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

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/asynchronous-batch-bayesian-optimization-with
Asynchronous Batch Bayesian Optimization with Pipelining Evaluations for Experimental Resource$\unicode{x2013}$constrained Conditions
2412.04392
https://arxiv.org/abs/2412.04392v1
https://arxiv.org/pdf/2412.04392v1.pdf
https://github.com/funalab/pipebo
true
true
true
none
https://paperswithcode.com/paper/naraim-native-aspect-ratio-autoregressive
NARAIM: Native Aspect Ratio Autoregressive Image Models
2410.10012
https://arxiv.org/abs/2410.10012v2
https://arxiv.org/pdf/2410.10012v2.pdf
https://github.com/daniel-gallo/naraim
true
true
false
jax
https://paperswithcode.com/paper/fastflow-in-fpga-stacks-of-data-centers
FastFlow in FPGA Stacks of Data Centers
2409.20099
https://arxiv.org/abs/2409.20099v1
https://arxiv.org/pdf/2409.20099v1.pdf
https://github.com/rourabpaul1986/fastflow_fpga_stacks
true
false
false
none
https://paperswithcode.com/paper/fast-and-accurate-model-scaling
Fast and Accurate Model Scaling
2103.06877
https://arxiv.org/abs/2103.06877v1
https://arxiv.org/pdf/2103.06877v1.pdf
https://github.com/edificewang/imageclassification
false
false
true
pytorch
https://paperswithcode.com/paper/designing-network-design-spaces
Designing Network Design Spaces
2003.13678
https://arxiv.org/abs/2003.13678v1
https://arxiv.org/pdf/2003.13678v1.pdf
https://github.com/edificewang/imageclassification
false
false
true
pytorch
https://paperswithcode.com/paper/agnostic-federated-learning
Agnostic Federated Learning
1902.00146
http://arxiv.org/abs/1902.00146v1
http://arxiv.org/pdf/1902.00146v1.pdf
https://github.com/vaseline555/aaggff
false
false
true
pytorch
https://paperswithcode.com/paper/disaggregated-multi-tower-topology-aware
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation
2403.00877
https://arxiv.org/abs/2403.00877v3
https://arxiv.org/pdf/2403.00877v3.pdf
https://github.com/facebookresearch/torchrec
false
false
true
pytorch
https://paperswithcode.com/paper/dissecting-payload-based-transaction-phishing
Dissecting Payload-based Transaction Phishing on Ethereum
2409.02386
https://arxiv.org/abs/2409.02386v2
https://arxiv.org/pdf/2409.02386v2.pdf
https://github.com/HypoopyH/PTXPhish
true
false
false
none
https://paperswithcode.com/paper/optimal-algorithms-for-smooth-and-strongly
Optimal algorithms for smooth and strongly convex distributed optimization in networks
1702.08704
http://arxiv.org/abs/1702.08704v2
http://arxiv.org/pdf/1702.08704v2.pdf
https://github.com/adelnabli/dadao
false
false
true
pytorch
https://paperswithcode.com/paper/a-continuized-view-on-nesterov-acceleration-1
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
2106.07644
https://arxiv.org/abs/2106.07644v2
https://arxiv.org/pdf/2106.07644v2.pdf
https://github.com/adelnabli/dadao
false
false
true
pytorch
https://paperswithcode.com/paper/xiyan-sql-a-novel-multi-generator-framework
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
2507.04701
https://arxiv.org/abs/2507.04701v1
https://arxiv.org/pdf/2507.04701v1.pdf
https://github.com/xgenerationlab/xiyan-dbdescgen
false
false
true
none
https://paperswithcode.com/paper/xiyan-sql-a-multi-generator-ensemble
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
2411.08599
https://arxiv.org/abs/2411.08599v3
https://arxiv.org/pdf/2411.08599v3.pdf
https://github.com/xgenerationlab/xiyan-dbdescgen
false
false
true
none
https://paperswithcode.com/paper/ph-dropout-prctical-epistemic-uncertainty
PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis
2410.05468
https://arxiv.org/abs/2410.05468v2
https://arxiv.org/pdf/2410.05468v2.pdf
https://github.com/thanostriantafyllou3/ph-dropout
true
true
true
none
https://paperswithcode.com/paper/optimizing-edge-offloading-decisions-for
Optimizing Edge Offloading Decisions for Object Detection
2410.18919
https://arxiv.org/abs/2410.18919v1
https://arxiv.org/pdf/2410.18919v1.pdf
https://github.com/rickywrq/Progressive-Neural-Compression
false
false
true
tf
https://paperswithcode.com/paper/variational-bayesian-bow-tie-neural-networks
Variational Bayesian Bow tie Neural Networks with Shrinkage
2411.11132
https://arxiv.org/abs/2411.11132v3
https://arxiv.org/pdf/2411.11132v3.pdf
https://github.com/sheinkmana/V_bowtie_NN
true
false
true
jax
https://paperswithcode.com/paper/sparse-approximate-cross-validation-for-high
Approximate Cross-Validation in High Dimensions with Guarantees
1905.13657
https://arxiv.org/abs/1905.13657v4
https://arxiv.org/pdf/1905.13657v4.pdf
https://bitbucket.org/wtstephe/sparse_appx_cv
true
true
false
none
https://paperswithcode.com/paper/generate-novel-and-robust-samples-from-data
Generate synthetic samples from tabular data
2209.06113
https://arxiv.org/abs/2209.06113v2
https://arxiv.org/pdf/2209.06113v2.pdf
https://github.com/askexplain/sampling4sharing
true
true
true
none
https://paperswithcode.com/paper/an-instrumental-variables-framework-to-unite
An Instrumental Variables Framework to Unite Spatial Confounding Methods
2411.10381
https://arxiv.org/abs/2411.10381v2
https://arxiv.org/pdf/2411.10381v2.pdf
https://github.com/NSAPH-Projects/spatial-scale-confounding
true
false
false
none
https://paperswithcode.com/paper/learning-high-frequency-functions-made-easy
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding
2407.09370
https://arxiv.org/abs/2407.09370v2
https://arxiv.org/pdf/2407.09370v2.pdf
https://github.com/zhyuan11/SPE
true
true
true
pytorch
https://paperswithcode.com/paper/scalable-learned-model-soup-on-a-single-gpu
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy
2407.03641
https://arxiv.org/abs/2407.03641v2
https://arxiv.org/pdf/2407.03641v2.pdf
https://github.com/nblt/mehl-soup
true
true
true
pytorch
https://paperswithcode.com/paper/crossnorm-normalization-for-off-policy-td
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
1902.05605
https://arxiv.org/abs/1902.05605v4
https://arxiv.org/pdf/1902.05605v4.pdf
https://github.com/DLR-RM/stable-baselines3
false
false
false
pytorch
https://paperswithcode.com/paper/reconciling-kaplan-and-chinchilla-scaling
Reconciling Kaplan and Chinchilla Scaling Laws
2406.12907
https://arxiv.org/abs/2406.12907v3
https://arxiv.org/pdf/2406.12907v3.pdf
https://github.com/teapearce/reconciling_kaplan_chinchilla_scaling_laws
true
true
true
none
https://paperswithcode.com/paper/map-it-anywhere-mia-empowering-bird-s-eye
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
2407.08726
https://arxiv.org/abs/2407.08726v2
https://arxiv.org/pdf/2407.08726v2.pdf
https://github.com/MapItAnywhere/MapItAnywhere
false
false
true
pytorch
https://paperswithcode.com/paper/few-shot-novel-category-discovery
Few-shot Novel Category Discovery
2505.08260
https://arxiv.org/abs/2505.08260v1
https://arxiv.org/pdf/2505.08260v1.pdf
https://github.com/ashengl/fsncd
true
true
true
pytorch
https://paperswithcode.com/paper/many-objective-evolutionary-influence
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
2403.18755
https://arxiv.org/abs/2403.18755v2
https://arxiv.org/pdf/2403.18755v2.pdf
https://github.com/eliacunegatti/moeim
true
true
true
none
https://paperswithcode.com/paper/self-supervised-learning-with-random
Self-supervised Learning with Random-projection Quantizer for Speech Recognition
2202.01855
https://arxiv.org/abs/2202.01855v2
https://arxiv.org/pdf/2202.01855v2.pdf
https://github.com/lucidrains/vector-quantize-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/finite-scalar-quantization-vq-vae-made-simple
Finite Scalar Quantization: VQ-VAE Made Simple
2309.15505
https://arxiv.org/abs/2309.15505v2
https://arxiv.org/pdf/2309.15505v2.pdf
https://github.com/lucidrains/vector-quantize-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/transparent-networks-for-multivariate-time
Transparent Networks for Multivariate Time Series
2410.10535
https://arxiv.org/abs/2410.10535v2
https://arxiv.org/pdf/2410.10535v2.pdf
https://github.com/gim4855744/gatsm
true
true
true
pytorch
https://paperswithcode.com/paper/prompt-engineering-and-its-implications-on
Prompt engineering and its implications on the energy consumption of Large Language Models
2501.05899
https://arxiv.org/abs/2501.05899v1
https://arxiv.org/pdf/2501.05899v1.pdf
https://github.com/riccardoRubei/Greens-2025-Replication-Package
true
true
false
pytorch
https://paperswithcode.com/paper/multi3hate-multimodal-multilingual-and
Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision-Language Models
2411.03888
https://arxiv.org/abs/2411.03888v1
https://arxiv.org/pdf/2411.03888v1.pdf
https://github.com/minhducbui/multi3hate
true
true
false
pytorch
https://paperswithcode.com/paper/leveraging-segment-anything-model-for-source
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
2505.08527
https://arxiv.org/abs/2505.08527v2
https://arxiv.org/pdf/2505.08527v2.pdf
https://github.com/xmed-lab/dfg
true
true
true
pytorch
https://paperswithcode.com/paper/context-aware-pseudo-label-refinement-for
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
2308.07731
https://arxiv.org/abs/2308.07731v1
https://arxiv.org/pdf/2308.07731v1.pdf
https://github.com/xmed-lab/dfg
false
false
true
pytorch
https://paperswithcode.com/paper/semeval-2024-task-3-multimodal-emotion-cause
SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
2405.13049
https://arxiv.org/abs/2405.13049v3
https://arxiv.org/pdf/2405.13049v3.pdf
https://github.com/nustm/semeval-2024_ecac
true
true
true
none
https://paperswithcode.com/paper/multi-label-logo-recognition-and-retrieval
Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
2205.05419
https://arxiv.org/abs/2205.05419v2
https://arxiv.org/pdf/2205.05419v2.pdf
https://github.com/pertusa/multilabelLogoRecognition
false
false
false
tf
https://paperswithcode.com/paper/segic-unleashing-the-emergent-correspondence
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
2311.14671
https://arxiv.org/abs/2311.14671v3
https://arxiv.org/pdf/2311.14671v3.pdf
https://github.com/menglcool/segic
true
true
true
pytorch
https://paperswithcode.com/paper/educoder-an-open-source-annotation-system-for
EduCoder: An Open-Source Annotation System for Education Transcript Data
2507.05385
https://arxiv.org/abs/2507.05385v1
https://arxiv.org/pdf/2507.05385v1.pdf
https://github.com/ArthurP-351/EduCoder
true
false
false
none
https://paperswithcode.com/paper/agl-net-aerial-ground-cross-modal-global
AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales
2404.03187
https://arxiv.org/abs/2404.03187v2
https://arxiv.org/pdf/2404.03187v2.pdf
https://github.com/rayguan97/agl-net
true
true
true
pytorch
https://paperswithcode.com/paper/privacy-enhanced-data-sharing-systems-from
Privacy-Enhanced Data Sharing Systems from Hierarchical ID-Based Puncturable Functional Encryption with Inner Product Predicates
null
https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/2024/5535196
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5535196
https://github.com/chengyi-chris/HIBP-IPFE
false
false
false
none
https://paperswithcode.com/paper/unpacking-sdxl-turbo-interpreting-text-to
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
2410.22366
https://arxiv.org/abs/2410.22366v2
https://arxiv.org/pdf/2410.22366v2.pdf
https://github.com/surkovv/sdxl-unbox
true
true
true
pytorch
https://paperswithcode.com/paper/aiscivision-a-framework-for-specializing
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification
2410.21480
https://arxiv.org/abs/2410.21480v1
https://arxiv.org/pdf/2410.21480v1.pdf
https://github.com/gomes-lab/AiSciVision
true
false
true
pytorch
https://paperswithcode.com/paper/dynamic-pricing-for-the-open-online-ticket
Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach
null
https://www.mdpi.com/2624-6511/6/3/63
https://www.mdpi.com/2624-6511/6/3/63/pdf?version=1683688086
https://github.com/AlgoMathITMO/DPRank
false
true
false
none
https://paperswithcode.com/paper/separation-of-periodic-orbits-in-the-delay
Separation of periodic orbits in the delay embedded space of chaotic attractors
2411.13103
https://arxiv.org/abs/2411.13103v1
https://arxiv.org/pdf/2411.13103v1.pdf
https://github.com/prernampatil/unstableperiodicorbits
true
true
true
none
https://paperswithcode.com/paper/metaheuristics-for-the-online-printing-shop
Metaheuristics for the Online Printing Shop Scheduling Problem
2006.12344
https://arxiv.org/abs/2006.12344v2
https://arxiv.org/pdf/2006.12344v2.pdf
https://github.com/willtl/online-printing-shop
true
true
true
none
https://paperswithcode.com/paper/predicting-temperatures-in-brazilian-states
Predicting temperatures in Brazilian states capitals via Machine Learning
2505.11511
https://arxiv.org/abs/2505.11511v1
https://arxiv.org/pdf/2505.11511v1.pdf
https://github.com/ecgabrick/braziltempforecasting
true
true
true
none
https://paperswithcode.com/paper/learning-the-trading-algorithm-in-simulated
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market
2208.02901
https://arxiv.org/abs/2208.02901v3
https://arxiv.org/pdf/2208.02901v3.pdf
https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation
true
true
false
none
https://paperswithcode.com/paper/general-multi-label-image-classification-with
General Multi-label Image Classification with Transformers
2011.14027
https://arxiv.org/abs/2011.14027v1
https://arxiv.org/pdf/2011.14027v1.pdf
https://github.com/QData/C-Tran
true
false
true
pytorch
https://paperswithcode.com/paper/retinexformer-one-stage-retinex-based
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
2303.06705
https://arxiv.org/abs/2303.06705v3
https://arxiv.org/pdf/2303.06705v3.pdf
https://github.com/dawnlh/awesome-low-light-image-enhancement
false
false
true
pytorch
https://paperswithcode.com/paper/learning-on-llm-output-signatures-for-gray
Learning on LLM Output Signatures for gray-box LLM Behavior Analysis
2503.14043
https://arxiv.org/abs/2503.14043v1
https://arxiv.org/pdf/2503.14043v1.pdf
https://github.com/barsguy/llm-output-signatures-network
true
true
false
pytorch
https://paperswithcode.com/paper/piast-a-multimodal-piano-dataset-with-audio
PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
2411.02551
https://arxiv.org/abs/2411.02551v2
https://arxiv.org/pdf/2411.02551v2.pdf
https://github.com/Hayeonbang/PIAST
true
true
true
none
https://paperswithcode.com/paper/can-language-models-learn-to-skip-steps
Can Language Models Learn to Skip Steps?
2411.01855
https://arxiv.org/abs/2411.01855v1
https://arxiv.org/pdf/2411.01855v1.pdf
https://github.com/tengxiaoliu/LM_skip
true
true
true
pytorch
https://paperswithcode.com/paper/dptdr-deep-prompt-tuning-for-dense-passage
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
2208.11503
https://arxiv.org/abs/2208.11503v1
https://arxiv.org/pdf/2208.11503v1.pdf
https://github.com/MindSpore-scientific/code-7/tree/main/DPT
false
false
false
none
https://paperswithcode.com/paper/learning-transferable-visual-models-from
Learning Transferable Visual Models From Natural Language Supervision
2103.00020
https://arxiv.org/abs/2103.00020v1
https://arxiv.org/pdf/2103.00020v1.pdf
https://github.com/borisdayma/clip-jax
false
false
true
jax
https://paperswithcode.com/paper/sigmoid-loss-for-language-image-pre-training
Sigmoid Loss for Language Image Pre-Training
2303.15343
https://arxiv.org/abs/2303.15343v4
https://arxiv.org/pdf/2303.15343v4.pdf
https://github.com/borisdayma/clip-jax
false
false
true
jax
https://paperswithcode.com/paper/image-captioners-are-scalable-vision-learners
Image Captioners Are Scalable Vision Learners Too
2306.07915
https://arxiv.org/abs/2306.07915v5
https://arxiv.org/pdf/2306.07915v5.pdf
https://github.com/borisdayma/clip-jax
false
false
true
jax
https://paperswithcode.com/paper/vision-transformers-need-registers
Vision Transformers Need Registers
2309.16588
https://arxiv.org/abs/2309.16588v2
https://arxiv.org/pdf/2309.16588v2.pdf
https://github.com/borisdayma/clip-jax
false
false
true
jax
https://paperswithcode.com/paper/unet-a-nested-u-net-architecture-for-medical
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
1807.10165
http://arxiv.org/abs/1807.10165v1
http://arxiv.org/pdf/1807.10165v1.pdf
https://github.com/qubvel/segmentation_models.pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/scalable-inference-with-autoregressive-neural
Scalable inference with Autoregressive Neural Ratio Estimation
2308.08597
https://arxiv.org/abs/2308.08597v2
https://arxiv.org/pdf/2308.08597v2.pdf
https://github.com/undark-lab/swyft
true
true
false
pytorch
https://paperswithcode.com/paper/rest-hands-rehabilitation-with-egocentric
REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke
2409.20116
https://arxiv.org/abs/2409.20116v1
https://arxiv.org/pdf/2409.20116v1.pdf
https://github.com/wiktormucha/rest-hands
true
true
true
pytorch
https://paperswithcode.com/paper/reducing-inference-energy-consumption-using
Reducing Inference Energy Consumption Using Dual Complementary CNNs
2412.01039
https://arxiv.org/abs/2412.01039v2
https://arxiv.org/pdf/2412.01039v2.pdf
https://github.com/michaelkinnas/Reducing-Inference-Energy-Consumption-Using-Two-Complementary-CNNs
true
false
false
pytorch
https://paperswithcode.com/paper/accio-table-understanding-enhanced-via
ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
2411.04443
https://arxiv.org/abs/2411.04443v1
https://arxiv.org/pdf/2411.04443v1.pdf
https://github.com/whnhch/accio
true
true
false
pytorch
https://paperswithcode.com/paper/robust-bayesian-graphical-regression-models
Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks
2310.18474
https://arxiv.org/abs/2310.18474v1
https://arxiv.org/pdf/2310.18474v1.pdf
https://github.com/bayesrx/rbgr
true
true
true
none
https://paperswithcode.com/paper/a-generative-model-for-gaia-astrometric-orbit
A generative model for Gaia astrometric orbit catalogs: selection functions for binary stars, giant planets, and compact object companions
2411.00088
https://arxiv.org/abs/2411.00088v1
https://arxiv.org/pdf/2411.00088v1.pdf
https://github.com/kareemelbadry/gaiamock
true
true
false
none
https://paperswithcode.com/paper/videomae-masked-autoencoders-are-data-1
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
2203.12602
https://arxiv.org/abs/2203.12602v3
https://arxiv.org/pdf/2203.12602v3.pdf
https://github.com/MindSpore-scientific/code-13/tree/main/token_learner
false
false
false
mindspore
https://paperswithcode.com/paper/improving-decision-sparsity
Improving Decision Sparsity
2410.20483
https://arxiv.org/abs/2410.20483v2
https://arxiv.org/pdf/2410.20483v2.pdf
https://github.com/williamsyy/multiplesev
true
true
false
pytorch
https://paperswithcode.com/paper/accuracy-of-a-vision-language-model-on
Multimodal Foundation Models Exploit Text to Make Medical Image Predictions
2311.05591
https://arxiv.org/abs/2311.05591v2
https://arxiv.org/pdf/2311.05591v2.pdf
https://github.com/2v/lmm-text-image
true
true
false
none
https://paperswithcode.com/paper/when-dataflow-analysis-meets-large-language
LLMDFA: Analyzing Dataflow in Code with Large Language Models
2402.10754
https://arxiv.org/abs/2402.10754v2
https://arxiv.org/pdf/2402.10754v2.pdf
https://github.com/chengpeng-wang/llmdfa
true
true
false
none
https://paperswithcode.com/paper/3dg-a-framework-for-using-generative-ai-for
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
2402.01746
https://arxiv.org/abs/2402.01746v1
https://arxiv.org/pdf/2402.01746v1.pdf
https://github.com/liangzhang2017/3dgai
false
false
true
none
https://paperswithcode.com/paper/the-search-for-stability-learning-dynamics-of
The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
2305.16695
https://arxiv.org/abs/2305.16695v5
https://arxiv.org/pdf/2305.16695v5.pdf
https://github.com/ireinman/the-search-for-stability
true
true
false
none
https://paperswithcode.com/paper/data-augmentation-for-sparse-multidimensional
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
2409.15631
https://arxiv.org/abs/2409.15631v3
https://arxiv.org/pdf/2409.15631v3.pdf
https://github.com/liangzhang2017/3dgai
true
true
true
none
https://paperswithcode.com/paper/haar-laplacian-for-directed-graphs
Haar-Laplacian for directed graphs
2411.15527
https://arxiv.org/abs/2411.15527v1
https://arxiv.org/pdf/2411.15527v1.pdf
https://github.com/theodorbadea/Haar-Laplacian
true
false
true
pytorch
https://paperswithcode.com/paper/mambacsr-dual-interleaved-scanning-for
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
2408.11758
https://arxiv.org/abs/2408.11758v2
https://arxiv.org/pdf/2408.11758v2.pdf
https://github.com/renyulin-f/mambacsr
true
true
true
pytorch
https://paperswithcode.com/paper/distractor-free-generalizable-3d-gaussian
Distractor-free Generalizable 3D Gaussian Splatting
2411.17605
https://arxiv.org/abs/2411.17605v1
https://arxiv.org/pdf/2411.17605v1.pdf
https://github.com/bbbbby-99/dggs
true
true
false
none
https://paperswithcode.com/paper/a-tool-for-generating-exceptional-behavior
A Tool for Generating Exceptional Behavior Tests With Large Language Models
2505.22818
https://arxiv.org/abs/2505.22818v1
https://arxiv.org/pdf/2505.22818v1.pdf
https://github.com/engineeringsoftware/exlong
true
true
true
none
https://paperswithcode.com/paper/mis-information-diffusion-and-the-financial
(Mis)information diffusion and the financial market
2412.16269
https://arxiv.org/abs/2412.16269v1
https://arxiv.org/pdf/2412.16269v1.pdf
https://github.com/danieltorren/misinformation_financial_markets
true
true
false
none
https://paperswithcode.com/paper/attamba-attending-to-multi-token-states
Attamba: Attending To Multi-Token States
2411.17685
https://arxiv.org/abs/2411.17685v1
https://arxiv.org/pdf/2411.17685v1.pdf
https://github.com/abdelfattah-lab/attamba
true
false
true
pytorch
https://paperswithcode.com/paper/droid-splat-combining-end-to-end-slam-with-3d
DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
2411.17660
https://arxiv.org/abs/2411.17660v2
https://arxiv.org/pdf/2411.17660v2.pdf
https://github.com/chenhoy/droid-splat
true
true
true
pytorch
https://paperswithcode.com/paper/searching-latent-program-spaces
Searching Latent Program Spaces
2411.08706
https://arxiv.org/abs/2411.08706v1
https://arxiv.org/pdf/2411.08706v1.pdf
https://github.com/clement-bonnet/lpn
true
true
true
jax
https://paperswithcode.com/paper/risk-aware-trading-portfolio-optimization
Risk-aware Trading Portfolio Optimization
2503.04662
https://arxiv.org/abs/2503.04662v1
https://arxiv.org/pdf/2503.04662v1.pdf
https://github.com/arnabdey929/Risk-Aware-Trading-Portfolio-Optimisation
false
false
true
none
https://paperswithcode.com/paper/semiparametric-counterfactual-regression
Semiparametric Counterfactual Regression
2504.02694
https://arxiv.org/abs/2504.02694v2
https://arxiv.org/pdf/2504.02694v2.pdf
https://github.com/kwangho-joshua-kim/counterfactual-prediction
true
false
true
none
https://paperswithcode.com/paper/un-detr-promoting-objectness-learning-via
UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection
2412.10176
https://arxiv.org/abs/2412.10176v1
https://arxiv.org/pdf/2412.10176v1.pdf
https://github.com/ndwxhmzz/un-detr
true
true
false
pytorch
https://paperswithcode.com/paper/finding-reproducible-and-prognostic-radiomic
Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
2501.11221
https://arxiv.org/abs/2501.11221v1
https://arxiv.org/pdf/2501.11221v1.pdf
https://github.com/jpeoples/melba2024
true
true
false
none
https://paperswithcode.com/paper/towards-satellite-image-road-graph-extraction
Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method
2411.16733
https://arxiv.org/abs/2411.16733v1
https://arxiv.org/pdf/2411.16733v1.pdf
https://github.com/earth-insights/samroadplus
true
true
true
pytorch
https://paperswithcode.com/paper/a-simple-framework-for-contrastive-learning
A Simple Framework for Contrastive Learning of Visual Representations
2002.05709
https://arxiv.org/abs/2002.05709v3
https://arxiv.org/pdf/2002.05709v3.pdf
https://github.com/mandiehyewon/goodviews_ecg
false
false
true
pytorch
https://paperswithcode.com/paper/segment-based-attention-masking-for-gpts
Segment-Based Attention Masking for GPTs
2412.18487
https://arxiv.org/abs/2412.18487v1
https://arxiv.org/pdf/2412.18487v1.pdf
https://github.com/shacharKZ/MAS-Segment-Based-Attention-Masking
true
false
false
pytorch
https://paperswithcode.com/paper/snake-inspired-mobile-robot-positioning-with
Snake-Inspired Mobile Robot Positioning with Hybrid Learning
2411.17430
https://arxiv.org/abs/2411.17430v2
https://arxiv.org/pdf/2411.17430v2.pdf
https://github.com/ansfl/MoRPINet
true
false
true
pytorch
https://paperswithcode.com/paper/on-turbulence-for-spacetimes-with-stable
On turbulence for spacetimes with stable trapping
2411.17445
https://arxiv.org/abs/2411.17445v2
https://arxiv.org/pdf/2411.17445v2.pdf
https://github.com/alejandroc137/ScalarWaveEvolution
true
false
true
tf
https://paperswithcode.com/paper/can-large-language-models-reason-about-the
Can Large Language Models Reason about the Region Connection Calculus?
2411.19589
https://arxiv.org/abs/2411.19589v1
https://arxiv.org/pdf/2411.19589v1.pdf
https://github.com/RobBlackwell/can-llms-reason-about-the-rcc
true
false
true
none
https://paperswithcode.com/paper/cor-gs-sparse-view-3d-gaussian-splatting-via
CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization
2405.12110
https://arxiv.org/abs/2405.12110v2
https://arxiv.org/pdf/2405.12110v2.pdf
https://github.com/jiaw-z/CoR-GS
true
false
true
pytorch
https://paperswithcode.com/paper/sadg-segment-any-dynamic-gaussian-without
SADG: Segment Any Dynamic Gaussian Without Object Trackers
2411.19290
https://arxiv.org/abs/2411.19290v1
https://arxiv.org/pdf/2411.19290v1.pdf
https://github.com/yunjinli/SADG-SegmentAnyDynamicGaussian
true
false
true
pytorch
https://paperswithcode.com/paper/verifastscore-speeding-up-long-form
VeriFastScore: Speeding up long-form factuality evaluation
2505.16973
https://arxiv.org/abs/2505.16973v1
https://arxiv.org/pdf/2505.16973v1.pdf
https://github.com/rishanthrajendhran/verifastscore
true
true
true
pytorch
https://paperswithcode.com/paper/multi-flow-multi-view-enriched-normalizing
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
2504.03306
https://arxiv.org/abs/2504.03306v1
https://arxiv.org/pdf/2504.03306v1.pdf
https://github.com/m-kruse98/multi-flow
true
true
true
pytorch
https://paperswithcode.com/paper/fair-generalized-linear-models-with-a-convex
Fair Generalized Linear Models with a Convex Penalty
2206.09076
https://arxiv.org/abs/2206.09076v1
https://arxiv.org/pdf/2206.09076v1.pdf
https://github.com/hyungrok-do/fair-glm-cvx
true
true
true
tf
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/hyungrok-do/fair-glm-cvx
false
false
true
tf
https://paperswithcode.com/paper/a-convex-framework-for-fair-regression
A Convex Framework for Fair Regression
1706.02409
http://arxiv.org/abs/1706.02409v1
http://arxiv.org/pdf/1706.02409v1.pdf
https://github.com/hyungrok-do/fair-glm-cvx
false
false
true
tf
https://paperswithcode.com/paper/disaggregated-multi-tower-topology-aware
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation
2403.00877
https://arxiv.org/abs/2403.00877v3
https://arxiv.org/pdf/2403.00877v3.pdf
https://github.com/pytorch/torchrec
false
false
true
pytorch
https://paperswithcode.com/paper/ifedrec-item-guided-federated-aggregation-for
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
2305.12650
https://arxiv.org/abs/2305.12650v2
https://arxiv.org/pdf/2305.12650v2.pdf
https://github.com/zhangcx19/ifedrec
true
true
true
pytorch
https://paperswithcode.com/paper/future-sight-and-tough-fights-revolutionizing
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
2412.11589
https://arxiv.org/abs/2412.11589v4
https://arxiv.org/pdf/2412.11589v4.pdf
https://github.com/uikdwnd/FENRec
true
true
false
pytorch
https://paperswithcode.com/paper/online-continual-learning-a-systematic
Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks
2501.04897
https://arxiv.org/abs/2501.04897v1
https://arxiv.org/pdf/2501.04897v1.pdf
https://github.com/kiyan-rezaee/systematic-literature-review-on-online-continual-learning
true
true
true
none
https://paperswithcode.com/paper/feyngame-3-0
FeynGame 3.0
2501.04651
https://arxiv.org/abs/2501.04651v1
https://arxiv.org/pdf/2501.04651v1.pdf
https://gitlab.com/feyngame/FeynGame
true
true
false
none
https://paperswithcode.com/paper/footstepnet-an-efficient-actor-critic-method
FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecasting
2403.12589
https://arxiv.org/abs/2403.12589v2
https://arxiv.org/pdf/2403.12589v2.pdf
https://github.com/Rhoban/footstepnet_envs
true
false
true
jax