807053v1
|
https://arxiv.org/pdf/quant-ph/9807053v1.pdf
|
https://github.com/hhy37/Liquid
| false | false | true |
none
|
https://paperswithcode.com/paper/improved-simulation-of-stabilizer-circuits
|
Improved Simulation of Stabilizer Circuits
|
quant-ph/0406196
|
https://arxiv.org/abs/quant-ph/0406196v5
|
https://arxiv.org/pdf/quant-ph/0406196v5.pdf
|
https://github.com/hhy37/Liquid
| false | false | true |
none
|
https://paperswithcode.com/paper/distributed-prioritized-experience-replay
|
Distributed Prioritized Experience Replay
|
1803.00933
|
http://arxiv.org/abs/1803.00933v1
|
http://arxiv.org/pdf/1803.00933v1.pdf
|
https://github.com/neka-nat/distributed_rl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/benchmarking-automatic-machine-learning
|
Benchmarking Automatic Machine Learning Frameworks
|
1808.06492
|
http://arxiv.org/abs/1808.06492v1
|
http://arxiv.org/pdf/1808.06492v1.pdf
|
https://github.com/ClimbsRocks/auto_ml
| false | true | false |
tf
|
https://paperswithcode.com/paper/a-fofe-based-local-detection-approach-for
|
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
|
1611.00801
|
http://arxiv.org/abs/1611.00801v1
|
http://arxiv.org/pdf/1611.00801v1.pdf
|
https://github.com/xmb-cipher/fofe-ner
| true | true | true |
tf
|
https://paperswithcode.com/paper/nonnegative-decomposition-of-multivariate
|
Nonnegative Decomposition of Multivariate Information
|
1004.2515
|
http://arxiv.org/abs/1004.2515v1
|
http://arxiv.org/pdf/1004.2515v1.pdf
|
https://github.com/robince/partial-info-decomp
| false | false | true |
none
|
https://paperswithcode.com/paper/a-tutorial-on-thompson-sampling
|
A Tutorial on Thompson Sampling
|
1707.02038
|
https://arxiv.org/abs/1707.02038v3
|
https://arxiv.org/pdf/1707.02038v3.pdf
|
https://github.com/iosband/ts_tutorial
| true | true | false |
none
|
https://paperswithcode.com/paper/penalizing-unfairness-in-binary
|
Penalizing Unfairness in Binary Classification
|
1707.00044
|
http://arxiv.org/abs/1707.00044v3
|
http://arxiv.org/pdf/1707.00044v3.pdf
|
https://github.com/jjgold012/lab-project-fairness
| true | true | false |
none
|
https://paperswithcode.com/paper/teacher-student-curriculum-learning
|
Teacher-Student Curriculum Learning
|
1707.00183
|
http://arxiv.org/abs/1707.00183v2
|
http://arxiv.org/pdf/1707.00183v2.pdf
|
https://github.com/tambetm/TSCL
| true | true | true |
tf
|
https://paperswithcode.com/paper/text-matching-as-image-recognition
|
Text Matching as Image Recognition
|
1602.06359
|
http://arxiv.org/abs/1602.06359v1
|
http://arxiv.org/pdf/1602.06359v1.pdf
|
https://github.com/pl8787/MatchPyramid-TensorFlow
| false | false | true |
tf
|
https://paperswithcode.com/paper/hyperband-a-novel-bandit-based-approach-to
|
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
|
1603.06560
|
http://arxiv.org/abs/1603.06560v4
|
http://arxiv.org/pdf/1603.06560v4.pdf
|
https://github.com/zygmuntz/hyperband
| false | false | false |
none
|
https://paperswithcode.com/paper/perceptual-losses-for-real-time-style
|
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
|
1603.08155
|
http://arxiv.org/abs/1603.08155v1
|
http://arxiv.org/pdf/1603.08155v1.pdf
|
https://github.com/ksivaman/super-res
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/optimal-transport-based-machine-learning-to
|
Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data
|
2107.11192
|
https://arxiv.org/abs/2107.11192v3
|
https://arxiv.org/pdf/2107.11192v3.pdf
|
https://github.com/yen-nguyen-thi-thanh/wtot_coclust_match
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/mushroomrl-simplifying-reinforcement-learning
|
MushroomRL: Simplifying Reinforcement Learning Research
|
2001.01102
|
https://arxiv.org/abs/2001.01102v2
|
https://arxiv.org/pdf/2001.01102v2.pdf
|
https://github.com/AIRLab-POLIMI/mushroom-rl
| true | true | false |
tf
|
https://paperswithcode.com/paper/how-sgd-selects-the-global-minima-in-over
|
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective
| null |
http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective
|
http://papers.nips.cc/paper/8049-how-sgd-selects-the-global-minima-in-over-parameterized-learning-a-dynamical-stability-perspective.pdf
|
https://github.com/leiwu1990/sgd.stability
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/extending-text-to-speech-synthesis-with
|
Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging
|
2107.05550
|
https://arxiv.org/abs/2107.05550v1
|
https://arxiv.org/pdf/2107.05550v1.pdf
|
https://github.com/BME-SmartLab/txt2ult
| true | true | true |
tf
|
https://paperswithcode.com/paper/simulaqron-a-simulator-for-developing-quantum
|
SimulaQron - A simulator for developing quantum internet software
|
1712.08032
|
http://arxiv.org/abs/1712.08032v2
|
http://arxiv.org/pdf/1712.08032v2.pdf
|
https://github.com/SoftwareQuTech/CQC-Python
| false | false | true |
none
|
https://paperswithcode.com/paper/dgm-a-deep-learning-algorithm-for-solving
|
DGM: A deep learning algorithm for solving partial differential equations
|
1708.07469
|
http://arxiv.org/abs/1708.07469v5
|
http://arxiv.org/pdf/1708.07469v5.pdf
|
https://github.com/alialaradi/DeepGalerkinMethod
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-spatiotemporal-volumetric-interpolation
|
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
|
2002.12680
|
https://arxiv.org/abs/2002.12680v2
|
https://arxiv.org/pdf/2002.12680v2.pdf
|
https://github.com/guoyu-niubility/SVIN
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/metapruning-meta-learning-for-automatic
|
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
|
1903.10258
|
https://arxiv.org/abs/1903.10258v3
|
https://arxiv.org/pdf/1903.10258v3.pdf
|
https://github.com/liuzechun/MetaPruning
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/variational-adversarial-active-learning
|
Variational Adversarial Active Learning
|
1904.00370
|
https://arxiv.org/abs/1904.00370v3
|
https://arxiv.org/pdf/1904.00370v3.pdf
|
https://github.com/sinhasam/vaal
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/learning-higher-order-logic-programs
|
Learning higher-order logic programs
|
1907.10953
|
https://arxiv.org/abs/1907.10953v1
|
https://arxiv.org/pdf/1907.10953v1.pdf
|
https://github.com/metagol/metagol
| true | true | false |
none
|
https://paperswithcode.com/paper/pde-net-learning-pdes-from-data
|
PDE-Net: Learning PDEs from Data
|
1710.09668
|
http://arxiv.org/abs/1710.09668v2
|
http://arxiv.org/pdf/1710.09668v2.pdf
|
https://github.com/agrundner24/pde-net-in-tf
| false | false | true |
tf
|
https://paperswithcode.com/paper/x-lxmert-paint-caption-and-answer-questions
|
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
|
2009.11278
|
https://arxiv.org/abs/2009.11278v1
|
https://arxiv.org/pdf/2009.11278v1.pdf
|
https://github.com/allenai/x-lxmert
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/infinitygan-towards-infinite-resolution-image
|
InfinityGAN: Towards Infinite-Pixel Image Synthesis
|
2104.03963
|
https://arxiv.org/abs/2104.03963v4
|
https://arxiv.org/pdf/2104.03963v4.pdf
|
https://github.com/hubert0527/infinityGAN
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/exploring-data-aggregation-in-policy-learning
|
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving
| null |
http://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.pdf
|
https://github.com/autonomousvision/data_aggregation
| true | true | false |
none
|
https://paperswithcode.com/paper/graph-structured-prediction-energy-networks
|
Graph Structured Prediction Energy Networks
|
1910.14670
|
https://arxiv.org/abs/1910.14670v2
|
https://arxiv.org/pdf/1910.14670v2.pdf
|
https://github.com/cgraber/GSPEN
| true | true | false |
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/sidneykingsley/fyp
| false | false | true |
tf
|
https://paperswithcode.com/paper/minibatch-processing-in-spiking-neural
|
Minibatch Processing in Spiking Neural Networks
|
1909.02549
|
https://arxiv.org/abs/1909.02549v1
|
https://arxiv.org/pdf/1909.02549v1.pdf
|
https://github.com/djsaunde/snn-minibatch
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/stochastic-chebyshev-gradient-descent-for
|
Stochastic Chebyshev Gradient Descent for Spectral Optimization
|
1802.06355
|
http://arxiv.org/abs/1802.06355v3
|
http://arxiv.org/pdf/1802.06355v3.pdf
|
https://github.com/EiffL/SpectralFlow
| false | false | true |
tf
|
https://paperswithcode.com/paper/learned-image-downscaling-for-upscaling-using
|
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
|
1907.12904
|
https://arxiv.org/abs/1907.12904v2
|
https://arxiv.org/pdf/1907.12904v2.pdf
|
https://github.com/twice154/ofa-for-super-resolution
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object
|
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
|
1506.01497
|
http://arxiv.org/abs/1506.01497v3
|
http://arxiv.org/pdf/1506.01497v3.pdf
|
https://github.com/daxiapazi/faster-rcnn
| false | false | true |
tf
|
https://paperswithcode.com/paper/every-positive-integer-is-a-sum-of-three
|
Every positive integer is a sum of three palindromes
|
1602.06208
|
http://arxiv.org/abs/1602.06208v2
|
http://arxiv.org/pdf/1602.06208v2.pdf
|
https://github.com/TroyLaurin/PalindromeSum
| false | false | true |
none
|
https://paperswithcode.com/paper/reducing-the-training-time-of-neural-networks
|
Reducing the Training Time of Neural Networks by Partitioning
|
1511.02954
|
http://arxiv.org/abs/1511.02954v2
|
http://arxiv.org/pdf/1511.02954v2.pdf
|
https://github.com/agongt408/vbranch
| false | false | true |
tf
|
https://paperswithcode.com/paper/net2net-accelerating-learning-via-knowledge
|
Net2Net: Accelerating Learning via Knowledge Transfer
|
1511.05641
|
http://arxiv.org/abs/1511.05641v4
|
http://arxiv.org/pdf/1511.05641v4.pdf
|
https://github.com/agongt408/vbranch
| false | false | true |
tf
|
https://paperswithcode.com/paper/simple-non-perturbative-resummation-schemes
|
Simple non-perturbative resummation schemes beyond mean-field: case study for scalar $φ^4$ theory in 1+1 dimensions
|
1901.05483
|
http://arxiv.org/abs/1901.05483v1
|
http://arxiv.org/pdf/1901.05483v1.pdf
|
https://github.com/paro8929/Resummation
| true | true | true |
none
|
https://paperswithcode.com/paper/the-maven-dependency-graph-a-temporal-graph
|
The Maven Dependency Graph: a Temporal Graph-based Representation of Maven Central
|
1901.05392
|
http://arxiv.org/abs/1901.05392v1
|
http://arxiv.org/pdf/1901.05392v1.pdf
|
https://github.com/tdegueul/sonar-dataset
| false | false | true |
none
|
https://paperswithcode.com/paper/adversarial-training-methods-for-network
|
Adversarial Training Methods for Network Embedding
|
1908.11514
|
https://arxiv.org/abs/1908.11514v1
|
https://arxiv.org/pdf/1908.11514v1.pdf
|
https://github.com/wonniu/AdvT4NE_WWW2019
| true | true | false |
tf
|
https://paperswithcode.com/paper/central-server-free-federated-learning-over
|
Central Server Free Federated Learning over Single-sided Trust Social Networks
|
1910.04956
|
https://arxiv.org/abs/1910.04956v2
|
https://arxiv.org/pdf/1910.04956v2.pdf
|
https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/standalone/decentralized
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/expert-load-matters-operating-networks-at-1
|
Expert load matters: operating networks at high accuracy and low manual effort
|
2308.05035
|
https://arxiv.org/abs/2308.05035v2
|
https://arxiv.org/pdf/2308.05035v2.pdf
|
https://github.com/salusanga/aucoc_loss
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training
|
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
|
1706.02677
|
http://arxiv.org/abs/1706.02677v2
|
http://arxiv.org/pdf/1706.02677v2.pdf
|
https://github.com/darkreapyre/HaaS-dev
| false | false | true |
tf
|
https://paperswithcode.com/paper/fiber-cnn-expanding-mask-r-cnn-to-improve
|
FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis
|
2006.04552
|
https://arxiv.org/abs/2006.04552v2
|
https://arxiv.org/pdf/2006.04552v2.pdf
|
https://github.com/maxfrei750/synthPIC4Python
| true | true | true |
none
|
https://paperswithcode.com/paper/knee-point-identification-based-on-trade-off
|
Knee Point Identification Based on Trade-Off Utility
|
2005.11600
|
https://arxiv.org/abs/2005.11600v1
|
https://arxiv.org/pdf/2005.11600v1.pdf
|
https://github.com/COLA-Laboratory/kpi
| true | true | false |
none
|
https://paperswithcode.com/paper/matching-networks-for-one-shot-learning
|
Matching Networks for One Shot Learning
|
1606.04080
|
http://arxiv.org/abs/1606.04080v2
|
http://arxiv.org/pdf/1606.04080v2.pdf
|
https://github.com/fujenchu/matchingNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/probabilistic-fasttext-for-multi-sense-word
|
Probabilistic FastText for Multi-Sense Word Embeddings
|
1806.02901
|
http://arxiv.org/abs/1806.02901v1
|
http://arxiv.org/pdf/1806.02901v1.pdf
|
https://github.com/benathi/multisense-prob-fasttext
| true | true | true |
none
|
https://paperswithcode.com/paper/self-supervised-visual-planning-with-temporal
|
Self-Supervised Visual Planning with Temporal Skip Connections
|
1710.05268
|
http://arxiv.org/abs/1710.05268v1
|
http://arxiv.org/pdf/1710.05268v1.pdf
|
https://github.com/CompVis/image2video-synthesis-using-cINNs
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/the-hitchhikers-guide-to-lda
|
The Hitchhiker's Guide to LDA
|
1908.03142
|
https://arxiv.org/abs/1908.03142v2
|
https://arxiv.org/pdf/1908.03142v2.pdf
|
https://github.com/MachineIntellect/GibbsLDA_plus
| false | false | true |
none
|
https://paperswithcode.com/paper/shufflenet-v2-practical-guidelines-for
|
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
|
1807.11164
|
http://arxiv.org/abs/1807.11164v1
|
http://arxiv.org/pdf/1807.11164v1.pdf
|
https://github.com/savageyusuff/MobilePose-Pi
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unite-unified-translation-evaluation
|
UniTE: Unified Translation Evaluation
|
2204.13346
|
https://arxiv.org/abs/2204.13346v1
|
https://arxiv.org/pdf/2204.13346v1.pdf
|
https://github.com/wanyu2018umac/UniTE
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/generative-adversarial-networks
|
Generative Adversarial Networks
|
1406.2661
|
https://arxiv.org/abs/1406.2661v1
|
https://arxiv.org/pdf/1406.2661v1.pdf
|
https://github.com/etjoa003/medical_imaging
| false | false | true |
none
|
https://paperswithcode.com/paper/efficient-nonparametric-statistical-inference
|
Efficient nonparametric statistical inference on population feature importance using Shapley values
|
2006.09481
|
https://arxiv.org/abs/2006.09481v1
|
https://arxiv.org/pdf/2006.09481v1.pdf
|
https://github.com/bdwilliamson/spvim_supplementary
| false | false | true |
none
|
https://paperswithcode.com/paper/searching-for-mobilenetv3
|
Searching for MobileNetV3
|
1905.02244
|
https://arxiv.org/abs/1905.02244v5
|
https://arxiv.org/pdf/1905.02244v5.pdf
|
https://github.com/rwightman/efficientnet-jax
| false | false | true |
jax
|
https://paperswithcode.com/paper/wiring-up-vision-minimizing-supervised
|
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
| null |
https://openreview.net/forum?id=5i4vRgoZauw
|
https://openreview.net/pdf?id=5i4vRgoZauw
|
https://github.com/franzigeiger/training_reductions
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/coha-ntt-a-configurable-hardware-accelerator
|
CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication
| null |
https://eprint.iacr.org/2021/1527
|
https://eprint.iacr.org/2021/1527.pdf
|
https://github.com/kemalderya/pqc-param-ntt
| false | true | false |
none
|
https://paperswithcode.com/paper/physics-informed-neural-networks-for-non
|
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
|
2004.04026
|
https://arxiv.org/abs/2004.04026v2
|
https://arxiv.org/pdf/2004.04026v2.pdf
|
https://github.com/jbesty/PINN_system_identification
| false | false | true |
tf
|
https://paperswithcode.com/paper/distilling-model-knowledge
|
Distilling Model Knowledge
|
1510.02437
|
http://arxiv.org/abs/1510.02437v1
|
http://arxiv.org/pdf/1510.02437v1.pdf
|
https://github.com/gpapamak/distilling_model_knowledge
| false | false | true |
none
|
https://paperswithcode.com/paper/effective-obstruction-to-lifting-tate-classes
|
Effective obstruction to lifting Tate classes from positive characteristic
|
2003.11037
|
https://arxiv.org/abs/2003.11037v3
|
https://arxiv.org/pdf/2003.11037v3.pdf
|
https://github.com/edgarcosta/crystalline_obstruction
| true | true | true |
none
|
https://paperswithcode.com/paper/attention-is-all-you-need
|
Attention Is All You Need
|
1706.03762
|
https://arxiv.org/abs/1706.03762v7
|
https://arxiv.org/pdf/1706.03762v7.pdf
|
https://github.com/StillKeepTry/Transformer-PyTorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/on-the-importance-of-capturing-a-sufficient
|
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs
|
2101.11164
|
https://arxiv.org/abs/2101.11164v1
|
https://arxiv.org/pdf/2101.11164v1.pdf
|
https://github.com/AdamByerly/micro-pcb-analysis
| true | false | false |
tf
|
https://paperswithcode.com/paper/dropout-as-a-bayesian-approximation
|
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
|
1506.02142
|
http://arxiv.org/abs/1506.02142v6
|
http://arxiv.org/pdf/1506.02142v6.pdf
|
https://github.com/cdebeunne/uncertainties_CNN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/gaussianprocessesjl-a-nonparametric-bayes
|
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
|
1812.09064
|
https://arxiv.org/abs/1812.09064v2
|
https://arxiv.org/pdf/1812.09064v2.pdf
|
https://github.com/UnofficialJuliaMirrorSnapshots/GaussianProcesses.jl-891a1506-143c-57d2-908e-e1f8e92e6de9
| false | false | true |
none
|
https://paperswithcode.com/paper/tractable-higher-order-under-approximating-ae
|
Tractable higher-order under-approximating AE extensions for non-linear systems
|
2101.11536
|
https://arxiv.org/abs/2101.11536v1
|
https://arxiv.org/pdf/2101.11536v1.pdf
|
https://github.com/cosynus-lix/RINO
| true | false | false |
none
|
https://paperswithcode.com/paper/lightweight-probabilistic-deep-networks
|
Lightweight Probabilistic Deep Networks
|
1805.11327
|
http://arxiv.org/abs/1805.11327v1
|
http://arxiv.org/pdf/1805.11327v1.pdf
|
https://github.com/cdebeunne/uncertainties_CNN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/reducing-complexity-and-unidentifiability
|
Reducing complexity and unidentifiability when modelling human atrial cells
|
2001.10954
|
https://arxiv.org/abs/2001.10954v1
|
https://arxiv.org/pdf/2001.10954v1.pdf
|
https://github.com/charleshouston/ion-channel-ABC
| true | true | false |
none
|
https://paperswithcode.com/paper/model-agnostic-meta-learning-for-fast
|
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
|
1703.03400
|
http://arxiv.org/abs/1703.03400v3
|
http://arxiv.org/pdf/1703.03400v3.pdf
|
https://github.com/MoritzTaylor/maml-rl-tf2
| false | false | true |
tf
|
https://paperswithcode.com/paper/robust-person-re-identification-by-modelling
|
Robust Person Re-Identification by Modelling Feature Uncertainty
| null |
http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.html
|
http://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.pdf
|
https://github.com/TianyuanYu/DistributionNet
| true | true | false |
tf
|
https://paperswithcode.com/paper/hierarchical-encoding-of-sequential-data-with
|
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost
| null |
http://openaccess.thecvf.com/content_ICCV_2019/html/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.html
|
http://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.pdf
|
https://github.com/intellhave/HESSL
| true | true | false |
none
|
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