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/dual-invariance-self-training-for-reliable	Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition	2501.17628	https://arxiv.org/abs/2501.17628v1	https://arxiv.org/pdf/2501.17628v1.pdf	https://github.com/sahar-nasiri/dist	true	true	false	pytorch
https://paperswithcode.com/paper/waste-not-want-not-why-rarefying-microbiome	Waste Not, Want Not: Why Rarefying Microbiome Data is Inadmissible	1310.0424	https://arxiv.org/abs/1310.0424v2	https://arxiv.org/pdf/1310.0424v2.pdf	https://github.com/joey711/phyloseq	false	false	true	none
https://paperswithcode.com/paper/self-adaptive-training-beyond-empirical-risk	Self-Adaptive Training: beyond Empirical Risk Minimization	2002.10319	https://arxiv.org/abs/2002.10319v2	https://arxiv.org/pdf/2002.10319v2.pdf	https://github.com/BorealisAI/towards-better-sel-cls	false	false	true	pytorch
https://paperswithcode.com/paper/stochastic-gradient-descent-with	Stochastic Gradient Descent with Preconditioned Polyak Step-size	2310.02093	https://arxiv.org/abs/2310.02093v1	https://arxiv.org/pdf/2310.02093v1.pdf	https://github.com/fxrshed/scaledsps	true	true	false	pytorch
https://paperswithcode.com/paper/turingbench-a-benchmark-environment-for	TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation	2109.13296	https://arxiv.org/abs/2109.13296v1	https://arxiv.org/pdf/2109.13296v1.pdf	https://github.com/amritabh/conda-gen-text-detection	false	false	true	pytorch
https://paperswithcode.com/paper/quantum-accelerated-causal-tomography-circuit	A scalable quantum gate-based implementation for causal hypothesis testing	2209.02016	https://arxiv.org/abs/2209.02016v4	https://arxiv.org/pdf/2209.02016v4.pdf	https://github.com/advanced-research-centre/qacht	true	true	false	none
https://paperswithcode.com/paper/integrating-earth-observation-data-into	Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities	2301.12985	https://arxiv.org/abs/2301.12985v1	https://arxiv.org/pdf/2301.12985v1.pdf	https://github.com/AIandGlobalDevelopmentLab/causalimages-software	false	false	true	tf
https://paperswithcode.com/paper/all-languages-matter-on-the-multilingual	All Languages Matter: On the Multilingual Safety of Large Language Models	2310.00905	https://arxiv.org/abs/2310.00905v2	https://arxiv.org/pdf/2310.00905v2.pdf	https://github.com/jarviswang94/multilingual_safety_benchmark	true	true	true	none
https://paperswithcode.com/paper/understanding-in-context-learning-from	Understanding In-Context Learning from Repetitions	2310.00297	https://arxiv.org/abs/2310.00297v3	https://arxiv.org/pdf/2310.00297v3.pdf	https://github.com/elliottyan/understand-icl-from-repetition	true	true	true	pytorch
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/uygarkurt/ViT-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/fedaiot-a-federated-learning-benchmark-for	FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things	2310.00109	https://arxiv.org/abs/2310.00109v3	https://arxiv.org/pdf/2310.00109v3.pdf	https://github.com/aiot-mlsys-lab/fedaiot	true	true	true	pytorch
https://paperswithcode.com/paper/hydra-multi-head-low-rank-adaptation-for	Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning	2309.06922	https://arxiv.org/abs/2309.06922v1	https://arxiv.org/pdf/2309.06922v1.pdf	https://github.com/extremebird/hydra	true	true	true	jax
https://paperswithcode.com/paper/mini-gpts-efficient-large-language-models	Mini-GPTs: Efficient Large Language Models through Contextual Pruning	2312.12682	https://arxiv.org/abs/2312.12682v1	https://arxiv.org/pdf/2312.12682v1.pdf	https://github.com/tval2/contextual-pruning	false	true	false	pytorch
https://paperswithcode.com/paper/colora-continuous-low-rank-adaptation-for	CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations	2402.14646	https://arxiv.org/abs/2402.14646v2	https://arxiv.org/pdf/2402.14646v2.pdf	https://github.com/julesberman/colora	true	true	false	jax
https://paperswithcode.com/paper/sublinear-time-opinion-estimation-in-the	Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model	2404.16464	https://arxiv.org/abs/2404.16464v1	https://arxiv.org/pdf/2404.16464v1.pdf	https://github.com/stefanresearch/sublinear-time-opinions	true	true	true	none
https://paperswithcode.com/paper/determination-of-optimal-chain-coupling-made	Determination of Optimal Chain Coupling made by Embedding in D-Wave Quantum Annealer	2406.03364	https://arxiv.org/abs/2406.03364v1	https://arxiv.org/pdf/2406.03364v1.pdf	https://github.com/hunpyolee/optimizechainstrength	true	true	false	none
https://paperswithcode.com/paper/population-group-2-0-bringing-the-umls	Population Group 2.0: Bringing the UMLS Semantic Network up to Speed	null	https://pubmed.ncbi.nlm.nih.gov/40380730/	https://ebooks.iospress.nl/doi/10.3233/SHTI250627	https://github.com/narenkhatwani/population-group-2.0	false	false	false	none
https://paperswithcode.com/paper/going-incognito-in-the-metaverse	Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR	2208.05604	https://arxiv.org/abs/2208.05604v5	https://arxiv.org/pdf/2208.05604v5.pdf	https://github.com/metaguard/metaguard	true	true	true	none
https://paperswithcode.com/paper/self-normalizing-neural-networks	Self-Normalizing Neural Networks	1706.02515	http://arxiv.org/abs/1706.02515v5	http://arxiv.org/pdf/1706.02515v5.pdf	https://github.com/2023-MindSpore-4/Code7/tree/main/snn_mlp	false	false	false	mindspore
https://paperswithcode.com/paper/is-automated-topic-model-evaluation-broken	Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence	2107.02173	https://arxiv.org/abs/2107.02173v3	https://arxiv.org/pdf/2107.02173v3.pdf	https://github.com/dominiksinsaarland/evaluating-topic-model-output	false	false	true	none
https://paperswithcode.com/paper/re-visiting-automated-topic-model-evaluation	Revisiting Automated Topic Model Evaluation with Large Language Models	2305.12152	https://arxiv.org/abs/2305.12152v2	https://arxiv.org/pdf/2305.12152v2.pdf	https://github.com/dominiksinsaarland/evaluating-topic-model-output	true	true	true	none
https://paperswithcode.com/paper/randomizing-hypergraphs-preserving-degree	Randomizing hypergraphs preserving degree correlation and local clustering	2106.12162	https://arxiv.org/abs/2106.12162v2	https://arxiv.org/pdf/2106.12162v2.pdf	https://github.com/kazuibasou/hyper-dk-series	true	true	true	none
https://paperswithcode.com/paper/identifying-important-group-of-pixels-using	Identifying Important Group of Pixels using Interactions	2401.03785	https://arxiv.org/abs/2401.03785v3	https://arxiv.org/pdf/2401.03785v3.pdf	https://github.com/kosukesumiyasu/moxi	true	true	true	pytorch
https://paperswithcode.com/paper/automated-security-response-through-online	Automated Security Response through Online Learning with Adaptive Conjectures	2402.12499	https://arxiv.org/abs/2402.12499v4	https://arxiv.org/pdf/2402.12499v4.pdf	https://github.com/limmen/csle	true	true	false	none
https://paperswithcode.com/paper/on-the-minimal-modules-for-exceptional-lie	On the minimal modules for exceptional Lie algebras: Jordan blocks and stabilisers	1508.02918	https://arxiv.org/abs/1508.02918v6	https://arxiv.org/pdf/1508.02918v6.pdf	https://github.com/davistem/nilpotent_orbits_gap	true	true	false	none
https://paperswithcode.com/paper/explainable-multimodal-emotion-reasoning	Explainable Multimodal Emotion Recognition	2306.15401	https://arxiv.org/abs/2306.15401v6	https://arxiv.org/pdf/2306.15401v6.pdf	https://github.com/zeroqiaoba/affectgpt	true	true	true	pytorch
https://paperswithcode.com/paper/seamlessm4t-massively-multilingual-multimodal	SeamlessM4T: Massively Multilingual & Multimodal Machine Translation	2308.11596	https://arxiv.org/abs/2308.11596v3	https://arxiv.org/pdf/2308.11596v3.pdf	https://github.com/facebookresearch/seamless_communication	true	true	false	pytorch
https://paperswithcode.com/paper/deriving-analytical-solutions-using-symbolic-1	Deriving Analytical Solutions Using Symbolic Matrix Structural Analysis: Part 2 -- Plane Trusses	2411.16573	https://arxiv.org/abs/2411.16573v1	https://arxiv.org/pdf/2411.16573v1.pdf	https://github.com/vplevris/symbolicmsa-2dtrusses	true	true	false	none
https://paperswithcode.com/paper/simple-pose-rethinking-and-improving-a-bottom	Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation	1911.10529	https://arxiv.org/abs/1911.10529v1	https://arxiv.org/pdf/1911.10529v1.pdf	https://github.com/2023-MindSpore-4/Code11/tree/main/simple_pose	false	false	false	mindspore
https://paperswithcode.com/paper/heuristic-learning-with-graph-neural-networks	Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction	2406.07979	https://arxiv.org/abs/2406.07979v2	https://arxiv.org/pdf/2406.07979v2.pdf	https://github.com/LARS-research/HL-GNN	true	false	true	pytorch
https://paperswithcode.com/paper/taming-diffusion-probabilistic-models-for	Taming Diffusion Probabilistic Models for Character Control	2404.15121	https://arxiv.org/abs/2404.15121v1	https://arxiv.org/pdf/2404.15121v1.pdf	https://github.com/AIGAnimation/CAMDM	false	false	true	pytorch
https://paperswithcode.com/paper/on-the-differentiability-of-the-primal-dual	On the Differentiability of the Primal-Dual Interior-Point Method	2406.11749	https://arxiv.org/abs/2406.11749v2	https://arxiv.org/pdf/2406.11749v2.pdf	https://github.com/kevin-tracy/qpax	true	true	true	jax
https://paperswithcode.com/paper/triggerless-backdoor-attack-for-nlp-tasks	Triggerless Backdoor Attack for NLP Tasks with Clean Labels	2111.07970	https://arxiv.org/abs/2111.07970v2	https://arxiv.org/pdf/2111.07970v2.pdf	https://github.com/2023-MindSpore-4/Code12/tree/main/ganleilei/CleanLabelBackdoorAttackMindspore-master	false	false	false	mindspore
https://paperswithcode.com/paper/context-based-interpretable-spatio-temporal	Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting	2402.19237	https://arxiv.org/abs/2402.19237v1	https://arxiv.org/pdf/2402.19237v1.pdf	https://github.com/qualityminds/cistgcn	true	true	true	pytorch
https://paperswithcode.com/paper/electron-energization-in-reconnection	Electron Energization in Reconnection: Eulerian versus Lagrangian Perspectives	2310.17480	https://arxiv.org/abs/2310.17480v2	https://arxiv.org/pdf/2310.17480v2.pdf	https://github.com/ammarhakim/gkyl-paper-inp	true	true	false	none
https://paperswithcode.com/paper/a-survey-on-autonomous-driving-datasets-data	A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook	2401.01454	https://arxiv.org/abs/2401.01454v2	https://arxiv.org/pdf/2401.01454v2.pdf	https://github.com/mingyuliu1/autonomous_driving_datasets	true	true	true	none
https://paperswithcode.com/paper/logformer-a-pre-train-and-tuning-pipeline-for	LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection	2401.04749	https://arxiv.org/abs/2401.04749v1	https://arxiv.org/pdf/2401.04749v1.pdf	https://github.com/hc-guo/logformer	true	true	false	pytorch
https://paperswithcode.com/paper/violation-of-expectation-via-metacognitive	Violation of Expectation via Metacognitive Prompting Reduces Theory of Mind Prediction Error in Large Language Models	2310.06983	https://arxiv.org/abs/2310.06983v1	https://arxiv.org/pdf/2310.06983v1.pdf	https://github.com/plastic-labs/voe-paper-eval	true	true	false	none
https://paperswithcode.com/paper/evolution-of-fullerenes-in-circumstellar	Evolution of Fullerenes in Circumstellar Envelopes by Carbon Condensation: Insights from Reactive Molecular Dynamics Simulations	2310.17095	https://arxiv.org/abs/2310.17095v1	https://arxiv.org/pdf/2310.17095v1.pdf	https://github.com/mengzss/fullerene_evolution	true	true	false	none
https://paperswithcode.com/paper/flag-hilbert-poincare-series-of-hyperplane	Flag Hilbert-Poincaré series of hyperplane arrangements and their Igusa zeta functions	2103.03640	https://arxiv.org/abs/2103.03640v2	https://arxiv.org/pdf/2103.03640v2.pdf	https://github.com/joshmaglione/hypigu	true	true	true	none
https://paperswithcode.com/paper/paraphrase-types-for-generation-and-detection	Paraphrase Types for Generation and Detection	2310.14863	https://arxiv.org/abs/2310.14863v3	https://arxiv.org/pdf/2310.14863v3.pdf	https://github.com/jpwahle/emnlp23-paraphrase-types	true	true	true	pytorch
https://paperswithcode.com/paper/on-the-representational-capacity-of-recurrent	On the Representational Capacity of Recurrent Neural Language Models	2310.12942	https://arxiv.org/abs/2310.12942v5	https://arxiv.org/pdf/2310.12942v5.pdf	https://github.com/rycolab/rnn-turing-completeness	true	true	true	none
https://paperswithcode.com/paper/pruning-for-protection-increasing-jailbreak	Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning	2401.10862	https://arxiv.org/abs/2401.10862v3	https://arxiv.org/pdf/2401.10862v3.pdf	https://github.com/crystaleye42/eval-safety	true	true	true	pytorch
https://paperswithcode.com/paper/efficient-graduated-non-convexity-for-pose	Efficient Graduated Non-Convexity for Pose Graph Optimization	2310.06765	https://arxiv.org/abs/2310.06765v1	https://arxiv.org/pdf/2310.06765v1.pdf	https://github.com/SNU-DLLAB/EGNC-PGO	true	true	true	none
https://paperswithcode.com/paper/chatvis-automating-scientific-visualization	ChatVis: Automating Scientific Visualization with a Large Language Model	2410.11863	https://arxiv.org/abs/2410.11863v1	https://arxiv.org/pdf/2410.11863v1.pdf	https://github.com/tanwimallick/chatvis	true	true	false	none
https://paperswithcode.com/paper/variable-selection-for-partially-linear	Robust variable selection for partially linear additive models	2401.10869	https://arxiv.org/abs/2401.10869v2	https://arxiv.org/pdf/2401.10869v2.pdf	https://github.com/alemermartinez/rplam-vs	true	true	false	none
https://paperswithcode.com/paper/multi-task-faces-mtf-data-set-a-legally-and	Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks	2311.11882	https://arxiv.org/abs/2311.11882v1	https://arxiv.org/pdf/2311.11882v1.pdf	https://github.com/ramihaf/mtf_data_set	true	true	false	pytorch
https://paperswithcode.com/paper/unearthing-a-billion-telegram-posts-about-the	Unearthing a Billion Telegram Posts about the 2024 U.S. Presidential Election: Development of a Public Dataset	2410.23638	https://arxiv.org/abs/2410.23638v1	https://arxiv.org/pdf/2410.23638v1.pdf	https://github.com/leonardo-blas/usc-tg-24-us-election	true	true	true	none
https://paperswithcode.com/paper/resnet50-on-cifar-100-without-transfer	ResNet50_on_Cifar_100_Without_Transfer_Learning	null	https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning/blob/master/abstract.txt	https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning/blob/master/abstract.txt	https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet50	false	false	false	mindspore
https://paperswithcode.com/paper/chatgpt-as-data-augmentation-for	ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection	2308.13517	https://arxiv.org/abs/2308.13517v1	https://arxiv.org/pdf/2308.13517v1.pdf	https://github.com/fangyihao/gptaug	true	false	false	pytorch
https://paperswithcode.com/paper/a-structured-l-bfgs-method-and-its	A structured L-BFGS method and its application to inverse problems	2310.07296	https://arxiv.org/abs/2310.07296v7	https://arxiv.org/pdf/2310.07296v7.pdf	https://github.com/hariagr/slbfgs	true	true	false	none
https://paperswithcode.com/paper/neur2sp-neural-two-stage-stochastic	Neur2SP: Neural Two-Stage Stochastic Programming	2205.12006	https://arxiv.org/abs/2205.12006v2	https://arxiv.org/pdf/2205.12006v2.pdf	https://github.com/khalil-research/neur2ro	false	false	true	pytorch
https://paperswithcode.com/paper/are-eeg-to-text-models-working	Are EEG-to-Text Models Working?	2405.06459	https://arxiv.org/abs/2405.06459v4	https://arxiv.org/pdf/2405.06459v4.pdf	https://github.com/mikewangwzhl/eeg-to-text	true	true	true	pytorch
https://paperswithcode.com/paper/contrastive-modules-with-temporal-attention	Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning	2311.01075	https://arxiv.org/abs/2311.01075v1	https://arxiv.org/pdf/2311.01075v1.pdf	https://github.com/niiceMing/CMTA	true	false	false	pytorch
https://paperswithcode.com/paper/mutox-universal-multilingual-audio-based	MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector	2401.05060	https://arxiv.org/abs/2401.05060v2	https://arxiv.org/pdf/2401.05060v2.pdf	https://github.com/facebookresearch/seamless_communication	true	true	false	pytorch
https://paperswithcode.com/paper/adaptive-optimizers-with-sparse-group-lasso-1	Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction	2107.14432	https://arxiv.org/abs/2107.14432v6	https://arxiv.org/pdf/2107.14432v6.pdf	https://github.com/intelligent-machine-learning/tfplus	true	true	true	tf
https://paperswithcode.com/paper/wasm-icare-a-portable-and-privacy-preserving	Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models	2310.09252	https://arxiv.org/abs/2310.09252v1	https://arxiv.org/pdf/2310.09252v1.pdf	https://github.com/jeyabbalas/wasm-icare	true	true	false	none
https://paperswithcode.com/paper/time-varying-optimization-of-lti-systems-via	Time-Varying Optimization of LTI Systems via Projected Primal-Dual Gradient Flows	2101.01799	https://arxiv.org/abs/2101.01799v3	https://arxiv.org/pdf/2101.01799v3.pdf	https://github.com/gianlucaBi/onlinePrimalDual_rampMetering	true	true	false	none
https://paperswithcode.com/paper/efficient-psf-modeling-with-shopt-jl-a-psf	Efficient PSF Modeling with ShOpt.jl: A PSF Benchmarking Study with JWST NIRCam Imaging	2401.11625	https://arxiv.org/abs/2401.11625v3	https://arxiv.org/pdf/2401.11625v3.pdf	https://github.com/edwardberman/shopt	true	true	true	jax
https://paperswithcode.com/paper/from-clip-to-dino-visual-encoders-shout-in	From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models	2310.08825	https://arxiv.org/abs/2310.08825v3	https://arxiv.org/pdf/2310.08825v3.pdf	https://github.com/yuchenliu98/comm	true	true	true	pytorch
https://paperswithcode.com/paper/metra-scalable-unsupervised-rl-with-metric	METRA: Scalable Unsupervised RL with Metric-Aware Abstraction	2310.08887	https://arxiv.org/abs/2310.08887v2	https://arxiv.org/pdf/2310.08887v2.pdf	https://github.com/seohongpark/metra	true	true	false	tf
https://paperswithcode.com/paper/self-storm-deep-unrolled-self-supervised	Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy	2403.16974	https://arxiv.org/abs/2403.16974v1	https://arxiv.org/pdf/2403.16974v1.pdf	https://github.com/yairbs/Self-STORM	true	false	true	pytorch
https://paperswithcode.com/paper/comparative-analysis-of-llama-and-chatgpt	Can Large Language Models Understand Molecules?	2402.00024	https://arxiv.org/abs/2402.00024v3	https://arxiv.org/pdf/2402.00024v3.pdf	https://github.com/sshaghayeghs/llama-vs-gpt	true	true	false	none
https://paperswithcode.com/paper/cosmos-web-the-over-abundance-and-physical	COSMOS-Web: The over-abundance and physical nature of "little red dots"--Implications for early galaxy and SMBH assembly	2406.10341	https://arxiv.org/abs/2406.10341v1	https://arxiv.org/pdf/2406.10341v1.pdf	https://github.com/hollisakins/akins24_cw	true	true	true	none
https://paperswithcode.com/paper/leveraging-adversarial-detection-to-enable	BreakHammer: Enhancing RowHammer Mitigations by Carefully Throttling Suspect Threads	2404.13477	https://arxiv.org/abs/2404.13477v2	https://arxiv.org/pdf/2404.13477v2.pdf	https://github.com/cmu-safari/breakhammer	true	true	true	none
https://paperswithcode.com/paper/crossmoco-multi-modal-momentum-contrastive	CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud	null	https://ieeexplore.ieee.org/abstract/document/10229841	https://ieeexplore.ieee.org/abstract/document/10229841	https://github.com/snehaputul/CrossMoCo	false	true	false	pytorch
https://paperswithcode.com/paper/apollo-unified-adapter-and-prompt-learning	APoLLo: Unified Adapter and Prompt Learning for Vision Language Models	2312.01564	https://arxiv.org/abs/2312.01564v1	https://arxiv.org/pdf/2312.01564v1.pdf	https://github.com/schowdhury671/APoLLo	false	false	false	pytorch
https://paperswithcode.com/paper/look-at-me-no-replay-surprisenet-anomaly	Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning	2310.20052	https://arxiv.org/abs/2310.20052v1	https://arxiv.org/pdf/2310.20052v1.pdf	https://github.com/tachyonicclock/surprisenet-cikm-23	true	true	true	pytorch
https://paperswithcode.com/paper/histopathological-image-analysis-with-style	Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization	2310.20638	https://arxiv.org/abs/2310.20638v1	https://arxiv.org/pdf/2310.20638v1.pdf	https://github.com/vaibhav-khamankar/fusestyle	true	true	false	pytorch
https://paperswithcode.com/paper/bias-against-93-stigmatized-groups-in-masked	Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks	2306.05550	https://arxiv.org/abs/2306.05550v1	https://arxiv.org/pdf/2306.05550v1.pdf	https://github.com/mooniem/mlms_bias_stigmas	true	true	true	pytorch
https://paperswithcode.com/paper/prefix-tree-decoding-for-predicting-mass	Prefix-Tree Decoding for Predicting Mass Spectra from Molecules	2303.06470	https://arxiv.org/abs/2303.06470v3	https://arxiv.org/pdf/2303.06470v3.pdf	https://github.com/samgoldman97/ms-pred	true	true	true	pytorch
https://paperswithcode.com/paper/dataset-and-benchmark-for-urdu-natural-scenes	Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering	2405.12533	https://arxiv.org/abs/2405.12533v1	https://arxiv.org/pdf/2405.12533v1.pdf	https://github.com/hiba-meiruan/urdu-vqa-dataset-	true	true	true	none
https://paperswithcode.com/paper/mind-the-gap-between-prototypes-and-images-in	Mind the Gap Between Prototypes and Images in Cross-domain Finetuning	2410.12474	https://arxiv.org/abs/2410.12474v2	https://arxiv.org/pdf/2410.12474v2.pdf	https://github.com/tmlr-group/CoPA	true	true	true	tf
https://paperswithcode.com/paper/aligning-llms-with-domain-invariant-reward	Aligning LLMs with Domain Invariant Reward Models	2501.00911	https://arxiv.org/abs/2501.00911v1	https://arxiv.org/pdf/2501.00911v1.pdf	https://github.com/portal-cornell/dial	true	true	true	pytorch
https://paperswithcode.com/paper/data-driven-study-of-composition-dependent	Data-driven study of composition-dependent phase compatibility in NiTi shape memory alloys	2402.12520	https://arxiv.org/abs/2402.12520v1	https://arxiv.org/pdf/2402.12520v1.pdf	https://github.com/sinazadeh/phase-compatibility-model-niti	true	true	false	none
https://paperswithcode.com/paper/universal-representation-learning-from	Universal Representation Learning from Multiple Domains for Few-shot Classification	2103.13841	https://arxiv.org/abs/2103.13841v1	https://arxiv.org/pdf/2103.13841v1.pdf	https://github.com/tmlr-group/CoPA	false	false	true	tf
https://paperswithcode.com/paper/meta-dataset-a-dataset-of-datasets-for	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	1903.03096	https://arxiv.org/abs/1903.03096v4	https://arxiv.org/pdf/1903.03096v4.pdf	https://github.com/tmlr-group/CoPA	false	false	true	tf
https://paperswithcode.com/paper/unleashing-the-creative-mind-language-model	Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving	2311.00694	https://arxiv.org/abs/2311.00694v2	https://arxiv.org/pdf/2311.00694v2.pdf	https://github.com/lz1oceani/llm-as-hierarchical-policy	true	true	true	none
https://paperswithcode.com/paper/event-causality-is-key-to-computational-story	Event Causality Is Key to Computational Story Understanding	2311.09648	https://arxiv.org/abs/2311.09648v2	https://arxiv.org/pdf/2311.09648v2.pdf	https://github.com/insundaycathy/event-causality-extraction	true	true	true	pytorch
https://paperswithcode.com/paper/chemplaskin-a-general-purpose-program-for	ChemPlasKin: a general-purpose program for unified gas and plasma kinetics simulations	2405.04224	https://arxiv.org/abs/2405.04224v1	https://arxiv.org/pdf/2405.04224v1.pdf	https://github.com/ShaoX96/ChemPlasKin	true	false	true	none
https://paperswithcode.com/paper/distributed-statistical-machine-learning-in	Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent	1705.05491	http://arxiv.org/abs/1705.05491v2	http://arxiv.org/pdf/1705.05491v2.pdf	https://github.com/bladesteam/blades	false	false	true	pytorch
https://paperswithcode.com/paper/smaller-language-models-are-capable-of	Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models	2402.10430	https://arxiv.org/abs/2402.10430v1	https://arxiv.org/pdf/2402.10430v1.pdf	https://github.com/dheeraj7596/small2large	true	true	true	pytorch
https://paperswithcode.com/paper/hierspeech-bridging-the-gap-between-semantic	HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis	2311.12454	https://arxiv.org/abs/2311.12454v2	https://arxiv.org/pdf/2311.12454v2.pdf	https://github.com/sh-lee-prml/hierspeechpp	true	true	true	pytorch
https://paperswithcode.com/paper/readme-bridging-medical-jargon-and-lay	README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP	2312.15561	https://arxiv.org/abs/2312.15561v5	https://arxiv.org/pdf/2312.15561v5.pdf	https://github.com/seasonyao/noteaid-readme	true	true	false	none
https://paperswithcode.com/paper/unsupervised-discovery-of-interpretable-3	Unsupervised discovery of Interpretable Visual Concepts	2309.00018	https://arxiv.org/abs/2309.00018v2	https://arxiv.org/pdf/2309.00018v2.pdf	https://github.com/carolmazini/unsupervised-ivc	true	true	false	pytorch
https://paperswithcode.com/paper/wear-a-multimodal-dataset-for-wearable-and	WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition	2304.05088	https://arxiv.org/abs/2304.05088v4	https://arxiv.org/pdf/2304.05088v4.pdf	https://github.com/mariusbock/wear	true	false	true	pytorch
https://paperswithcode.com/paper/bridging-the-gap-between-domain-specific	Bridging the Gap Between Domain-specific Frameworks and Multiple Hardware Devices	2405.12491	https://arxiv.org/abs/2405.12491v1	https://arxiv.org/pdf/2405.12491v1.pdf	https://github.com/benchcouncil/bridger	true	true	false	none
https://paperswithcode.com/paper/mm-sap-a-comprehensive-benchmark-for	MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception	2401.07529	https://arxiv.org/abs/2401.07529v3	https://arxiv.org/pdf/2401.07529v3.pdf	https://github.com/yhwmz/mm-sap	true	true	true	none
https://paperswithcode.com/paper/asgir-audio-spectrogram-transformer-guided	ASGIR: Audio Spectrogram Transformer Guided Classification And Information Retrieval For Birds	2407.18927	https://arxiv.org/abs/2407.18927v1	https://arxiv.org/pdf/2407.18927v1.pdf	https://github.com/mainsample1234/as-gir	true	true	false	pytorch
https://paperswithcode.com/paper/gohberg-semencul-estimation-of-toeplitz	Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses	2311.14995	https://arxiv.org/abs/2311.14995v1	https://arxiv.org/pdf/2311.14995v1.pdf	https://github.com/beneboeck/toep-cov-estimation	true	true	false	none
https://paperswithcode.com/paper/a-self-attentive-model-for-knowledge-tracing	A Self-Attentive model for Knowledge Tracing	1907.06837	https://arxiv.org/abs/1907.06837v1	https://arxiv.org/pdf/1907.06837v1.pdf	https://github.com/nanzhaogang/contrib/tree/master/application/a-self-attentive-model-for-knowledge-tracing	false	false	false	mindspore
https://paperswithcode.com/paper/zo-adamu-optimizer-adapting-perturbation-by	ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization	2312.15184	https://arxiv.org/abs/2312.15184v1	https://arxiv.org/pdf/2312.15184v1.pdf	https://github.com/mathisall/zo-adamu	true	true	false	pytorch
https://paperswithcode.com/paper/grokformer-graph-fourier-kolmogorov-arnold	GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers	2411.17296	https://arxiv.org/abs/2411.17296v1	https://arxiv.org/pdf/2411.17296v1.pdf	https://github.com/GGA23/GrokFormer	true	false	false	pytorch
https://paperswithcode.com/paper/fine-tuning-language-models-with-just-forward-1	Fine-Tuning Language Models with Just Forward Passes	2305.17333	https://arxiv.org/abs/2305.17333v3	https://arxiv.org/pdf/2305.17333v3.pdf	https://github.com/mathisall/zo-adamu	false	false	true	pytorch
https://paperswithcode.com/paper/sfpnet-sparse-focal-point-network-for	SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds	2407.11569	https://arxiv.org/abs/2407.11569v1	https://arxiv.org/pdf/2407.11569v1.pdf	https://github.com/Cavendish518/SFPNet	true	true	true	pytorch
https://paperswithcode.com/paper/towards-non-robocentric-dynamic-landing-of	Towards Non-Robocentric Dynamic Landing of Quadrotor UAVs	2401.11445	https://arxiv.org/abs/2401.11445v1	https://arxiv.org/pdf/2401.11445v1.pdf	https://github.com/hkpolyu-uav/alan	true	true	false	none
https://paperswithcode.com/paper/couler-unified-machine-learning-workflow	Couler: Unified Machine Learning Workflow Optimization in Cloud	2403.07608	https://arxiv.org/abs/2403.07608v1	https://arxiv.org/pdf/2403.07608v1.pdf	https://github.com/couler-proj/couler	true	true	true	none
https://paperswithcode.com/paper/the-mass-profiles-of-dwarf-galaxies-from-dark	The mass profiles of dwarf galaxies from Dark Energy Survey lensing	2311.14659	https://arxiv.org/abs/2311.14659v1	https://arxiv.org/pdf/2311.14659v1.pdf	https://github.com/aamon/dwarf-lensing	true	true	true	none
https://paperswithcode.com/paper/rapid-training-of-deep-neural-networks	Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping	2110.01765	https://arxiv.org/abs/2110.01765v1	https://arxiv.org/pdf/2110.01765v1.pdf	https://github.com/ml-jku/convex-init	false	false	true	pytorch
https://paperswithcode.com/paper/wav2vec-2-0-a-framework-for-self-supervised	wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations	2006.11477	https://arxiv.org/abs/2006.11477v3	https://arxiv.org/pdf/2006.11477v3.pdf	https://github.com/sh-lee-prml/hierspeechpp	false	false	true	pytorch

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.