Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
xet
Community
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242
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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/clock-me/text-restyle
false
false
true
pytorch
https://paperswithcode.com/paper/shellcode-ia32-a-dataset-for-automatic
Shellcode_IA32: A Dataset for Automatic Shellcode Generation
2104.13100
https://arxiv.org/abs/2104.13100v4
https://arxiv.org/pdf/2104.13100v4.pdf
https://github.com/dessertlab/Shellcode_IA32
true
true
true
none
https://paperswithcode.com/paper/staktau-profiling-hpc-applications-operating
STaKTAU: profiling HPC applications' operating system usage
2304.11205
https://arxiv.org/abs/2304.11205v1
https://arxiv.org/pdf/2304.11205v1.pdf
https://github.com/coti/staktau
true
true
false
none
https://paperswithcode.com/paper/meta-learners-for-estimating-heterogeneous
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
1706.03461
http://arxiv.org/abs/1706.03461v5
http://arxiv.org/pdf/1706.03461v5.pdf
https://github.com/forestry-labs/causalToolbox
false
false
true
none
https://paperswithcode.com/paper/information-theoretic-stochastic-contrastive-1
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
2112.09653
https://arxiv.org/abs/2112.09653v1
https://arxiv.org/pdf/2112.09653v1.pdf
https://github.com/vkinakh/InfoSCC-GAN
true
true
false
pytorch
https://paperswithcode.com/paper/ernet-efficient-and-reliable-human-object
ERNet: Efficient and Reliable Human-Object Interaction Detection
null
https://ieeexplore.ieee.org/abstract/document/10026602
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10026602
https://github.com/Monash-CyPhi-AI-Research-Lab/ernet
false
true
false
pytorch
https://paperswithcode.com/paper/coherent-semantic-attention-for-image
Coherent Semantic Attention for Image Inpainting
1905.12384
https://arxiv.org/abs/1905.12384v3
https://arxiv.org/pdf/1905.12384v3.pdf
https://github.com/yangyucheng000/University/tree/main/model-2/cohere
false
false
false
mindspore
https://paperswithcode.com/paper/utterance-level-aggregation-for-speaker
Utterance-level Aggregation For Speaker Recognition In The Wild
1902.10107
https://arxiv.org/abs/1902.10107v2
https://arxiv.org/pdf/1902.10107v2.pdf
https://github.com/khassanoff/Speaker_Verification
false
false
true
pytorch
https://paperswithcode.com/paper/exactly-computing-the-local-lipschitz
Exactly Computing the Local Lipschitz Constant of ReLU Networks
2003.01219
https://arxiv.org/abs/2003.01219v2
https://arxiv.org/pdf/2003.01219v2.pdf
https://github.com/revbucket/lipMIP
false
false
true
pytorch
https://paperswithcode.com/paper/neural-network-verification-in-control
Neural Network Verification in Control
2110.01388
https://arxiv.org/abs/2110.01388v1
https://arxiv.org/pdf/2110.01388v1.pdf
https://github.com/mit-acl/nn_robustness_analysis
true
true
false
pytorch
https://paperswithcode.com/paper/of-models-and-tin-men-a-behavioural-economics
Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models
2307.11137
https://arxiv.org/abs/2307.11137v3
https://arxiv.org/pdf/2307.11137v3.pdf
https://github.com/phelps-sg/llm-cooperation
true
true
true
none
https://paperswithcode.com/paper/effectiveness-of-anonymization-in-double
Effectiveness of Anonymization in Double-Blind Review
1709.01609
https://arxiv.org/abs/1709.01609v1
https://arxiv.org/pdf/1709.01609v1.pdf
https://github.com/double-blind-reviewing/double-blind-reviewing.github.io
false
false
true
none
https://paperswithcode.com/paper/padim-a-patch-distribution-modeling-framework
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
2011.08785
https://arxiv.org/abs/2011.08785v1
https://arxiv.org/pdf/2011.08785v1.pdf
https://github.com/koheitokda/PaDiM
false
false
true
pytorch
https://paperswithcode.com/paper/model-construction-for-convex-constrained
Model Construction for Convex-Constrained Derivative-Free Optimization
2403.14960
https://arxiv.org/abs/2403.14960v1
https://arxiv.org/pdf/2403.14960v1.pdf
https://github.com/numericalalgorithmsgroup/pybobyqa
false
false
true
none
https://paperswithcode.com/paper/mixmatch-a-holistic-approach-to-semi
MixMatch: A Holistic Approach to Semi-Supervised Learning
1905.02249
https://arxiv.org/abs/1905.02249v2
https://arxiv.org/pdf/1905.02249v2.pdf
https://github.com/smkim7-kr/albu-MixMatch-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/aspect-sentiment-triplet-extraction-using
Aspect Sentiment Triplet Extraction Using Reinforcement Learning
2108.06107
https://arxiv.org/abs/2108.06107v1
https://arxiv.org/pdf/2108.06107v1.pdf
https://github.com/declare-lab/aste-rl
true
true
true
pytorch
https://paperswithcode.com/paper/edgeformer-improving-light-weight-convnets-by
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
2203.03952
https://arxiv.org/abs/2203.03952v5
https://arxiv.org/pdf/2203.03952v5.pdf
https://github.com/hkzhang91/edgeformer
true
true
true
pytorch
https://paperswithcode.com/paper/optimal-multiple-change-point-detection-for
Optimal multiple change-point detection for high-dimensional data
2011.07818
https://arxiv.org/abs/2011.07818v2
https://arxiv.org/pdf/2011.07818v2.pdf
https://github.com/epilliat/multicpdetec
true
true
false
pytorch
https://paperswithcode.com/paper/tower-data-structures-in-quantum
Tower: Data Structures in Quantum Superposition
2205.10255
https://arxiv.org/abs/2205.10255v3
https://arxiv.org/pdf/2205.10255v3.pdf
https://github.com/psg-mit/tower-oopsla22
true
true
false
none
https://paperswithcode.com/paper/deepsplit-scalable-verification-of-deep
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
2106.09117
https://arxiv.org/abs/2106.09117v3
https://arxiv.org/pdf/2106.09117v3.pdf
https://github.com/shaoruchen/deepsplit
true
true
true
pytorch
https://paperswithcode.com/paper/post-selection-inference-for-l1-penalized
Post-selection inference for L1-penalized likelihood models
1602.07358
http://arxiv.org/abs/1602.07358v3
http://arxiv.org/pdf/1602.07358v3.pdf
https://github.com/quentin-duchemin/sigle
false
false
true
none
https://paperswithcode.com/paper/relating-human-perception-of-musicality-to
Relating Human Perception of Musicality to Prediction in a Predictive Coding Model
2210.16587
https://arxiv.org/abs/2210.16587v1
https://arxiv.org/pdf/2210.16587v1.pdf
https://github.com/nikolasmcneal/music-prediction
false
false
true
pytorch
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-large
Very Deep Convolutional Networks for Large-Scale Image Recognition
1409.1556
http://arxiv.org/abs/1409.1556v6
http://arxiv.org/pdf/1409.1556v6.pdf
https://github.com/jiweibo/imagenet
false
false
true
pytorch
https://paperswithcode.com/paper/squeezenet-alexnet-level-accuracy-with-50x
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
1602.07360
http://arxiv.org/abs/1602.07360v4
http://arxiv.org/pdf/1602.07360v4.pdf
https://github.com/jiweibo/imagenet
false
false
true
pytorch
https://paperswithcode.com/paper/graph-convolutional-matrix-completion
Graph Convolutional Matrix Completion
1706.02263
http://arxiv.org/abs/1706.02263v2
http://arxiv.org/pdf/1706.02263v2.pdf
https://github.com/OweysMomenzada/Graph-Neural-Networks-for-effecient-Recommender-Systems
false
false
true
pytorch
https://paperswithcode.com/paper/objects-as-points
Objects as Points
1904.07850
http://arxiv.org/abs/1904.07850v2
http://arxiv.org/pdf/1904.07850v2.pdf
https://github.com/mindspore-ai/models/tree/master/research/cv/centernet_resnet50_v1
false
false
false
mindspore
https://paperswithcode.com/paper/dyverse-dynamic-vertical-scaling-in-multi
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
1810.04608
http://arxiv.org/abs/1810.04608v1
http://arxiv.org/pdf/1810.04608v1.pdf
https://github.com/qub-blesson/DYVERSE
false
false
true
none
https://paperswithcode.com/paper/generalization-without-systematicity-on-the
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
1711.00350
http://arxiv.org/abs/1711.00350v3
http://arxiv.org/pdf/1711.00350v3.pdf
https://github.com/yoonkim/neural-qcfg
false
false
true
pytorch
https://paperswithcode.com/paper/styleptb-a-compositional-benchmark-for-fine
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
2104.05196
https://arxiv.org/abs/2104.05196v1
https://arxiv.org/pdf/2104.05196v1.pdf
https://github.com/yoonkim/neural-qcfg
false
false
true
pytorch
https://paperswithcode.com/paper/special-subsets-of-addresses-for-blockchains
Special subsets of addresses for blockchains using the secp256k1 curve
2206.14107
https://arxiv.org/abs/2206.14107v1
https://arxiv.org/pdf/2206.14107v1.pdf
https://github.com/gitgab19/blockchain_addresses_list
true
true
true
none
https://paperswithcode.com/paper/joint-inference-of-multiple-graphs-with
Joint inference of multiple graphs with hidden variables from stationary graph signals
2110.03666
https://arxiv.org/abs/2110.03666v2
https://arxiv.org/pdf/2110.03666v2.pdf
https://github.com/reysam93/hidden_joint_inference
true
true
false
none
https://paperswithcode.com/paper/verifiable-smart-contract-portability
Verifiable Smart Contract Portability
1902.03868
http://arxiv.org/abs/1902.03868v1
http://arxiv.org/pdf/1902.03868v1.pdf
https://github.com/informartin/VeriSmart
true
true
true
none
https://paperswithcode.com/paper/deep-particulate-matter-forecasting-model
Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss
2106.03032
https://arxiv.org/abs/2106.03032v2
https://arxiv.org/pdf/2106.03032v2.pdf
https://github.com/appleparan/mise.py
true
true
true
pytorch
https://paperswithcode.com/paper/modeling-long-and-short-term-temporal
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
1703.07015
http://arxiv.org/abs/1703.07015v3
http://arxiv.org/pdf/1703.07015v3.pdf
https://github.com/appleparan/mise.py
false
false
true
pytorch
https://paperswithcode.com/paper/aegis-mitigating-targeted-bit-flip-attacks
Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks
2302.13520
https://arxiv.org/abs/2302.13520v1
https://arxiv.org/pdf/2302.13520v1.pdf
https://github.com/wjl123wjl/aegis
true
true
false
pytorch
https://paperswithcode.com/paper/knowledge-graph-informed-fake-news
Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
2110.10457
https://arxiv.org/abs/2110.10457v2
https://arxiv.org/pdf/2110.10457v2.pdf
https://gitlab.com/boshko.koloski/codename_fn_b
true
false
false
pytorch
https://paperswithcode.com/paper/a-study-in-mathbb-g-mathbb-r-geq-0-from-the
A study in $\mathbb{G}_{\mathbb{R}, \geq 0}$: from the geometric case book of Wilson loop diagrams and SYM $N=4$
1803.00958
https://arxiv.org/abs/1803.00958v1
https://arxiv.org/pdf/1803.00958v1.pdf
https://github.com/zeefryer/positroids
false
false
true
none
https://paperswithcode.com/paper/deep-analysis-on-subgraph-isomorphism
Deep Analysis on Subgraph Isomorphism
2012.06802
https://arxiv.org/abs/2012.06802v2
https://arxiv.org/pdf/2012.06802v2.pdf
https://github.com/bookug/siep
true
false
true
none
https://paperswithcode.com/paper/optimal-monomial-quadratization-for-ode
Optimal monomial quadratization for ODE systems
2103.08013
https://arxiv.org/abs/2103.08013v3
https://arxiv.org/pdf/2103.08013v3.pdf
https://github.com/AndreyBychkov/QBee
true
true
true
none
https://paperswithcode.com/paper/distance-function-for-spike-prediction
Spike distance function as a learning objective for spike prediction
2312.01966
https://arxiv.org/abs/2312.01966v2
https://arxiv.org/pdf/2312.01966v2.pdf
https://github.com/kevindoran/spikedistance
true
true
false
pytorch
https://paperswithcode.com/paper/validating-simulations-of-user-query-variants
Validating Simulations of User Query Variants
2201.07620
https://arxiv.org/abs/2201.07620v2
https://arxiv.org/pdf/2201.07620v2.pdf
https://github.com/irgroup/ecir2022-uqv-sim
true
true
false
none
https://paperswithcode.com/paper/the-imspoc-snapshot-imaging-spectrometer
Interferometer response characterization algorithm for multi-aperture Fabry-Perot imaging spectrometers
2303.14076
https://arxiv.org/abs/2303.14076v4
https://arxiv.org/pdf/2303.14076v4.pdf
https://github.com/danaroth83/irca
true
true
false
none
https://paperswithcode.com/paper/divnoising-diversity-denoising-with-fully
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
2006.06072
https://arxiv.org/abs/2006.06072v2
https://arxiv.org/pdf/2006.06072v2.pdf
https://github.com/IVRL/w2s
false
false
true
tf
https://paperswithcode.com/paper/joint-self-supervised-blind-denoising-and
Joint self-supervised blind denoising and noise estimation
2102.08023
https://arxiv.org/abs/2102.08023v1
https://arxiv.org/pdf/2102.08023v1.pdf
https://github.com/IVRL/w2s
false
false
true
tf
https://paperswithcode.com/paper/lessons-on-parameter-sharing-across-layers-in
Lessons on Parameter Sharing across Layers in Transformers
2104.06022
https://arxiv.org/abs/2104.06022v4
https://arxiv.org/pdf/2104.06022v4.pdf
https://github.com/takase/share_layer_params
true
true
true
pytorch
https://paperswithcode.com/paper/improving-blind-spot-denoising-for-microscopy
Improving Blind Spot Denoising for Microscopy
2008.08414
https://arxiv.org/abs/2008.08414v1
https://arxiv.org/pdf/2008.08414v1.pdf
https://github.com/IVRL/w2s
false
false
true
tf
https://paperswithcode.com/paper/energy-efficient-parking-analytics-system
Energy-Efficient Parking Analytics System using Deep Reinforcement Learning
2202.08973
https://arxiv.org/abs/2202.08973v2
https://arxiv.org/pdf/2202.08973v2.pdf
https://github.com/pittcps/rl-parking
true
true
true
pytorch
https://paperswithcode.com/paper/let-there-be-a-clock-on-the-beach-reducing
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning
2110.01705
https://arxiv.org/abs/2110.01705v2
https://arxiv.org/pdf/2110.01705v2.pdf
https://github.com/furkanbiten/object-bias
true
true
true
pytorch
https://paperswithcode.com/paper/order-optimal-one-shot-distributed-learning
Order Optimal One-Shot Distributed Learning
1911.00731
https://arxiv.org/abs/1911.00731v1
https://arxiv.org/pdf/1911.00731v1.pdf
https://github.com/sabersalehk/MRE_C
false
false
true
none
https://paperswithcode.com/paper/few-shot-clustering-for-indoor-occupancy
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras
2008.05654
https://arxiv.org/abs/2008.05654v1
https://arxiv.org/pdf/2008.05654v1.pdf
https://github.com/Homagn/Few_shot_clustering
true
false
true
pytorch
https://paperswithcode.com/paper/skew-gaussian-processes-for-classification
Skew Gaussian Processes for Classification
2005.12987
https://arxiv.org/abs/2005.12987v1
https://arxiv.org/pdf/2005.12987v1.pdf
https://github.com/benavoli/SkewGP
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/diff7/DARTS-devices
false
false
true
pytorch
https://paperswithcode.com/paper/a-non-parametric-proportional-risk-model-to
A non-parametric proportional risk model to assess a treatment effect in time-to-event data
2303.07479
https://arxiv.org/abs/2303.07479v1
https://arxiv.org/pdf/2303.07479v1.pdf
https://github.com/luciaameis/nppr-model-for-time-to-event-data
true
true
false
none
https://paperswithcode.com/paper/combining-learned-skills-and-reinforcement
Learning to combine primitive skills: A step towards versatile robotic manipulation
1908.00722
https://arxiv.org/abs/1908.00722v3
https://arxiv.org/pdf/1908.00722v3.pdf
https://github.com/rstrudel/rlbc
true
false
true
pytorch
https://paperswithcode.com/paper/learning-to-augment-synthetic-images-for
Learning to Augment Synthetic Images for Sim2Real Policy Transfer
1903.07740
https://arxiv.org/abs/1903.07740v2
https://arxiv.org/pdf/1903.07740v2.pdf
https://github.com/rstrudel/rlbc
true
false
true
pytorch
https://paperswithcode.com/paper/hypothesis-tests-for-structured-rank
Hypothesis tests for structured rank correlation matrices
2007.09738
https://arxiv.org/abs/2007.09738v2
https://arxiv.org/pdf/2007.09738v2.pdf
https://github.com/samperochkin/testing-tau
true
true
false
none
https://paperswithcode.com/paper/analysing-e-commerce-a-b-tests-with-dependent
Measuring e-Commerce Metric Changes in Online Experiments
2210.17187
https://arxiv.org/abs/2210.17187v2
https://arxiv.org/pdf/2210.17187v2.pdf
https://github.com/liuchbryan/oce-ecomm-abv-calculation
true
false
false
none
https://paperswithcode.com/paper/utilizing-automated-breast-cancer-detection
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
1905.10841
https://arxiv.org/abs/1905.10841v3
https://arxiv.org/pdf/1905.10841v3.pdf
https://github.com/SBU-BMI/quip_cancer_segmentation
false
false
true
pytorch
https://paperswithcode.com/paper/the-percolating-cluster-is-invisible-to-image
The percolating cluster is invisible to image recognition with deep learning
2303.15298
https://arxiv.org/abs/2303.15298v1
https://arxiv.org/pdf/2303.15298v1.pdf
https://github.com/DisQS/MachineLearning-Percolation
true
false
false
pytorch
https://paperswithcode.com/paper/semi-supervised-domain-generalizable-person
Semi-Supervised Domain Generalizable Person Re-Identification
2108.05045
https://arxiv.org/abs/2108.05045v2
https://arxiv.org/pdf/2108.05045v2.pdf
https://github.com/JDAI-CV/fast-reid
true
true
true
pytorch
https://paperswithcode.com/paper/exact-and-heuristic-algorithms-for-energy
Exact and Heuristic Algorithms for Energy-Efficient Scheduling
2203.14070
https://arxiv.org/abs/2203.14070v2
https://arxiv.org/pdf/2203.14070v2.pdf
https://github.com/orresearcher/phd-thesis
true
true
false
none
https://paperswithcode.com/paper/cost-sensitive-label-embedding-for-multi
Cost-Sensitive Label Embedding for Multi-Label Classification
1603.09048
http://arxiv.org/abs/1603.09048v5
http://arxiv.org/pdf/1603.09048v5.pdf
https://github.com/evantkchong/LEPAR
false
false
true
tf
https://paperswithcode.com/paper/fgcrec-fine-grained-geographical
FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation
null
https://ieeexplore.ieee.org/document/9148797
http://www.suyijun.tech/papers/2020-ICC-FGCRec.pdf
https://github.com/YijunSu/ICC2020_FGCRec
false
false
false
none
https://paperswithcode.com/paper/grape-fast-and-scalable-graph-processing-and
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
2110.06196
https://arxiv.org/abs/2110.06196v3
https://arxiv.org/pdf/2110.06196v3.pdf
https://github.com/AnacletoLAB/ensmallen
false
false
false
none
https://paperswithcode.com/paper/lnemlc-label-network-embeddings-for-multi
LNEMLC: Label Network Embeddings for Multi-Label Classification
1812.02956
http://arxiv.org/abs/1812.02956v2
http://arxiv.org/pdf/1812.02956v2.pdf
https://github.com/evantkchong/LEPAR
false
false
true
tf
https://paperswithcode.com/paper/rethinking-of-pedestrian-attribute
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
2005.11909
https://arxiv.org/abs/2005.11909v2
https://arxiv.org/pdf/2005.11909v2.pdf
https://github.com/evantkchong/LEPAR
false
false
true
tf
https://paperswithcode.com/paper/deep-imbalanced-attribute-classification
Deep Imbalanced Attribute Classification using Visual Attention Aggregation
1807.03903
http://arxiv.org/abs/1807.03903v2
http://arxiv.org/pdf/1807.03903v2.pdf
https://github.com/evantkchong/LEPAR
false
false
true
tf
https://paperswithcode.com/paper/social-welfare-maximization-and-conformism
Maximizing Social Welfare and Agreement via Information Design in Linear-Quadratic-Gaussian Games
2102.13047
https://arxiv.org/abs/2102.13047v2
https://arxiv.org/pdf/2102.13047v2.pdf
https://github.com/furkansezer/Furkan-Sezer
false
false
true
none
https://paperswithcode.com/paper/effective-crowd-annotation-of-participants
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
null
https://aclanthology.org/2020.findings-emnlp.274
https://aclanthology.org/2020.findings-emnlp.274.pdf
https://github.com/markus-zlabinger/ssts
true
true
false
none
https://paperswithcode.com/paper/variational-inference-for-infinitely-deep
Variational Inference for Infinitely Deep Neural Networks
2209.10091
https://arxiv.org/abs/2209.10091v1
https://arxiv.org/pdf/2209.10091v1.pdf
https://github.com/anazaret/unbounded-depth-neural-networks
true
true
false
pytorch
https://paperswithcode.com/paper/extended-u-net-for-speaker-verification-in
Extended U-Net for Speaker Verification in Noisy Environments
2206.13044
https://arxiv.org/abs/2206.13044v1
https://arxiv.org/pdf/2206.13044v1.pdf
https://github.com/wngh1187/exu-net
true
true
false
pytorch
https://paperswithcode.com/paper/learning-spatiotemporal-features-with-3d
Learning Spatiotemporal Features with 3D Convolutional Networks
1412.0767
http://arxiv.org/abs/1412.0767v4
http://arxiv.org/pdf/1412.0767v4.pdf
https://github.com/facebookarchive/C3D
true
false
false
caffe2
https://paperswithcode.com/paper/desiderata-for-representation-learning-a
Desiderata for Representation Learning: A Causal Perspective
2109.03795
https://arxiv.org/abs/2109.03795v2
https://arxiv.org/pdf/2109.03795v2.pdf
https://github.com/yixinwang/representation-causal-public
false
false
true
pytorch
https://paperswithcode.com/paper/catch-a-waveform-learning-to-generate-audio
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
2106.06426
https://arxiv.org/abs/2106.06426v2
https://arxiv.org/pdf/2106.06426v2.pdf
https://github.com/galgreshler/Catch-A-Waveform
true
true
true
pytorch
https://paperswithcode.com/paper/mastering-atari-go-chess-and-shogi-by
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
1911.08265
https://arxiv.org/abs/1911.08265v2
https://arxiv.org/pdf/1911.08265v2.pdf
https://github.com/k-lombard/CS4641_Project
false
false
true
tf
https://paperswithcode.com/paper/medcare-advancing-medical-llms-through
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation
2406.17484
https://arxiv.org/abs/2406.17484v3
https://arxiv.org/pdf/2406.17484v3.pdf
https://github.com/bluezeros/medcare
true
true
true
pytorch
https://paperswithcode.com/paper/icarl-incremental-classifier-and
iCaRL: Incremental Classifier and Representation Learning
1611.07725
http://arxiv.org/abs/1611.07725v2
http://arxiv.org/pdf/1611.07725v2.pdf
https://github.com/srebuffi/iCaRL
true
true
true
tf
https://paperswithcode.com/paper/analyzing-the-impact-of-meteorological
Analyzing the Impact of Meteorological Parameters on Rainfall Prediction
2110.11059
https://arxiv.org/abs/2110.11059v1
https://arxiv.org/pdf/2110.11059v1.pdf
https://github.com/sammyy092/impact-of-meteorological-parameters-on-rainfall
true
true
false
none
https://paperswithcode.com/paper/quantum-flatness-in-two-dimensional-cdt
Quantum Flatness in Two-Dimensional CDT Quantum Gravity
2110.11100
https://arxiv.org/abs/2110.11100v1
https://arxiv.org/pdf/2110.11100v1.pdf
https://github.com/jorenb/2d-cdt
true
true
false
none
https://paperswithcode.com/paper/deep-learning-approach-for-identification-of
Deep learning approach for identification of HII regions during reionization in 21-cm observations
2102.06713
https://arxiv.org/abs/2102.06713v2
https://arxiv.org/pdf/2102.06713v2.pdf
https://github.com/micbia/SegU-Net
true
true
true
tf
https://paperswithcode.com/paper/a-simple-framework-for-text-supervised
A Simple Framework for Text-Supervised Semantic Segmentation
null
http://openaccess.thecvf.com//content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html
http://openaccess.thecvf.com//content/CVPR2023/papers/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf
https://github.com/muyangyi/simseg
true
true
false
pytorch
https://paperswithcode.com/paper/raneus-ray-adaptive-neural-surface
RaNeuS: Ray-adaptive Neural Surface Reconstruction
2406.09801
https://arxiv.org/abs/2406.09801v1
https://arxiv.org/pdf/2406.09801v1.pdf
https://github.com/wangyida/ra-neus
true
true
false
pytorch
https://paperswithcode.com/paper/universality-of-critical-exponent-in-scale
Scaling properties of scale-free networks in degree-thresholding renormalization flows
2109.12309
https://arxiv.org/abs/2109.12309v2
https://arxiv.org/pdf/2109.12309v2.pdf
https://github.com/cdzqf/dtr
true
true
false
none
https://paperswithcode.com/paper/latentclr-a-contrastive-learning-approach-for
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
2104.00820
https://arxiv.org/abs/2104.00820v2
https://arxiv.org/pdf/2104.00820v2.pdf
https://github.com/gulperii/githubio
false
false
true
none
https://paperswithcode.com/paper/dense-passage-retrieval-for-open-domain
Dense Passage Retrieval for Open-Domain Question Answering
2004.04906
https://arxiv.org/abs/2004.04906v3
https://arxiv.org/pdf/2004.04906v3.pdf
https://github.com/alexlimh/DPR_MUF
false
false
true
pytorch
https://paperswithcode.com/paper/photo-pre-training-but-for-sketch
Photo Pre-Training, but for Sketch
null
http://openaccess.thecvf.com//content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html
http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.pdf
https://github.com/keli-sketchx/photo-pre-training-but-for-sketch
true
true
false
none
https://paperswithcode.com/paper/orbit-a-unified-simulation-framework-for
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
2301.04195
https://arxiv.org/abs/2301.04195v2
https://arxiv.org/pdf/2301.04195v2.pdf
https://github.com/NVIDIA-Omniverse/Orbit
true
false
true
pytorch
https://paperswithcode.com/paper/a-real-time-deep-network-for-crowd-counting
A Real-Time Deep Network for Crowd Counting
2002.06515
https://arxiv.org/abs/2002.06515v1
https://arxiv.org/pdf/2002.06515v1.pdf
https://github.com/jplumail/people-counting
false
false
true
tf
https://paperswithcode.com/paper/contracttinker-llm-empowered-vulnerability
ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts
2409.09661
https://arxiv.org/abs/2409.09661v1
https://arxiv.org/pdf/2409.09661v1.pdf
https://github.com/CheWang09/LLM4SMAPR
true
false
false
none
https://paperswithcode.com/paper/the-power-of-halometry
The Power of Halometry
2003.02264
https://arxiv.org/abs/2003.02264v1
https://arxiv.org/pdf/2003.02264v1.pdf
https://github.com/smsharma/neural-global-astrometry
false
false
true
pytorch
https://paperswithcode.com/paper/hci-papers-cite-hci-papers-increasingly-so
HCI Papers Cite HCI Papers, Increasingly So
2303.07539
https://arxiv.org/abs/2303.07539v2
https://arxiv.org/pdf/2303.07539v2.pdf
https://github.com/hotnany/x-index
true
true
false
none
https://paperswithcode.com/paper/deepsphere-a-graph-based-spherical-cnn-1
DeepSphere: a graph-based spherical CNN
2012.15000
https://arxiv.org/abs/2012.15000v1
https://arxiv.org/pdf/2012.15000v1.pdf
https://github.com/smsharma/neural-global-astrometry
false
false
true
pytorch
https://paperswithcode.com/paper/mining-for-dark-matter-substructure-inferring
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
1909.02005
https://arxiv.org/abs/1909.02005v2
https://arxiv.org/pdf/1909.02005v2.pdf
https://github.com/smsharma/neural-global-astrometry
false
false
true
pytorch
https://paperswithcode.com/paper/random-feature-stein-discrepancies
Random Feature Stein Discrepancies
1806.07788
https://arxiv.org/abs/1806.07788v5
https://arxiv.org/pdf/1806.07788v5.pdf
https://bitbucket.org/jhhuggins/random-feature-stein-discrepancies
true
true
true
none
https://paperswithcode.com/paper/exploring-simple-siamese-representation
Exploring Simple Siamese Representation Learning
2011.10566
https://arxiv.org/abs/2011.10566v1
https://arxiv.org/pdf/2011.10566v1.pdf
https://github.com/reza-safdari/simsiam-91.9-top1-acc-on-cifar10
false
false
true
pytorch
https://paperswithcode.com/paper/additive-margin-softmax-for-face-verification
Additive Margin Softmax for Face Verification
1801.05599
http://arxiv.org/abs/1801.05599v4
http://arxiv.org/pdf/1801.05599v4.pdf
https://github.com/dalisson/am_softmax
false
false
true
pytorch
https://paperswithcode.com/paper/a-siamese-cnn-for-image-steganalysis
A Siamese CNN for Image Steganalysis
null
https://ieeexplore.ieee.org/abstract/document/9153041
https://ieeexplore.ieee.org/abstract/document/9153041
https://github.com/albblgb/Deep-Steganalysis
false
false
false
pytorch
https://paperswithcode.com/paper/a-data-centric-framework-for-crystal
A data-centric framework for crystal structure identification in atomistic simulations using machine learning
2010.04815
https://arxiv.org/abs/2010.04815v5
https://arxiv.org/pdf/2010.04815v5.pdf
https://github.com/freitas-rodrigo/dc3
true
true
false
none
https://paperswithcode.com/paper/a-unified-framework-for-closed-form
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
2012.06846
https://arxiv.org/abs/2012.06846v2
https://arxiv.org/pdf/2012.06846v2.pdf
https://github.com/benavoli/SkewGP
true
true
true
none
https://paperswithcode.com/paper/domain-specific-bias-filtering-for-single
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
2110.00726
https://arxiv.org/abs/2110.00726v3
https://arxiv.org/pdf/2110.00726v3.pdf
https://github.com/junkunyuan/dsbf
true
true
true
pytorch