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

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/rebalanced-zero-shot-learning
Rebalanced Zero-shot Learning
2210.07031
https://arxiv.org/abs/2210.07031v2
https://arxiv.org/pdf/2210.07031v2.pdf
https://github.com/fouriye/rezsl-tip23
true
true
true
pytorch
https://paperswithcode.com/paper/legal-syllogism-prompting-teaching-large
Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction
2307.08321
https://arxiv.org/abs/2307.08321v1
https://arxiv.org/pdf/2307.08321v1.pdf
https://github.com/jiangcong7/legal-syllogism-prompting
true
true
true
none
https://paperswithcode.com/paper/kinetic-monte-carlo-methods-for-three
Kinetic Monte Carlo methods for three-dimensional diffusive capture problems in exterior domains
2406.13644
https://arxiv.org/abs/2406.13644v2
https://arxiv.org/pdf/2406.13644v2.pdf
https://github.com/alanlindsay/3DKMC
true
true
false
none
https://paperswithcode.com/paper/back-to-mass-square-d-one-the-neutrino-mass
Back to (Mass-)Square(d) One: The Neutrino Mass Ordering in Light of Recent Data
2007.08526
https://arxiv.org/abs/2007.08526v2
https://arxiv.org/pdf/2007.08526v2.pdf
https://github.com/speysidehep/example-neutrino
false
false
true
none
https://paperswithcode.com/paper/adaptssr-pre-training-user-model-with
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
2310.09706
https://arxiv.org/abs/2310.09706v2
https://arxiv.org/pdf/2310.09706v2.pdf
https://github.com/yflyl613/AdaptSSR
true
true
false
pytorch
https://paperswithcode.com/paper/multimodal-brain-age-estimation-using
Multimodal brain age estimation using interpretable adaptive population-graph learning
2307.04639
https://arxiv.org/abs/2307.04639v2
https://arxiv.org/pdf/2307.04639v2.pdf
https://github.com/bintsi/adaptive-graph-learning
true
true
true
pytorch
https://paperswithcode.com/paper/can-neural-network-memorization-be-localized
Can Neural Network Memorization Be Localized?
2307.09542
https://arxiv.org/abs/2307.09542v1
https://arxiv.org/pdf/2307.09542v1.pdf
https://github.com/pratyushmaini/localizing-memorization
true
true
false
pytorch
https://paperswithcode.com/paper/bsdm-background-suppression-diffusion-model
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
2307.09861
https://arxiv.org/abs/2307.09861v1
https://arxiv.org/pdf/2307.09861v1.pdf
https://github.com/majitao-xd/bsdm-had
true
true
false
pytorch
https://paperswithcode.com/paper/a-deep-learning-framework-for-efficient
A deep learning framework for efficient pathology image analysis
2502.13027
https://arxiv.org/abs/2502.13027v1
https://arxiv.org/pdf/2502.13027v1.pdf
https://github.com/hms-dbmi/chief
false
false
true
pytorch
https://paperswithcode.com/paper/towards-an-ai-to-win-ghana-s-national-science
Towards an AI to Win Ghana's National Science and Maths Quiz
2308.04333
https://arxiv.org/abs/2308.04333v1
https://arxiv.org/pdf/2308.04333v1.pdf
https://github.com/nsmq-ai/nsmqai
true
true
false
none
https://paperswithcode.com/paper/can-an-ai-win-ghana-s-national-science-and
Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education
2301.13089
https://arxiv.org/abs/2301.13089v1
https://arxiv.org/pdf/2301.13089v1.pdf
https://github.com/nsmq-ai/nsmqai
false
false
true
none
https://paperswithcode.com/paper/how-generalizable-are-deepfake-detectors-an
How Generalizable are Deepfake Image Detectors? An Empirical Study
2308.04177
https://arxiv.org/abs/2308.04177v2
https://arxiv.org/pdf/2308.04177v2.pdf
https://github.com/boutiquelee/deepfakeempiricalstudy
true
true
false
tf
https://paperswithcode.com/paper/xflow-benchmarking-flow-behaviors-over-graphs
XFlow: Benchmarking Flow Behaviors over Graphs
2308.03819
https://arxiv.org/abs/2308.03819v1
https://arxiv.org/pdf/2308.03819v1.pdf
https://github.com/xgraphing/xflow
true
true
true
pytorch
https://paperswithcode.com/paper/sok-evaluations-in-industrial-intrusion
SoK: Evaluations in Industrial Intrusion Detection Research
2311.02929
https://arxiv.org/abs/2311.02929v1
https://arxiv.org/pdf/2311.02929v1.pdf
https://github.com/fkie-cad/ipal_evaluate
true
true
false
none
https://paperswithcode.com/paper/delivering-document-conversion-as-a-cloud
Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness
2206.00785
https://arxiv.org/abs/2206.00785v1
https://arxiv.org/pdf/2206.00785v1.pdf
https://github.com/ds4sd/deepsearch-toolkit
false
false
true
none
https://paperswithcode.com/paper/neural-networks-for-programming-quantum
Neural Networks for Programming Quantum Annealers
2308.06807
https://arxiv.org/abs/2308.06807v1
https://arxiv.org/pdf/2308.06807v1.pdf
https://github.com/boschsamuel/nnforprogrammingquantumannealers
true
true
true
pytorch
https://paperswithcode.com/paper/hierarchy-flow-for-high-fidelity-image-to
Hierarchy Flow For High-Fidelity Image-to-Image Translation
2308.06909
https://arxiv.org/abs/2308.06909v1
https://arxiv.org/pdf/2308.06909v1.pdf
https://github.com/weichenfan/hierarchyflow
true
true
true
pytorch
https://paperswithcode.com/paper/global-features-are-all-you-need-for-image
Global Features are All You Need for Image Retrieval and Reranking
2308.06954
https://arxiv.org/abs/2308.06954v2
https://arxiv.org/pdf/2308.06954v2.pdf
https://github.com/shihaoshao-gh/superglobal
true
true
true
pytorch
https://paperswithcode.com/paper/the-importance-of-being-scalable-improving
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
2410.24169
https://arxiv.org/abs/2410.24169v1
https://arxiv.org/pdf/2410.24169v1.pdf
https://github.com/ASK-Berkeley/EScAIP
true
false
false
pytorch
https://paperswithcode.com/paper/bounding-and-estimating-mcmc-convergence
Estimating MCMC convergence rates using common random number simulation
2309.15735
https://arxiv.org/abs/2309.15735v3
https://arxiv.org/pdf/2309.15735v3.pdf
https://github.com/sixter/commonrandomnumber
true
true
false
none
https://paperswithcode.com/paper/powerset-multi-class-cross-entropy-loss-for
Powerset multi-class cross entropy loss for neural speaker diarization
2310.13025
https://arxiv.org/abs/2310.13025v1
https://arxiv.org/pdf/2310.13025v1.pdf
https://github.com/frenchkrab/is2023-powerset-diarization
true
true
false
none
https://paperswithcode.com/paper/speech-recognition-and-multi-speaker
Speech Recognition and Multi-Speaker Diarization of Long Conversations
2005.08072
https://arxiv.org/abs/2005.08072v2
https://arxiv.org/pdf/2005.08072v2.pdf
https://github.com/frenchkrab/is2023-powerset-diarization
false
false
true
none
https://paperswithcode.com/paper/ava-avd-audio-visual-speaker-diarization-in
AVA-AVD: Audio-Visual Speaker Diarization in the Wild
2111.14448
https://arxiv.org/abs/2111.14448v5
https://arxiv.org/pdf/2111.14448v5.pdf
https://github.com/frenchkrab/is2023-powerset-diarization
false
false
true
none
https://paperswithcode.com/paper/understanding-and-optimizing-deep-learning
Boosting DNN Cold Inference on Edge Devices
2206.07446
https://arxiv.org/abs/2206.07446v2
https://arxiv.org/pdf/2206.07446v2.pdf
https://github.com/ubiquitouslearning/nnv12
true
true
true
none
https://paperswithcode.com/paper/primitive-geometry-segment-pre-training-for
Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation
2401.03665
https://arxiv.org/abs/2401.03665v1
https://arxiv.org/pdf/2401.03665v1.pdf
https://github.com/super-tadory/primgeoseg
true
true
false
pytorch
https://paperswithcode.com/paper/nef-neural-edge-fields-for-3d-parametric
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images
2303.07653
https://arxiv.org/abs/2303.07653v2
https://arxiv.org/pdf/2303.07653v2.pdf
https://github.com/yunfan1202/NEF_code
true
false
true
pytorch
https://paperswithcode.com/paper/are-current-long-term-video-understanding
Are current long-term video understanding datasets long-term?
2308.11244
https://arxiv.org/abs/2308.11244v1
https://arxiv.org/pdf/2308.11244v1.pdf
https://github.com/ombretta/longterm_datasets
true
true
true
none
https://paperswithcode.com/paper/protein-dna-binding-sites-prediction-based-on
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning
2306.15912
https://arxiv.org/abs/2306.15912v1
https://arxiv.org/pdf/2306.15912v1.pdf
https://github.com/yandrewl/clape
true
true
true
pytorch
https://paperswithcode.com/paper/exposing-flaws-of-generative-model-evaluation-1
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
2306.04675
https://arxiv.org/abs/2306.04675v2
https://arxiv.org/pdf/2306.04675v2.pdf
https://github.com/gmum/PALATE
false
false
true
jax
https://paperswithcode.com/paper/coarse-to-fine-amodal-segmentation-with-shape
Coarse-to-Fine Amodal Segmentation with Shape Prior
2308.16825
https://arxiv.org/abs/2308.16825v1
https://arxiv.org/pdf/2308.16825v1.pdf
https://github.com/amazon-science/c2f-seg
true
false
true
pytorch
https://paperswithcode.com/paper/large-language-model-can-transcribe-speech-in
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
2409.08596
https://arxiv.org/abs/2409.08596v2
https://arxiv.org/pdf/2409.08596v2.pdf
https://github.com/cuhealthybrains/mt-llm
true
true
false
pytorch
https://paperswithcode.com/paper/communication-efficient-learning-of-deep
Communication-Efficient Learning of Deep Networks from Decentralized Data
1602.05629
https://arxiv.org/abs/1602.05629v4
https://arxiv.org/pdf/1602.05629v4.pdf
https://github.com/Lyhao0212/FedAvg
false
false
true
pytorch
https://paperswithcode.com/paper/ego4d-around-the-world-in-3000-hours-of
Ego4D: Around the World in 3,000 Hours of Egocentric Video
2110.07058
https://arxiv.org/abs/2110.07058v3
https://arxiv.org/pdf/2110.07058v3.pdf
https://github.com/frenchkrab/is2023-powerset-diarization
false
false
true
none
https://paperswithcode.com/paper/product-attribute-value-extraction-using
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction
2310.12537
https://arxiv.org/abs/2310.12537v5
https://arxiv.org/pdf/2310.12537v5.pdf
https://github.com/wbsg-uni-mannheim/extractgpt
true
true
true
none
https://paperswithcode.com/paper/voxelmorph-a-learning-framework-for
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
1809.05231
https://arxiv.org/abs/1809.05231v3
https://arxiv.org/pdf/1809.05231v3.pdf
https://github.com/jw4hv/geo-sic
false
false
true
pytorch
https://paperswithcode.com/paper/stratmed-relevance-stratification-for-low
StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation
2308.16781
https://arxiv.org/abs/2308.16781v4
https://arxiv.org/pdf/2308.16781v4.pdf
https://github.com/lixiang-222/drug-recommendations
true
true
true
pytorch
https://paperswithcode.com/paper/a-divergence-preserving-cut-finite-element
A divergence preserving cut finite element method for Darcy flow
2205.12023
https://arxiv.org/abs/2205.12023v8
https://arxiv.org/pdf/2205.12023v8.pdf
https://github.com/cutfem/cutfem-library
true
true
false
none
https://paperswithcode.com/paper/tensorshield-safeguarding-on-device-inference
TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE
2505.22735
https://arxiv.org/abs/2505.22735v1
https://arxiv.org/pdf/2505.22735v1.pdf
https://github.com/suntong30/tensorshield
true
true
false
pytorch
https://paperswithcode.com/paper/vistruct-visual-structural-knowledge
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation
2311.13258
https://arxiv.org/abs/2311.13258v1
https://arxiv.org/pdf/2311.13258v1.pdf
https://github.com/yangyi-chen/vi-struct
true
true
true
pytorch
https://paperswithcode.com/paper/the-third-dihard-diarization-challenge
The Third DIHARD Diarization Challenge
2012.01477
https://arxiv.org/abs/2012.01477v3
https://arxiv.org/pdf/2012.01477v3.pdf
https://github.com/frenchkrab/is2023-powerset-diarization
false
false
true
none
https://paperswithcode.com/paper/echomamba4rec-harmonizing-bidirectional-state
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation
2406.02638
https://arxiv.org/abs/2406.02638v2
https://arxiv.org/pdf/2406.02638v2.pdf
https://github.com/wyd0042/EchoMamba4Rec
true
false
false
pytorch
https://paperswithcode.com/paper/a-physical-model-guided-framework-for
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
2407.04230
https://arxiv.org/abs/2407.04230v1
https://arxiv.org/pdf/2407.04230v1.pdf
https://github.com/ddz16/UWEnhancer
true
false
true
pytorch
https://paperswithcode.com/paper/post-ocr-document-correction-with-large
Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models
2109.06264
https://arxiv.org/abs/2109.06264v3
https://arxiv.org/pdf/2109.06264v3.pdf
https://github.com/jarobyte91/post_ocr_correction
true
true
true
pytorch
https://paperswithcode.com/paper/distributed-representations-of-words-and-1
Distributed Representations of Words and Phrases and their Compositionality
1310.4546
http://arxiv.org/abs/1310.4546v1
http://arxiv.org/pdf/1310.4546v1.pdf
https://github.com/Robotmurlock/Deepwalk-and-Node2vec
false
false
true
pytorch
https://paperswithcode.com/paper/self-distillation-regularized-connectionist
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach
2308.08806
https://arxiv.org/abs/2308.08806v4
https://arxiv.org/pdf/2308.08806v4.pdf
https://github.com/zzyhlyoko/DCTC
true
true
true
pytorch
https://paperswithcode.com/paper/roboss-a-robust-bounded-sparse-and-smooth
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
2309.02250
https://arxiv.org/abs/2309.02250v1
https://arxiv.org/pdf/2309.02250v1.pdf
https://github.com/mtanveer1/RoBoSS
true
true
false
none
https://paperswithcode.com/paper/mask-of-truth-model-sensitivity-to-unexpected
Mask of truth: model sensitivity to unexpected regions of medical images
2412.04030
https://arxiv.org/abs/2412.04030v2
https://arxiv.org/pdf/2412.04030v2.pdf
https://github.com/theosourget/mmc_masking_eyefundus
true
true
false
pytorch
https://paperswithcode.com/paper/aggregating-capacity-in-fl-through-successive
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
2305.17005
https://arxiv.org/abs/2305.17005v2
https://arxiv.org/pdf/2305.17005v2.pdf
https://github.com/k1l1/SLT
true
true
false
pytorch
https://paperswithcode.com/paper/event-based-dynamic-graph-representation
Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
2308.09780
https://arxiv.org/abs/2308.09780v2
https://arxiv.org/pdf/2308.09780v2.pdf
https://github.com/Hope-Rita/EDGPAT
true
false
false
pytorch
https://paperswithcode.com/paper/graph-neural-networks-use-graphs-when-they
Graph Neural Networks Use Graphs When They Shouldn't
2309.04332
https://arxiv.org/abs/2309.04332v2
https://arxiv.org/pdf/2309.04332v2.pdf
https://github.com/mayabechlerspeicher/Graph_Neural_Networks_Overfit_Graphs
true
true
false
pytorch
https://paperswithcode.com/paper/how-susceptible-are-large-language-models-to
How Susceptible are Large Language Models to Ideological Manipulation?
2402.11725
https://arxiv.org/abs/2402.11725v3
https://arxiv.org/pdf/2402.11725v3.pdf
https://github.com/kaichen23/llm_ideo_manipulate
true
true
true
none
https://paperswithcode.com/paper/mxt-mamba-x-transformer-for-image-inpainting
MxT: Mamba x Transformer for Image Inpainting
2407.16126
https://arxiv.org/abs/2407.16126v3
https://arxiv.org/pdf/2407.16126v3.pdf
https://github.com/chrischen1023/mxt
true
true
false
pytorch
https://paperswithcode.com/paper/omnisearchsage-multi-task-multi-entity
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
2404.16260
https://arxiv.org/abs/2404.16260v1
https://arxiv.org/pdf/2404.16260v1.pdf
https://github.com/pinterest/atg-research
true
true
false
pytorch
https://paperswithcode.com/paper/item-graph2vec-a-efficient-and-effective
Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering
2310.14215
https://arxiv.org/abs/2310.14215v1
https://arxiv.org/pdf/2310.14215v1.pdf
https://github.com/cpu135/item-graph2vec
true
true
false
none
https://paperswithcode.com/paper/end-to-end-user-behavior-retrieval-in-click
End-to-End User Behavior Retrieval in Click-Through RatePrediction Model
2108.04468
https://arxiv.org/abs/2108.04468v1
https://arxiv.org/pdf/2108.04468v1.pdf
https://github.com/reczoo/FuxiCTR
false
false
true
pytorch
https://paperswithcode.com/paper/imface-a-sophisticated-nonlinear-3d-morphable
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
2312.04028
https://arxiv.org/abs/2312.04028v3
https://arxiv.org/pdf/2312.04028v3.pdf
https://github.com/mingwuzheng/imface
true
true
true
pytorch
https://paperswithcode.com/paper/deep-interest-network-for-click-through-rate
Deep Interest Network for Click-Through Rate Prediction
1706.06978
http://arxiv.org/abs/1706.06978v4
http://arxiv.org/pdf/1706.06978v4.pdf
https://github.com/reczoo/FuxiCTR
false
false
false
pytorch
https://paperswithcode.com/paper/affective-and-dynamic-beam-search-for-story
Affective and Dynamic Beam Search for Story Generation
2310.15079
https://arxiv.org/abs/2310.15079v1
https://arxiv.org/pdf/2310.15079v1.pdf
https://github.com/tenghaohuang/affgen
true
true
false
pytorch
https://paperswithcode.com/paper/impala-scalable-distributed-deep-rl-with
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
1802.01561
http://arxiv.org/abs/1802.01561v3
http://arxiv.org/pdf/1802.01561v3.pdf
https://github.com/seolhokim/SimpleDistributedRL
false
false
false
pytorch
https://paperswithcode.com/paper/seamless-integration-of-tactile-sensors-for
Seamless Integration of Tactile Sensors for Cobots
2309.05792
https://arxiv.org/abs/2309.05792v1
https://arxiv.org/pdf/2309.05792v1.pdf
https://github.com/remkopr/airo-halberd
true
true
false
none
https://paperswithcode.com/paper/conversational-recommender-system-and-large
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue
2310.14626
https://arxiv.org/abs/2310.14626v2
https://arxiv.org/pdf/2310.14626v2.pdf
https://github.com/leeeeoliu/llm-crs
true
true
false
pytorch
https://paperswithcode.com/paper/normdial-a-comparable-bilingual-synthetic
NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation
2310.14563
https://arxiv.org/abs/2310.14563v2
https://arxiv.org/pdf/2310.14563v2.pdf
https://github.com/aochong-li/normdial
true
true
true
none
https://paperswithcode.com/paper/qonfusion-quantum-approaches-to-gaussian
QonFusion -- Quantum Approaches to Gaussian Random Variables: Applications in Stable Diffusion and Brownian Motion
2309.16258
https://arxiv.org/abs/2309.16258v1
https://arxiv.org/pdf/2309.16258v1.pdf
https://github.com/BoltzmannEntropy/QonFusion
true
false
true
pytorch
https://paperswithcode.com/paper/w2v-bert-combining-contrastive-learning-and
W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
2108.06209
https://arxiv.org/abs/2108.06209v2
https://arxiv.org/pdf/2108.06209v2.pdf
https://github.com/wenet-e2e/wenet
false
false
false
pytorch
https://paperswithcode.com/paper/doclayout-yolo-enhancing-document-layout
DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
2410.12628
https://arxiv.org/abs/2410.12628v1
https://arxiv.org/pdf/2410.12628v1.pdf
https://github.com/opendatalab/PDF-Extract-Kit
false
false
true
pytorch
https://paperswithcode.com/paper/tree-prompting-efficient-task-adaptation
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
2310.14034
https://arxiv.org/abs/2310.14034v1
https://arxiv.org/pdf/2310.14034v1.pdf
https://github.com/csinva/tree-prompt
true
false
true
pytorch
https://paperswithcode.com/paper/identifiable-cognitive-diagnosis-with-encoder
Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
2309.00300
https://arxiv.org/abs/2309.00300v4
https://arxiv.org/pdf/2309.00300v4.pdf
https://github.com/cslijt/id-cdf
true
true
false
pytorch
https://paperswithcode.com/paper/the-convergence-of-stochastic-differential
The convergence of stochastic differential equations to their linearisation in small noise limits
2309.16334
https://arxiv.org/abs/2309.16334v2
https://arxiv.org/pdf/2309.16334v2.pdf
https://github.com/liamblake/explicit-characterisation-sde-linearisation
true
true
true
none
https://paperswithcode.com/paper/mapping-bias-in-vision-language-models
debiaSAE: Benchmarking and Mitigating Vision-Language Model Bias
2410.13146
https://arxiv.org/abs/2410.13146v2
https://arxiv.org/pdf/2410.13146v2.pdf
https://github.com/kuleens/vlmbiaseval
true
true
false
jax
https://paperswithcode.com/paper/accelerating-ilp-solvers-for-minimum-flow
Accelerating ILP solvers for Minimum Flow Decompositions through search space and dimensionality reductions
2311.10563
https://arxiv.org/abs/2311.10563v1
https://arxiv.org/pdf/2311.10563v1.pdf
https://github.com/algbio/optimized-fd
true
true
false
none
https://paperswithcode.com/paper/second-order-group-knockoffs-with
Second-order group knockoffs with applications to GWAS
2310.15069
https://arxiv.org/abs/2310.15069v2
https://arxiv.org/pdf/2310.15069v2.pdf
https://github.com/biona001/knockoffspy
true
true
true
none
https://paperswithcode.com/paper/visualwebbench-how-far-have-multimodal-llms
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
2404.05955
https://arxiv.org/abs/2404.05955v1
https://arxiv.org/pdf/2404.05955v1.pdf
https://github.com/visualwebbench/visualwebbench
false
false
true
pytorch
https://paperswithcode.com/paper/fully-differentiable-ransac
Generalized Differentiable RANSAC
2212.13185
https://arxiv.org/abs/2212.13185v3
https://arxiv.org/pdf/2212.13185v3.pdf
https://github.com/weitong8591/ars_magsac
false
false
true
pytorch
https://paperswithcode.com/paper/consensus-guided-correspondence-denoising
Progressive Correspondence Pruning by Consensus Learning
2101.00591
https://arxiv.org/abs/2101.00591v2
https://arxiv.org/pdf/2101.00591v2.pdf
https://github.com/weitong8591/ars_magsac
false
false
true
pytorch
https://paperswithcode.com/paper/risk-of-bias-in-chest-x-ray-foundation-models
Risk of Bias in Chest Radiography Deep Learning Foundation Models
2209.02965
https://arxiv.org/abs/2209.02965v3
https://arxiv.org/pdf/2209.02965v3.pdf
https://github.com/biomedia-mira/cxr-foundation-bias
true
true
false
pytorch
https://paperswithcode.com/paper/an-application-driven-method-for-assembling
An Application Driven Method for Assembling Numerical Schemes for the Solution of Complex Multiphysics Problems
2309.17055
https://arxiv.org/abs/2309.17055v2
https://arxiv.org/pdf/2309.17055v2.pdf
https://github.com/pzimbrod/multiphysics-pde-methods
true
true
false
none
https://paperswithcode.com/paper/alma-lensing-cluster-survey-average-dust-gas
ALMA Lensing Cluster Survey: average dust, gas, and star formation properties of cluster and field galaxies from stacking analysis
2309.16832
https://arxiv.org/abs/2309.16832v1
https://arxiv.org/pdf/2309.16832v1.pdf
https://github.com/guerrero-andrea/stacking_codes
true
true
true
none
https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
2010.11929
https://arxiv.org/abs/2010.11929v2
https://arxiv.org/pdf/2010.11929v2.pdf
https://github.com/rayanramoul/Visual-Transformer-PyTorch
false
false
true
pytorch
https://paperswithcode.com/paper/improved-anisotropic-gaussian-filters
Improved Anisotropic Gaussian Filters
2303.13278
https://arxiv.org/abs/2303.13278v2
https://arxiv.org/pdf/2303.13278v2.pdf
https://github.com/akeilmann/anigauss
true
true
true
none
https://paperswithcode.com/paper/the-partial-entropy-decomposition-decomposing
The Partial Entropy Decomposition: Decomposing multivariate entropy and mutual information via pointwise common surprisal
1702.01591
http://arxiv.org/abs/1702.01591v2
http://arxiv.org/pdf/1702.01591v2.pdf
https://github.com/robince/partial-info-decomp
true
true
true
none
https://paperswithcode.com/paper/measuring-multivariate-redundant-information
Measuring multivariate redundant information with pointwise common change in surprisal
1602.05063
http://arxiv.org/abs/1602.05063v3
http://arxiv.org/pdf/1602.05063v3.pdf
https://github.com/robince/partial-info-decomp
true
true
true
none
https://paperswithcode.com/paper/engineering-serendipity-through
Engineering Serendipity through Recommendations of Items with Atypical Aspects
2505.23580
https://arxiv.org/abs/2505.23580v1
https://arxiv.org/pdf/2505.23580v1.pdf
https://github.com/ramituncc49er/atars
true
true
true
none
https://paperswithcode.com/paper/object-aware-adaptive-positivity-learning-for
Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering
2312.12816
https://arxiv.org/abs/2312.12816v1
https://arxiv.org/pdf/2312.12816v1.pdf
https://github.com/zhangbin-ai/apl
true
true
true
pytorch
https://paperswithcode.com/paper/manipulation-robust-regression-discontinuity
Manipulation-Robust Regression Discontinuity Designs
2009.07551
https://arxiv.org/abs/2009.07551v7
https://arxiv.org/pdf/2009.07551v7.pdf
https://github.com/smasa11/rdtest
false
false
true
none
https://paperswithcode.com/paper/linear-recurrent-units-for-sequential
Linear Recurrent Units for Sequential Recommendation
2310.02367
https://arxiv.org/abs/2310.02367v2
https://arxiv.org/pdf/2310.02367v2.pdf
https://github.com/yueqirex/lrurec
true
true
true
pytorch
https://paperswithcode.com/paper/nearest-neighbor-machine-translation-is-meta
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer
2305.13034
https://arxiv.org/abs/2305.13034v2
https://arxiv.org/pdf/2305.13034v2.pdf
https://github.com/ruizgao/knnmt-meta-optimizer
true
true
false
pytorch
https://paperswithcode.com/paper/eden-multimodal-synthetic-dataset-of-enclosed
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes
2011.04389
https://arxiv.org/abs/2011.04389v2
https://arxiv.org/pdf/2011.04389v2.pdf
https://github.com/lhoangan/eden-generation
false
false
true
none
https://paperswithcode.com/paper/vision-language-pseudo-labels-for-single
Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
2310.15985
https://arxiv.org/abs/2310.15985v1
https://arxiv.org/pdf/2310.15985v1.pdf
https://github.com/mvrl/vlpl
true
true
false
pytorch
https://paperswithcode.com/paper/rrhf-v-ranking-responses-to-mitigate
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback
null
https://aclanthology.org/2025.coling-main.454/
https://aclanthology.org/2025.coling-main.454.pdf
https://github.com/MindSpore-scientific-2/code-7/tree/main/RRHF-V_mindformers
false
false
false
mindspore
https://paperswithcode.com/paper/pruning-filters-for-efficient-convnets
Pruning Filters for Efficient ConvNets
1608.08710
http://arxiv.org/abs/1608.08710v3
http://arxiv.org/pdf/1608.08710v3.pdf
https://github.com/VainF/Torch-Pruning
false
false
true
pytorch
https://paperswithcode.com/paper/few-shot-generative-model-adaption-via
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
2203.04121
https://arxiv.org/abs/2203.04121v3
https://arxiv.org/pdf/2203.04121v3.pdf
https://github.com/2023-MindSpore-4/Code12/tree/main/liliang/RSSA_mds-main
false
false
false
mindspore
https://paperswithcode.com/paper/quark-controllable-text-generation-with
Quark: Controllable Text Generation with Reinforced Unlearning
2205.13636
https://arxiv.org/abs/2205.13636v2
https://arxiv.org/pdf/2205.13636v2.pdf
https://github.com/gximinglu/quark
true
true
true
pytorch
https://paperswithcode.com/paper/fingerspelling-recognition-in-the-wild-with
Fingerspelling recognition in the wild with iterative visual attention
1908.10546
https://arxiv.org/abs/1908.10546v1
https://arxiv.org/pdf/1908.10546v1.pdf
https://github.com/fmahoudeau/MiCT-RANet-ASL-FingerSpelling
false
false
true
pytorch
https://paperswithcode.com/paper/improving-fairness-of-graph-neural-networks-a
Towards Fair Graph Neural Networks via Graph Counterfactual
2307.04937
https://arxiv.org/abs/2307.04937v2
https://arxiv.org/pdf/2307.04937v2.pdf
https://github.com/timelovercc/caf-gnn
true
true
true
pytorch
https://paperswithcode.com/paper/quantum-clustering-and-gaussian-mixtures
Quantum Clustering and Gaussian Mixtures
1612.09199
http://arxiv.org/abs/1612.09199v1
http://arxiv.org/pdf/1612.09199v1.pdf
https://github.com/mrpintime/Quantum_Gaussian_Mixtures_Clustering
false
false
true
none
https://paperswithcode.com/paper/revisiting-semidefinite-programming
Revisiting semidefinite programming approaches to options pricing: complexity and computational perspectives
2111.07701
https://arxiv.org/abs/2111.07701v3
https://arxiv.org/pdf/2111.07701v3.pdf
https://github.com/informsjoc/2022.0328
false
false
true
none
https://paperswithcode.com/paper/out-of-distribution-detection-by-leveraging
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
2310.02832
https://arxiv.org/abs/2310.02832v2
https://arxiv.org/pdf/2310.02832v2.pdf
https://github.com/fjelenic/between-layer-ood
true
true
true
pytorch
https://paperswithcode.com/paper/docreal-robust-document-dewarping-of-real
DocReal: Robust Document Dewarping of Real-Life Images via Attention-Enhanced Control Point Prediction
null
https://openaccess.thecvf.com/content/WACV2024/html/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.html
https://openaccess.thecvf.com/content/WACV2024/papers/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.pdf
https://github.com/SciYu/DocReal
false
false
false
none
https://paperswithcode.com/paper/stella-nera-achieving-161-top-s-w-with
Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication
2311.10207
https://arxiv.org/abs/2311.10207v1
https://arxiv.org/pdf/2311.10207v1.pdf
https://github.com/joennlae/halutmatmul
true
true
true
pytorch
https://paperswithcode.com/paper/sums-of-hurwitz-class-numbers-cm-modular
Sums of Hurwitz class numbers, CM modular forms, and primes of the form $x^2+ny^2$
2405.07565
https://arxiv.org/abs/2405.07565v1
https://arxiv.org/pdf/2405.07565v1.pdf
https://github.com/zinmik/Hurwitz-class-numbers
true
true
false
none