Split (1)

train · 300k rows

paper_url stringlengths 36 81	paper_title stringlengths 1 242 ⌀	paper_arxiv_id stringlengths 9 16 ⌀	paper_url_abs stringlengths 18 314	paper_url_pdf stringlengths 21 935 ⌀	repo_url stringlengths 26 200	is_official bool 2 classes	mentioned_in_paper bool 2 classes	mentioned_in_github bool 2 classes	framework stringclasses 9 values
https://paperswithcode.com/paper/random-reshuffling-for-stochastic-gradient	Random Reshuffling for Stochastic Gradient Langevin Dynamics	2501.16055	https://arxiv.org/abs/2501.16055v1	https://arxiv.org/pdf/2501.16055v1.pdf	https://github.com/lshaw8317/RandomReshuffleSGLD	true	false	false	pytorch
https://paperswithcode.com/paper/optimizing-near-field-computation-in-the	Optimizing Near Field Computation in the MLFMA Algorithm with Data Redundancy and Performance Modeling on a Single GPU	2403.01596	https://arxiv.org/abs/2403.01596v1	https://arxiv.org/pdf/2403.01596v1.pdf	https://github.com/mortezamsp/P2P_with_data_redundancy	true	false	true	none
https://paperswithcode.com/paper/fusionaudio-1-2m-towards-fine-grained-audio	FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion	2506.01111	https://arxiv.org/abs/2506.01111v1	https://arxiv.org/pdf/2506.01111v1.pdf	https://github.com/satsuki2486441738/fusionaudio	true	true	true	jax
https://paperswithcode.com/paper/fastabx-a-library-for-efficient-computation	fastabx: A library for efficient computation of ABX discriminability	2505.02692	https://arxiv.org/abs/2505.02692v1	https://arxiv.org/pdf/2505.02692v1.pdf	https://github.com/bootphon/fastabx	true	true	true	pytorch
https://paperswithcode.com/paper/racnn-residual-attention-convolutional-neural	RACNN: Residual Attention Convolutional Neural Network for Near-Field Channel Estimation in 6G Wireless Communications	2503.02299	https://arxiv.org/abs/2503.02299v3	https://arxiv.org/pdf/2503.02299v3.pdf	https://github.com/DoHaiSon/RACNN	true	true	false	tf
https://paperswithcode.com/paper/transpl-vq-code-transition-matrices-for	TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation	2505.09955	https://arxiv.org/abs/2505.09955v1	https://arxiv.org/pdf/2505.09955v1.pdf	https://github.com/eai-lab/transpl	true	true	false	pytorch
https://paperswithcode.com/paper/an-invitation-to-tropical-alexandrov	An Invitation to Tropical Alexandrov Curvature	2105.07423	https://arxiv.org/abs/2105.07423v3	https://arxiv.org/pdf/2105.07423v3.pdf	https://github.com/antheamonod/TropAlex	true	true	true	none
https://paperswithcode.com/paper/a-multi-modal-neural-geometric-solver-with	A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram	2302.11097	https://arxiv.org/abs/2302.11097v2	https://arxiv.org/pdf/2302.11097v2.pdf	https://github.com/mingliangzhang2018/pgps	true	true	true	pytorch
https://paperswithcode.com/paper/socialjax-an-evaluation-suite-for-multi-agent	SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmas	2503.14576	https://arxiv.org/abs/2503.14576v2	https://arxiv.org/pdf/2503.14576v2.pdf	https://github.com/cooperativex/socialjax	true	true	true	jax
https://paperswithcode.com/paper/seg-zero-reasoning-chain-guided-segmentation	Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement	2503.06520	https://arxiv.org/abs/2503.06520v1	https://arxiv.org/pdf/2503.06520v1.pdf	https://github.com/dvlab-research/VisionReasoner	false	false	true	pytorch
https://paperswithcode.com/paper/mri-super-resolution-reconstruction-using	MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting	2503.01576	https://arxiv.org/abs/2503.01576v2	https://arxiv.org/pdf/2503.01576v2.pdf	https://github.com/mosaf/res-srdiff	true	true	true	pytorch
https://paperswithcode.com/paper/learning-hierarchical-prompt-with-structured	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models	2312.06323	https://arxiv.org/abs/2312.06323v1	https://arxiv.org/pdf/2312.06323v1.pdf	https://github.com/vill-lab/2024-aaai-hpt	true	true	true	pytorch
https://paperswithcode.com/paper/hpt-hierarchically-prompting-vision-language	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling	2408.14812	https://arxiv.org/abs/2408.14812v1	https://arxiv.org/pdf/2408.14812v1.pdf	https://github.com/vill-lab/2024-aaai-hpt	false	false	true	pytorch
https://paperswithcode.com/paper/learning-to-prompt-for-vision-language-models	Learning to Prompt for Vision-Language Models	2109.01134	https://arxiv.org/abs/2109.01134v6	https://arxiv.org/pdf/2109.01134v6.pdf	https://github.com/vill-lab/2024-aaai-hpt	false	false	true	pytorch
https://paperswithcode.com/paper/conditional-prompt-learning-for-vision	Conditional Prompt Learning for Vision-Language Models	2203.05557	https://arxiv.org/abs/2203.05557v2	https://arxiv.org/pdf/2203.05557v2.pdf	https://github.com/vill-lab/2024-aaai-hpt	false	false	true	pytorch
https://paperswithcode.com/paper/trajectory-class-aware-multi-agent	Trajectory-Class-Aware Multi-Agent Reinforcement Learning	2503.01440	https://arxiv.org/abs/2503.01440v1	https://arxiv.org/pdf/2503.01440v1.pdf	https://github.com/aailab-kaist/trama	true	true	false	pytorch
https://paperswithcode.com/paper/nbnet-noise-basis-learning-for-image	NBNet: Noise Basis Learning for Image Denoising with Subspace Projection	2012.15028	https://arxiv.org/abs/2012.15028v2	https://arxiv.org/pdf/2012.15028v2.pdf	https://github.com/MindSpore-scientific-2/code-4/tree/main/NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection	false	false	false	mindspore
https://paperswithcode.com/paper/full-scale-representation-guided-network-for	Full-scale Representation Guided Network for Retinal Vessel Segmentation	2501.18921	https://arxiv.org/abs/2501.18921v1	https://arxiv.org/pdf/2501.18921v1.pdf	https://github.com/zombasy/fsg-net-pytorch	true	true	true	pytorch
https://paperswithcode.com/paper/generalized-recorrupted-to-recorrupted-self	Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise	2412.04648	https://arxiv.org/abs/2412.04648v2	https://arxiv.org/pdf/2412.04648v2.pdf	https://github.com/deepinv/deepinv	false	true	false	pytorch
https://paperswithcode.com/paper/satori-towards-proactive-ar-assistant-with	Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling	2410.16668	https://arxiv.org/abs/2410.16668v3	https://arxiv.org/pdf/2410.16668v3.pdf	https://github.com/vida-nyu/satori-assistance	true	true	true	pytorch
https://paperswithcode.com/paper/vmts-vision-assisted-teacher-student	VMTS: Vision-Assisted Teacher-Student Reinforcement Learning for Multi-Terrain Locomotion in Bipedal Robots	2503.07049	https://arxiv.org/abs/2503.07049v1	https://arxiv.org/pdf/2503.07049v1.pdf	https://github.com/chenfu-user/VMTS	true	false	true	pytorch
https://paperswithcode.com/paper/ethosgpt-mapping-human-value-diversity-to	EthosGPT: Mapping Human Value Diversity to Advance Sustainable Development Goals (SDGs)	2504.09861	https://arxiv.org/abs/2504.09861v1	https://arxiv.org/pdf/2504.09861v1.pdf	https://github.com/sunshineluyao/EthosGPT	false	true	false	none
https://paperswithcode.com/paper/q-eval-100k-evaluating-visual-quality-and	Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content	2503.02357	https://arxiv.org/abs/2503.02357v2	https://arxiv.org/pdf/2503.02357v2.pdf	https://github.com/zzc-1998/q-eval	true	true	true	pytorch
https://paperswithcode.com/paper/cmmloc-advancing-text-to-pointcloud	CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework	2503.02593	https://arxiv.org/abs/2503.02593v2	https://arxiv.org/pdf/2503.02593v2.pdf	https://github.com/kevin301342/cmmloc	true	true	true	pytorch
https://paperswithcode.com/paper/leveraging-optimization-for-adaptive-attacks	Leveraging Optimization for Adaptive Attacks on Image Watermarks	2309.16952	https://arxiv.org/abs/2309.16952v2	https://arxiv.org/pdf/2309.16952v2.pdf	https://github.com/nilslukas/adaptive-watermark-attacks	true	false	true	pytorch
https://paperswithcode.com/paper/exploring-the-physical-properties-of-type-ii	Exploring the physical properties of Type II Quasar candidates at intermediate redshifts with CIGALE	2503.03547	https://arxiv.org/abs/2503.03547v1	https://arxiv.org/pdf/2503.03547v1.pdf	https://github.com/pedro-acunha/AMELIA	true	true	false	none
https://paperswithcode.com/paper/flexible-and-probabilistic-topology-tracking	Flexible and Probabilistic Topology Tracking with Partial Optimal Transport	2302.02895	https://arxiv.org/abs/2302.02895v3	https://arxiv.org/pdf/2302.02895v3.pdf	https://github.com/tdavislab/gwmt	true	true	false	none
https://paperswithcode.com/paper/preference-diffusion-for-recommendation	Preference Diffusion for Recommendation	2410.13117	https://arxiv.org/abs/2410.13117v2	https://arxiv.org/pdf/2410.13117v2.pdf	https://github.com/lswhim/preferdiff	true	true	false	pytorch
https://paperswithcode.com/paper/dafi-an-open-source-framework-for-ensemble	DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion	2012.02651	https://arxiv.org/abs/2012.02651v1	https://arxiv.org/pdf/2012.02651v1.pdf	https://github.com/xiaoh/DAFI	true	true	true	none
https://paperswithcode.com/paper/regularized-ensemble-kalman-methods-for	Regularized Ensemble Kalman Methods for Inverse Problems	1910.01292	http://arxiv.org/abs/1910.01292v2	http://arxiv.org/pdf/1910.01292v2.pdf	https://github.com/xiaoh/DAFI	true	true	true	none
https://paperswithcode.com/paper/evaluation-of-ensemble-methods-for	Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes	2004.05541	http://arxiv.org/abs/2004.05541v1	http://arxiv.org/pdf/2004.05541v1.pdf	https://github.com/xiaoh/DAFI	true	false	true	none
https://paperswithcode.com/paper/talk-is-not-always-cheap-promoting-wireless	Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts	2504.14621	https://arxiv.org/abs/2504.14621v1	https://arxiv.org/pdf/2504.14621v1.pdf	https://github.com/yangzhenkui/witalk	true	true	true	pytorch
https://paperswithcode.com/paper/fast-deep-learning-for-automatic-modulation	Fast Deep Learning for Automatic Modulation Classification	1901.05850	http://arxiv.org/abs/1901.05850v1	http://arxiv.org/pdf/1901.05850v1.pdf	https://github.com/dharaspatel/CNN_Signal_Classification	false	false	true	none
https://paperswithcode.com/paper/visual-sketchpad-sketching-as-a-visual-chain	Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models	2406.09403	https://arxiv.org/abs/2406.09403v3	https://arxiv.org/pdf/2406.09403v3.pdf	https://github.com/zhaochen0110/openthinkimg	false	false	true	pytorch
https://paperswithcode.com/paper/a-comparison-of-the-effects-of-different	A comparison of the effects of different methodologies on the statistics learning profiles of prospective primary education teachers from a gender perspective	2402.05479	https://arxiv.org/abs/2402.05479v1	https://arxiv.org/pdf/2402.05479v1.pdf	https://github.com/zhaochen0110/openthinkimg	false	false	true	pytorch
https://paperswithcode.com/paper/a-retrospective-systematic-study-on	A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma	2502.03772	https://arxiv.org/abs/2502.03772v1	https://arxiv.org/pdf/2502.03772v1.pdf	https://github.com/Asunatan/HSQformer	true	true	true	pytorch
https://paperswithcode.com/paper/every-call-is-precious-global-optimization-of	Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants	2502.04290	https://arxiv.org/abs/2502.04290v1	https://arxiv.org/pdf/2502.04290v1.pdf	https://github.com/fouratifares/ECP	true	false	true	none
https://paperswithcode.com/paper/medalpaca-an-open-source-collection-of	MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data	2304.08247	https://arxiv.org/abs/2304.08247v2	https://arxiv.org/pdf/2304.08247v2.pdf	https://github.com/tuneinsight/federated-llms	false	false	true	pytorch
https://paperswithcode.com/paper/2506-06407	TimeWak: Temporal Chained-Hashing Watermark for Time Series Data	2506.06407	https://arxiv.org/abs/2506.06407v2	https://arxiv.org/pdf/2506.06407v2.pdf	https://github.com/soizhiwen/TimeWak	true	true	false	pytorch
https://paperswithcode.com/paper/profiling-electric-vehicles-via-early	Profiling Electric Vehicles via Early Charging Voltage Patterns	2506.07714	https://arxiv.org/abs/2506.07714v1	https://arxiv.org/pdf/2506.07714v1.pdf	https://github.com/spritz-group/EV-Volt-Auth	true	true	false	none
https://paperswithcode.com/paper/interactrank-personalized-web-scale-search	InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features	2504.06609	https://arxiv.org/abs/2504.06609v1	https://arxiv.org/pdf/2504.06609v1.pdf	https://github.com/pinterest/atg-research	true	true	false	pytorch
https://paperswithcode.com/paper/copyspec-accelerating-llms-with-speculative	CopySpec: Accelerating LLMs with Speculative Copy-and-Paste Without Compromising Quality	2502.08923	https://arxiv.org/abs/2502.08923v1	https://arxiv.org/pdf/2502.08923v1.pdf	https://github.com/razvandu/copyspec	true	true	true	pytorch
https://paperswithcode.com/paper/cvalues-measuring-the-values-of-chinese-large	CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility	2307.09705	https://arxiv.org/abs/2307.09705v1	https://arxiv.org/pdf/2307.09705v1.pdf	https://github.com/sunshineluyao/EthosGPT	false	false	true	none
https://paperswithcode.com/paper/how-well-do-llms-represent-values-across	How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions	2406.14805	https://arxiv.org/abs/2406.14805v1	https://arxiv.org/pdf/2406.14805v1.pdf	https://github.com/sunshineluyao/EthosGPT	false	false	true	none
https://paperswithcode.com/paper/culturellm-incorporating-cultural-differences	CultureLLM: Incorporating Cultural Differences into Large Language Models	2402.10946	https://arxiv.org/abs/2402.10946v3	https://arxiv.org/pdf/2402.10946v3.pdf	https://github.com/sunshineluyao/EthosGPT	false	false	true	none
https://paperswithcode.com/paper/nextou-efficient-topology-aware-u-net-for	NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation	2305.15911	https://arxiv.org/abs/2305.15911v1	https://arxiv.org/pdf/2305.15911v1.pdf	https://github.com/PengchengShi1220/AortaSeg24	false	false	true	pytorch
https://paperswithcode.com/paper/mc-2-a-multilingual-corpus-of-minority	MC$^2$: Towards Transparent and Culturally-Aware NLP for Minority Languages in China	2311.08348	https://arxiv.org/abs/2311.08348v2	https://arxiv.org/pdf/2311.08348v2.pdf	https://github.com/luciusssss/mc2_corpus	true	true	true	none
https://paperswithcode.com/paper/mganet-a-robust-model-for-quality-enhancement	MGANet: A Robust Model for Quality Enhancement of Compressed Video	1811.09150	http://arxiv.org/abs/1811.09150v4	http://arxiv.org/pdf/1811.09150v4.pdf	https://github.com/MindSpore-scientific/code-8/tree/main/mgan-mindspore	false	false	false	mindspore
https://paperswithcode.com/paper/ptwt-the-pytorch-wavelet-toolbox	ptwt - The PyTorch Wavelet Toolbox	null	https://jmlr.org/papers/v25/23-0636.html	https://jmlr.org/papers/volume25/23-0636/23-0636.pdf	https://github.com/v0lta/pytorch-wavelet-toolbox	false	false	false	pytorch
https://paperswithcode.com/paper/scalable-image-coding-for-humans-and-machines	Scalable Image Coding for Humans and Machines	2107.08373	https://arxiv.org/abs/2107.08373v2	https://arxiv.org/pdf/2107.08373v2.pdf	https://github.com/InterDigitalInc/CompressAI-Vision/blob/main/compressai_vision/codecs/sic_sfu2022.py	false	false	false	pytorch
https://paperswithcode.com/paper/gradient-surgery-for-multi-task-learning-1	Gradient Surgery for Multi-Task Learning	2001.06782	https://arxiv.org/abs/2001.06782v4	https://arxiv.org/pdf/2001.06782v4.pdf	https://github.com/MindSpore-scientific/code-10/tree/main/PCGrad-mindspore-example	false	false	false	mindspore
https://paperswithcode.com/paper/pyslam-an-open-source-modular-and-extensible	pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM	2502.11955	https://arxiv.org/abs/2502.11955v2	https://arxiv.org/pdf/2502.11955v2.pdf	https://github.com/luigifreda/pyslam	true	false	true	pytorch
https://paperswithcode.com/paper/a-comprehensive-survey-of-mixture-of-experts	A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications	2503.07137	https://arxiv.org/abs/2503.07137v3	https://arxiv.org/pdf/2503.07137v3.pdf	https://github.com/deepseek-ai/DeepEP	true	false	false	pytorch
https://paperswithcode.com/paper/smtpd-a-new-benchmark-for-temporal-prediction	SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity	2503.04446	https://arxiv.org/abs/2503.04446v1	https://arxiv.org/pdf/2503.04446v1.pdf	https://github.com/zhuwei321/smtpd	true	true	true	pytorch
https://paperswithcode.com/paper/khuri-treiman-equations-for-3p-decays-of	Khuri-Treiman equations for $3π$ decays of particles with spin	1910.03107	https://arxiv.org/abs/1910.03107v1	https://arxiv.org/pdf/1910.03107v1.pdf	https://github.com/dwinney/jpacTriangle	false	false	true	none
https://paperswithcode.com/paper/navigation-world-models	Navigation World Models	2412.03572	https://arxiv.org/abs/2412.03572v2	https://arxiv.org/pdf/2412.03572v2.pdf	https://github.com/facebookresearch/nwm	false	false	true	pytorch
https://paperswithcode.com/paper/inn-par-invertible-neural-network-for-ppg-to	INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction	2409.09021	https://arxiv.org/abs/2409.09021v2	https://arxiv.org/pdf/2409.09021v2.pdf	https://github.com/soumitra1992/innpar-ppg2abp	true	true	true	pytorch
https://paperswithcode.com/paper/energy-efficient-federated-learning-for-aiot	Energy-Efficient Federated Learning for AIoT using Clustering Methods	2505.09704	https://arxiv.org/abs/2505.09704v1	https://arxiv.org/pdf/2505.09704v1.pdf	https://github.com/robertomatheuspp/clustering_ee_fl	true	false	false	pytorch
https://paperswithcode.com/paper/elucidating-the-design-space-of-diffusion	Elucidating the Design Space of Diffusion-Based Generative Models	2206.00364	https://arxiv.org/abs/2206.00364v2	https://arxiv.org/pdf/2206.00364v2.pdf	https://github.com/dopplerchase/cira-diff	false	false	true	pytorch
https://paperswithcode.com/paper/denoising-diffusion-probabilistic-models	Denoising Diffusion Probabilistic Models	2006.11239	https://arxiv.org/abs/2006.11239v2	https://arxiv.org/pdf/2006.11239v2.pdf	https://github.com/dopplerchase/cira-diff	false	false	true	pytorch
https://paperswithcode.com/paper/arxivdigestables-synthesizing-scientific	ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models	2410.22360	https://arxiv.org/abs/2410.22360v1	https://arxiv.org/pdf/2410.22360v1.pdf	https://github.com/allenai/ai2-scholarqa-lib	false	false	true	pytorch
https://paperswithcode.com/paper/efficient-reasoning-models-a-survey	Efficient Reasoning Models: A Survey	2504.10903	https://arxiv.org/abs/2504.10903v1	https://arxiv.org/pdf/2504.10903v1.pdf	https://github.com/fscdc/awesome-efficient-reasoning-models	true	true	true	none
https://paperswithcode.com/paper/perfcam-digital-twinning-for-production-lines	PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models	2504.18165	https://arxiv.org/abs/2504.18165v1	https://arxiv.org/pdf/2504.18165v1.pdf	https://github.com/AstraZeneca/PerfCam	true	false	false	none
https://paperswithcode.com/paper/dfpn-deformable-frame-prediction-network	DFPN: Deformable Frame Prediction Network	2105.12794	https://arxiv.org/abs/2105.12794v1	https://arxiv.org/pdf/2105.12794v1.pdf	https://github.com/KUIS-AI-Tekalp-Research-Group/frame-prediction	false	false	true	pytorch
https://paperswithcode.com/paper/rsafe-incentivizing-proactive-reasoning-to	RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards	2506.07736	https://arxiv.org/abs/2506.07736v1	https://arxiv.org/pdf/2506.07736v1.pdf	https://github.com/sophiezheng998/rsafe	true	true	false	pytorch
https://paperswithcode.com/paper/reinforcement-learning-for-reasoning-in-small	Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't	2503.16219	https://arxiv.org/abs/2503.16219v1	https://arxiv.org/pdf/2503.16219v1.pdf	https://github.com/knoveleng/open-rs	true	true	true	pytorch
https://paperswithcode.com/paper/2505-11239	Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks	2505.11239	https://arxiv.org/abs/2505.11239v2	https://arxiv.org/pdf/2505.11239v2.pdf	https://github.com/cruiseresearchgroup/massive-steps	true	true	true	none
https://paperswithcode.com/paper/diffusion-posterior-sampling-for-general	Diffusion Posterior Sampling for General Noisy Inverse Problems	2209.14687	https://arxiv.org/abs/2209.14687v4	https://arxiv.org/pdf/2209.14687v4.pdf	https://github.com/alexdenker/SteerableConditionalDiffusion	false	false	true	pytorch
https://paperswithcode.com/paper/rankclip-ranking-consistent-language-image	RankCLIP: Ranking-Consistent Language-Image Pretraining	2404.09387	https://arxiv.org/abs/2404.09387v2	https://arxiv.org/pdf/2404.09387v2.pdf	https://github.com/jam1ezhang/rankclip	true	true	true	pytorch
https://paperswithcode.com/paper/estimating-object-physical-properties-from-1	Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning	2507.05029	https://arxiv.org/abs/2507.05029v1	https://arxiv.org/pdf/2507.05029v1.pdf	https://github.com/RavineWindteer/Depth-mass-estimator	true	true	false	pytorch
https://paperswithcode.com/paper/polynomial-description-for-the-t-orbit-spaces	Orbit spaces of Weyl groups acting on compact tori: a unified and explicit polynomial description	2203.13152	https://arxiv.org/abs/2203.13152v2	https://arxiv.org/pdf/2203.13152v2.pdf	https://github.com/tobiasmetzlaff/generalizedchebyshev	true	true	false	none
https://paperswithcode.com/paper/adiabatic-replay-for-continual-learning	Adiabatic replay for continual learning	2303.13157	https://arxiv.org/abs/2303.13157v1	https://arxiv.org/pdf/2303.13157v1.pdf	https://github.com/alexk1704/scclv2	true	true	false	tf
https://paperswithcode.com/paper/semantic-decoupled-spatial-partition-guided	Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection	2506.10601	https://arxiv.org/abs/2506.10601v1	https://arxiv.org/pdf/2506.10601v1.pdf	https://github.com/antxinyuan/ssp	true	true	true	pytorch
https://paperswithcode.com/paper/cradmap-applied-distributed-volumetric	CRADMap: Applied Distributed Volumetric Mapping with 5G-Connected Multi-Robots and 4D Radar Perception	2503.00262	https://arxiv.org/abs/2503.00262v2	https://arxiv.org/pdf/2503.00262v2.pdf	https://github.com/Maaz-qureshi98/VolumetricMapping	true	false	true	none
https://paperswithcode.com/paper/hierarchically-accelerated-coverage-path	Hierarchically Accelerated Coverage Path Planning for Redundant Manipulators	2502.19591	https://arxiv.org/abs/2502.19591v1	https://arxiv.org/pdf/2502.19591v1.pdf	https://github.com/uwgraphics/arm_coverage	true	false	true	none
https://paperswithcode.com/paper/mom-linear-sequence-modeling-with-mixture-of	MoM: Linear Sequence Modeling with Mixture-of-Memories	2502.13685	https://arxiv.org/abs/2502.13685v2	https://arxiv.org/pdf/2502.13685v2.pdf	https://github.com/opensparsellms/linear-moe	true	true	true	pytorch
https://paperswithcode.com/paper/lasp-2-rethinking-sequence-parallelism-for	LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid	2502.07563	https://arxiv.org/abs/2502.07563v1	https://arxiv.org/pdf/2502.07563v1.pdf	https://github.com/opensparsellms/linear-moe	true	true	true	pytorch
https://paperswithcode.com/paper/clip-adapter-better-vision-language-models	CLIP-Adapter: Better Vision-Language Models with Feature Adapters	2110.04544	https://arxiv.org/abs/2110.04544v2	https://arxiv.org/pdf/2110.04544v2.pdf	https://github.com/gaopengcuhk/clip-adapter	true	true	true	pytorch
https://paperswithcode.com/paper/progressive-rendering-distillation-adapting	Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data	2503.21694	https://arxiv.org/abs/2503.21694v1	https://arxiv.org/pdf/2503.21694v1.pdf	https://github.com/theericma/triplaneturbo	true	true	true	jax
https://paperswithcode.com/paper/non-centering-for-discrete-valued-state	Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi	2504.11836	https://arxiv.org/abs/2504.11836v2	https://arxiv.org/pdf/2504.11836v2.pdf	https://github.com/neilljn/antidote_methods	true	true	false	jax
https://paperswithcode.com/paper/differentially-private-permutation-tests	Differentially Private Permutation Tests: Applications to Kernel Methods	2310.19043	https://arxiv.org/abs/2310.19043v2	https://arxiv.org/pdf/2310.19043v2.pdf	https://github.com/antoninschrab/dckernel-paper	false	false	true	jax
https://paperswithcode.com/paper/deep-reinforcement-learning-for-controlled	Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming	2402.08491	https://arxiv.org/abs/2402.08491v3	https://arxiv.org/pdf/2402.08491v3.pdf	https://github.com/jakub-zarzycki2022/gattaca	false	false	true	pytorch
https://paperswithcode.com/paper/reasoning-towards-fairness-mitigating-bias-in	Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning	2504.05632	https://arxiv.org/abs/2504.05632v2	https://arxiv.org/pdf/2504.05632v2.pdf	https://github.com/Sanchit-404/Reasoing-Towards-Fairness	true	false	false	pytorch
https://paperswithcode.com/paper/data-driven-learning-of-geometric-scattering-1	Data-Driven Learning of Geometric Scattering Networks	2010.02415	https://arxiv.org/abs/2010.02415v3	https://arxiv.org/pdf/2010.02415v3.pdf	https://github.com/KrishnaswamyLab/LearnableScattering	false	false	true	pytorch
https://paperswithcode.com/paper/relational-representation-learning-network	Relational Representation Learning Network for Cross-Spectral Image Patch Matching	2403.11751	https://arxiv.org/abs/2403.11751v3	https://arxiv.org/pdf/2403.11751v3.pdf	https://github.com/yuchuang1205/rrl-net	true	true	true	tf
https://paperswithcode.com/paper/non-hermitian-numerical-renormalization-group	Non-Hermitian Numerical Renormalization Group: Solution of the non-Hermitian Kondo model	2504.07019	https://arxiv.org/abs/2504.07019v2	https://arxiv.org/pdf/2504.07019v2.pdf	https://github.com/phillipbc/nonhermitiannrg	true	true	true	none
https://paperswithcode.com/paper/bayesian-model-averaging-in-causal	Bayesian Model Averaging in Causal Instrumental Variable Models	2504.13520	https://arxiv.org/abs/2504.13520v3	https://arxiv.org/pdf/2504.13520v3.pdf	https://github.com/gregorsteiner/givbma.jl	true	true	false	none
https://paperswithcode.com/paper/turning-trash-into-treasure-accelerating	Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling	2408.08696	https://arxiv.org/abs/2408.08696v2	https://arxiv.org/pdf/2408.08696v2.pdf	https://github.com/luowaterbi/tokenrecycling	true	true	true	pytorch
https://paperswithcode.com/paper/using-time-structure-to-estimate-causal	Using Time Structure to Estimate Causal Effects	2504.11076	https://arxiv.org/abs/2504.11076v2	https://arxiv.org/pdf/2504.11076v2.pdf	https://gitlab.com/dlr-dw/using_time_structure_to_estimate_causal_effects_code	true	true	true	none
https://paperswithcode.com/paper/a-combined-channel-approach-for-decoding	A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration	2412.06336	https://arxiv.org/abs/2412.06336v1	https://arxiv.org/pdf/2412.06336v1.pdf	https://github.com/Navid-Ziaei/combined-channel-iEEG-decoder	true	false	true	none
https://paperswithcode.com/paper/cfics-graph-based-classification-of-common	CFiCS: Graph-Based Classification of Common Factors and Microcounseling Skills	2503.22277	https://arxiv.org/abs/2503.22277v1	https://arxiv.org/pdf/2503.22277v1.pdf	https://github.com/smidtfab/CFiCS	true	false	false	pytorch
https://paperswithcode.com/paper/federated-semantic-learning-for-privacy	Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation	2503.23026	https://arxiv.org/abs/2503.23026v1	https://arxiv.org/pdf/2503.23026v1.pdf	https://github.com/sapphire-star/ffmsr	true	true	false	pytorch
https://paperswithcode.com/paper/instantid-zero-shot-identity-preserving	InstantID: Zero-shot Identity-Preserving Generation in Seconds	2401.07519	https://arxiv.org/abs/2401.07519v2	https://arxiv.org/pdf/2401.07519v2.pdf	https://github.com/instantx-research/instantid	false	false	true	pytorch
https://paperswithcode.com/paper/moving-object-segmentation-in-point-cloud	Moving Object Segmentation in Point Cloud Data using Hidden Markov Models	2410.18638	https://arxiv.org/abs/2410.18638v1	https://arxiv.org/pdf/2410.18638v1.pdf	https://github.com/vb44/hmm-mos	true	true	true	none
https://paperswithcode.com/paper/omniesi-a-unified-framework-for-enzyme	OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning	2506.17963	https://arxiv.org/abs/2506.17963v1	https://arxiv.org/pdf/2506.17963v1.pdf	https://github.com/hong-yu-zhang/omniesi	true	true	true	pytorch
https://paperswithcode.com/paper/how-is-llm-reasoning-distracted-by-irrelevant	How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark	2505.18761	https://arxiv.org/abs/2505.18761v1	https://arxiv.org/pdf/2505.18761v1.pdf	https://github.com/mlyann/gsm-dc	true	true	false	pytorch
https://paperswithcode.com/paper/counterfactual-query-rewriting-to-use	Counterfactual Query Rewriting to Use Historical Relevance Feedback	2502.03891	https://arxiv.org/abs/2502.03891v1	https://arxiv.org/pdf/2502.03891v1.pdf	https://github.com/webis-de/ecir25-counterfactual-query-rewriting	true	false	false	none
https://paperswithcode.com/paper/sundial-a-family-of-highly-capable-time	Sundial: A Family of Highly Capable Time Series Foundation Models	2502.00816	https://arxiv.org/abs/2502.00816v1	https://arxiv.org/pdf/2502.00816v1.pdf	https://github.com/thuml/Sundial	false	false	true	pytorch
https://paperswithcode.com/paper/why-should-adversarial-perturbations-be	Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP	2210.10683	https://arxiv.org/abs/2210.10683v1	https://arxiv.org/pdf/2210.10683v1.pdf	https://github.com/yang-yan-yang-yan/sop	false	false	true	none
https://paperswithcode.com/paper/sop-unlock-the-power-of-social-facilitation	SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters	2407.01902	https://arxiv.org/abs/2407.01902v2	https://arxiv.org/pdf/2407.01902v2.pdf	https://github.com/yang-yan-yang-yan/sop	true	true	true	none

Subsets and Splits

Framework Repo Connectivity Analysis

Reveals the number of official and unofficial repositories and papers associated with different frameworks, highlighting the most connected ones.

Deduplicated Paper-Code Links

This query provides a detailed and organized list of repositories linked to single papers, highlighting official status and mention sources, which is useful for understanding the relationship between papers and their corresponding repositories.

Paper Repo Counts & Distribution

Provides detailed statistics on the distribution of papers across different numbers of repositories, highlighting the percentage of papers with multiple repositories.

Quantum Papers with Code Links

Lists quantum-related papers with their titles, arXiv IDs, frameworks, and code repository links, providing a valuable resource for researchers interested in quantum computing.

Financial Stock Price Prediction

Finds papers related to stock prices, financial markets, and predictions, providing a focused subset for further analysis.

SQL Console for pwc-archive/links-between-paper-and-code

Retrieves specific details about a single paper by its arXiv ID, providing limited insight into the dataset.

Search for YOLO Links

Retrieves a limited set of records related to YOLO, providing basic information about papers and repositories but without deeper analysis.

Prompt Optimization and Personalization

Retrieves a limited set of papers with titles containing specific keywords related to prompt optimization and personalization, providing basic filtering of the dataset.