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classes | framework
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|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/parallel-and-external-memory-construction-of
|
Parallel and External-Memory Construction of Minimal Perfect Hash Functions with PTHash
|
2106.02350
|
https://arxiv.org/abs/2106.02350v2
|
https://arxiv.org/pdf/2106.02350v2.pdf
|
https://github.com/jermp/pthash
| true | true | true |
none
|
https://paperswithcode.com/paper/open-amp-synthetic-data-framework-for-audio
|
Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models
|
2411.14972
|
https://arxiv.org/abs/2411.14972v1
|
https://arxiv.org/pdf/2411.14972v1.pdf
|
https://github.com/Alec-Wright/OpenAmp
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/efficient-sequence-transduction-by-jointly
|
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
|
2304.06795
|
https://arxiv.org/abs/2304.06795v2
|
https://arxiv.org/pdf/2304.06795v2.pdf
|
https://github.com/chimechallenge/C8DASR-Baseline-NeMo
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/investigating-the-corruption-robustness-of
|
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
|
2305.05400
|
https://arxiv.org/abs/2305.05400v4
|
https://arxiv.org/pdf/2305.05400v4.pdf
|
https://github.com/georgsiedel/lp-norm-corruption-robustness
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/high-expectations-an-observational-study-of
|
High Expectations: An Observational Study of Programming and Cannabis Intoxication
|
2402.19194
|
https://arxiv.org/abs/2402.19194v1
|
https://arxiv.org/pdf/2402.19194v1.pdf
|
https://github.com/cellocorgi/cannabisobservationalstudy
| true | true | false |
none
|
https://paperswithcode.com/paper/how-to-evaluate-your-medical-time-series
|
How to evaluate your medical time series classification?
|
2410.03057
|
https://arxiv.org/abs/2410.03057v1
|
https://arxiv.org/pdf/2410.03057v1.pdf
|
https://github.com/dl4mhealth/medts_evaluation
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/oneformer-one-transformer-to-rule-universal
|
OneFormer: One Transformer to Rule Universal Image Segmentation
|
2211.06220
|
https://arxiv.org/abs/2211.06220v2
|
https://arxiv.org/pdf/2211.06220v2.pdf
|
https://github.com/yangyucheng000/University/tree/main/model-1/oneformer
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/generalized-out-of-distribution-detection-a
|
Generalized Out-of-Distribution Detection: A Survey
|
2110.11334
|
https://arxiv.org/abs/2110.11334v3
|
https://arxiv.org/pdf/2110.11334v3.pdf
|
https://github.com/antoinedemathelin/openood
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/openood-benchmarking-generalized-out-of
|
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
|
2210.07242
|
https://arxiv.org/abs/2210.07242v1
|
https://arxiv.org/pdf/2210.07242v1.pdf
|
https://github.com/antoinedemathelin/openood
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/icassp-2023-acoustic-echo-cancellation
|
ICASSP 2023 Acoustic Echo Cancellation Challenge
|
2309.12553
|
https://arxiv.org/abs/2309.12553v1
|
https://arxiv.org/pdf/2309.12553v1.pdf
|
https://github.com/microsoft/AEC-Challenge
| true | true | false |
none
|
https://paperswithcode.com/paper/rsc-snn-exploring-the-trade-off-between
|
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
|
2407.20099
|
https://arxiv.org/abs/2407.20099v1
|
https://arxiv.org/pdf/2407.20099v1.pdf
|
https://github.com/KemingWu/RSC-SNN
| true | true | false |
none
|
https://paperswithcode.com/paper/on-the-efficacy-of-sampling-adapters
|
On the Efficacy of Sampling Adapters
|
2307.03749
|
https://arxiv.org/abs/2307.03749v2
|
https://arxiv.org/pdf/2307.03749v2.pdf
|
https://github.com/rycolab/sampling-adapters
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/parcorfull2-0-a-parallel-corpus-annotated
|
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference
| null |
https://aclanthology.org/2022.lrec-1.85
|
https://aclanthology.org/2022.lrec-1.85.pdf
|
https://github.com/chardmeier/parcor-full
| true | true | false |
none
|
https://paperswithcode.com/paper/cracking-the-code-of-negative-transfer-a
|
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation
|
2311.13188
|
https://arxiv.org/abs/2311.13188v1
|
https://arxiv.org/pdf/2311.13188v1.pdf
|
https://github.com/cpark88/CGRec
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/dataset-level-attribute-leakage-in
|
Leakage of Dataset Properties in Multi-Party Machine Learning
|
2006.07267
|
https://arxiv.org/abs/2006.07267v3
|
https://arxiv.org/pdf/2006.07267v3.pdf
|
https://github.com/epfl-dlab/property-inference-attacks
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/the-deep-generative-decoder-using-map-1
|
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
|
2110.06672
|
https://arxiv.org/abs/2110.06672v3
|
https://arxiv.org/pdf/2110.06672v3.pdf
|
https://github.com/center-for-health-data-science/dgd_paper
| false | true | true |
pytorch
|
https://paperswithcode.com/paper/seer-language-instructed-video-prediction
|
Seer: Language Instructed Video Prediction with Latent Diffusion Models
|
2303.14897
|
https://arxiv.org/abs/2303.14897v3
|
https://arxiv.org/pdf/2303.14897v3.pdf
|
https://github.com/seervideodiffusion/SeerVideoLDM
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/fast-model-inference-and-training-on-board-of
|
Fast model inference and training on-board of Satellites
|
2307.08700
|
https://arxiv.org/abs/2307.08700v1
|
https://arxiv.org/pdf/2307.08700v1.pdf
|
https://github.com/previtus/ravaen-unibap-dorbit
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/unifying-token-and-span-level-supervisions
|
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
|
2307.07946
|
https://arxiv.org/abs/2307.07946v2
|
https://arxiv.org/pdf/2307.07946v2.pdf
|
https://github.com/zifengcheng/cdap
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/nonlinear-wave-damping-by-kelvin-helmholtz
|
Nonlinear wave damping by Kelvin-Helmholtz instability induced turbulence
|
2308.02217
|
https://arxiv.org/abs/2308.02217v2
|
https://arxiv.org/pdf/2308.02217v2.pdf
|
https://github.com/AstroSnow/PIP
| true | true | false |
none
|
https://paperswithcode.com/paper/sample-factory-egocentric-3d-control-from
|
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
|
2006.11751
|
https://arxiv.org/abs/2006.11751v2
|
https://arxiv.org/pdf/2006.11751v2.pdf
|
https://github.com/pervasive-ai-lab/nle-language-wrapper
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/gradient-free-training-of-neural-odes-for
|
Gradient-free training of neural ODEs for system identification and control using ensemble Kalman inversion
|
2307.07882
|
https://arxiv.org/abs/2307.07882v1
|
https://arxiv.org/pdf/2307.07882v1.pdf
|
https://gitlab.com/computationalscience/eki-neural-ode
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/pomdp-inference-and-robust-solution-via-deep
|
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance
|
2307.08082
|
https://arxiv.org/abs/2307.08082v1
|
https://arxiv.org/pdf/2307.08082v1.pdf
|
https://github.com/giarcieri/robust-optimal-maintenance-planning-through-reinforcement-learning-and-rllib
| true | true | false |
none
|
https://paperswithcode.com/paper/energy-dependent-implementation-of-secondary
|
Energy-dependent implementation of secondary electron emission models in continuum kinetic sheath simulations
|
2311.02689
|
https://arxiv.org/abs/2311.02689v2
|
https://arxiv.org/pdf/2311.02689v2.pdf
|
https://github.com/ammarhakim/gkyl-paper-inp
| true | true | false |
none
|
https://paperswithcode.com/paper/spiral-an-efficient-algorithm-for-the
|
SPIRAL: An Efficient Algorithm for the Integration of the Equation of Rotational Motion
|
2311.04106
|
https://arxiv.org/abs/2311.04106v1
|
https://arxiv.org/pdf/2311.04106v1.pdf
|
https://github.com/cdelv/algorithmsforrotationalmotion
| true | true | false |
none
|
https://paperswithcode.com/paper/disentangling-writer-and-character-styles-for
|
Disentangling Writer and Character Styles for Handwriting Generation
|
2303.14736
|
https://arxiv.org/abs/2303.14736v2
|
https://arxiv.org/pdf/2303.14736v2.pdf
|
https://github.com/dailenson/sdt
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/carousel-phase-retrieval-algorithm-for-3d
|
Real time 3D coherent X-ray diffraction imaging
|
2402.05283
|
https://arxiv.org/abs/2402.05283v3
|
https://arxiv.org/pdf/2402.05283v3.pdf
|
https://github.com/UCSD-CEM/Carousel-Phase-Retrieval-Algorithm
| true | true | false |
none
|
https://paperswithcode.com/paper/improving-transformer-based-image-matching-by
|
Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints
|
2303.02885
|
https://arxiv.org/abs/2303.02885v2
|
https://arxiv.org/pdf/2303.02885v2.pdf
|
https://github.com/ewrfcas/casmtr
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/the-sequence-to-sequence-baseline-for-the
|
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
|
2010.02434
|
https://arxiv.org/abs/2010.02434v1
|
https://arxiv.org/pdf/2010.02434v1.pdf
|
https://github.com/MindSpore-paper-code-3/code2/tree/main/crnn_seq2seq_ocr
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/a-survey-on-learning-from-imbalanced-data
|
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
|
2204.03719
|
https://arxiv.org/abs/2204.03719v2
|
https://arxiv.org/pdf/2204.03719v2.pdf
|
https://github.com/canoalberto/imbalanced-streams
| true | true | true |
none
|
https://paperswithcode.com/paper/variable-importance-for-causal-forests
|
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
|
2308.03369
|
https://arxiv.org/abs/2308.03369v1
|
https://arxiv.org/pdf/2308.03369v1.pdf
|
https://gitlab.com/cbenard/grf-vimp
| true | true | false |
none
|
https://paperswithcode.com/paper/generalization-error-of-generalized-linear
|
Generalization Error of Generalized Linear Models in High Dimensions
|
2005.00180
|
https://arxiv.org/abs/2005.00180v1
|
https://arxiv.org/pdf/2005.00180v1.pdf
|
https://github.com/crowd-ai-lab/generating-figure-captions-as-a-text-summarization-task
| false | false | true |
none
|
https://paperswithcode.com/paper/measure-theoretic-time-delay-embedding
|
Measure-Theoretic Time-Delay Embedding
|
2409.08768
|
https://arxiv.org/abs/2409.08768v1
|
https://arxiv.org/pdf/2409.08768v1.pdf
|
https://github.com/jrbotvinick/Measure-Theoretic-Time-Delay-Embedding
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/cross-domain-product-representation-learning
|
Cross-Domain Product Representation Learning for Rich-Content E-Commerce
|
2308.05550
|
https://arxiv.org/abs/2308.05550v1
|
https://arxiv.org/pdf/2308.05550v1.pdf
|
https://github.com/adxcreative/cope
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/revisiting-domain-adaptive-3d-object
|
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
|
2307.07944
|
https://arxiv.org/abs/2307.07944v3
|
https://arxiv.org/pdf/2307.07944v3.pdf
|
https://github.com/zhuoxiao-chen/redb-da-3ddet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/haplotype-based-variant-detection-from-short
|
Haplotype-based variant detection from short-read sequencing
|
1207.3907
|
http://arxiv.org/abs/1207.3907v2
|
http://arxiv.org/pdf/1207.3907v2.pdf
|
https://github.com/ekg/freebayes
| true | true | true |
none
|
https://paperswithcode.com/paper/towards-understanding-adversarial
|
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
|
2307.07873
|
https://arxiv.org/abs/2307.07873v7
|
https://arxiv.org/pdf/2307.07873v7.pdf
|
https://github.com/cgcl-codes/transferattacksurrogates
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/trojdiff-trojan-attacks-on-diffusion-models
|
TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets
|
2303.05762
|
https://arxiv.org/abs/2303.05762v1
|
https://arxiv.org/pdf/2303.05762v1.pdf
|
https://github.com/mikiyaxi/watermark-audio-diffusion
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/progressive-distillation-for-fast-sampling-of-1
|
Progressive Distillation for Fast Sampling of Diffusion Models
|
2202.00512
|
https://arxiv.org/abs/2202.00512v2
|
https://arxiv.org/pdf/2202.00512v2.pdf
|
https://github.com/viniciusmikuni/caloscorev2
| false | false | true |
tf
|
https://paperswithcode.com/paper/caloscore-v2-single-shot-calorimeter-shower
|
CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
|
2308.03847
|
https://arxiv.org/abs/2308.03847v1
|
https://arxiv.org/pdf/2308.03847v1.pdf
|
https://github.com/viniciusmikuni/caloscorev2
| true | true | false |
tf
|
https://paperswithcode.com/paper/dynamic-landslide-susceptibility-mapping-over
|
Dynamic landslide susceptibility mapping over recent three decades to uncover variations in landslide causes in subtropical urban mountainous areas
|
2308.11929
|
https://arxiv.org/abs/2308.11929v1
|
https://arxiv.org/pdf/2308.11929v1.pdf
|
https://github.com/cli-de/d_lsm
| true | true | true |
tf
|
https://paperswithcode.com/paper/circuit-as-set-of-points-1
|
Circuit as Set of Points
|
2310.17418
|
https://arxiv.org/abs/2310.17418v1
|
https://arxiv.org/pdf/2310.17418v1.pdf
|
https://github.com/hustvl/circuitformer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/quantifying-nonradiative-recombination-and
|
Quantifying Nonradiative Recombination and Resistive Losses in Perovskite Photovoltaics: A Modified Diode Model Approach
|
2311.17442
|
https://arxiv.org/abs/2311.17442v2
|
https://arxiv.org/pdf/2311.17442v2.pdf
|
https://github.com/wpt-lab124/modified-diode-model
| true | true | false |
none
|
https://paperswithcode.com/paper/contrastive-latent-variable-models-for-neural
|
Contrastive Latent Variable Models for Neural Text Generation
| null |
https://proceedings.mlr.press/v180/teng22a.html
|
https://proceedings.mlr.press/v180/teng22a.html
|
https://github.com/2024-MindSpore-1/Code2/tree/main/tengzhiyang/contrastive_vae_mindspore-main
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/what-to-do-when-things-get-crowded-scalable
|
What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals
|
2308.06318
|
https://arxiv.org/abs/2308.06318v1
|
https://arxiv.org/pdf/2308.06318v1.pdf
|
https://github.com/undark-lab/swyft
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/taming-differentiable-logics-with-coq
|
Taming Differentiable Logics with Coq Formalisation
|
2403.13700
|
https://arxiv.org/abs/2403.13700v2
|
https://arxiv.org/pdf/2403.13700v2.pdf
|
https://github.com/math-comp/analysis
| true | true | false |
none
|
https://paperswithcode.com/paper/ravss-robust-audio-visual-speech-separation
|
RAVSS: Robust Audio-Visual Speech Separation in Multi-Speaker Scenarios with Missing Visual Cues
|
2407.19224
|
https://arxiv.org/abs/2407.19224v2
|
https://arxiv.org/pdf/2407.19224v2.pdf
|
https://github.com/pantianrui/RAVSS
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/notation3-as-an-existential-rule-language
|
Existential Notation3 Logic
|
2308.07332
|
https://arxiv.org/abs/2308.07332v2
|
https://arxiv.org/pdf/2308.07332v2.pdf
|
https://github.com/smennicke/n32rules
| true | true | false |
none
|
https://paperswithcode.com/paper/a-unified-query-based-paradigm-for
|
A Unified Query-based Paradigm for Camouflaged Instance Segmentation
|
2308.07392
|
https://arxiv.org/abs/2308.07392v2
|
https://arxiv.org/pdf/2308.07392v2.pdf
|
https://github.com/dongbo811/uqformer
| true | true | false |
none
|
https://paperswithcode.com/paper/jwst-early-release-science-program-templates
|
JWST Early Release Science Program TEMPLATES: Targeting Extremely Magnified Panchromatic Lensed Arcs and their Extended Star formation
|
2312.10465
|
https://arxiv.org/abs/2312.10465v1
|
https://arxiv.org/pdf/2312.10465v1.pdf
|
https://github.com/jwst-templates/notebooks
| true | true | true |
none
|
https://paperswithcode.com/paper/deep-learning-with-plasma-plume-image
|
Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition
|
2312.09133
|
https://arxiv.org/abs/2312.09133v1
|
https://arxiv.org/pdf/2312.09133v1.pdf
|
https://github.com/sumner-harris/Deep-Learning-with-ICCD-Images
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/empowering-large-language-model-for-continual
|
Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting
|
2410.00771
|
https://arxiv.org/abs/2410.00771v2
|
https://arxiv.org/pdf/2410.00771v2.pdf
|
https://github.com/caicch/colpro
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/monocular-human-object-reconstruction-in-the
|
Monocular Human-Object Reconstruction in the Wild
|
2407.20566
|
https://arxiv.org/abs/2407.20566v2
|
https://arxiv.org/pdf/2407.20566v2.pdf
|
https://github.com/huochf/WildHOI
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/deep-symbolic-regression-for-physics-guided
|
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
|
2303.03192
|
https://arxiv.org/abs/2303.03192v2
|
https://arxiv.org/pdf/2303.03192v2.pdf
|
https://github.com/wassimtenachi/physo
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/self-supervised-learning-for-visual
|
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
|
2311.04834
|
https://arxiv.org/abs/2311.04834v1
|
https://arxiv.org/pdf/2311.04834v1.pdf
|
https://github.com/deeplab-ai/selfsupervisedvrd
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-truly-concurrent-semantics-for-reversible
|
A Truly Concurrent Semantics for Reversible CCS
|
2309.14011
|
https://arxiv.org/abs/2309.14011v4
|
https://arxiv.org/pdf/2309.14011v4.pdf
|
https://github.com/hmelgra/reversible-ccs-as-nets
| true | true | false |
none
|
https://paperswithcode.com/paper/depth-self-supervision-for-single-image-novel
|
Depth self-supervision for single image novel view synthesis
|
2308.14108
|
https://arxiv.org/abs/2308.14108v1
|
https://arxiv.org/pdf/2308.14108v1.pdf
|
https://github.com/johnminelli/twowaysynth
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/brain-like-representational-straightening-of
|
Brain-like representational straightening of natural movies in robust feedforward neural networks
|
2308.13870
|
https://arxiv.org/abs/2308.13870v1
|
https://arxiv.org/pdf/2308.13870v1.pdf
|
https://github.com/toosi/brainlike_straightening
| true | true | false |
none
|
https://paperswithcode.com/paper/time-to-pattern-information-theoretic
|
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization
|
2308.13722
|
https://arxiv.org/abs/2308.13722v1
|
https://arxiv.org/pdf/2308.13722v1.pdf
|
https://github.com/alirezaghods/t2p-time-to-pattern
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/spiking-pointnet-spiking-neural-networks-for-1
|
Spiking PointNet: Spiking Neural Networks for Point Clouds
|
2310.06232
|
https://arxiv.org/abs/2310.06232v1
|
https://arxiv.org/pdf/2310.06232v1.pdf
|
https://github.com/dayongren/spiking-pointnet
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/investigation-of-tearing-mode-stability-near
|
Investigation of Tearing Mode Stability Near Ideal Stability Boundaries Via Asymptotic Matching Techniques
|
2503.24184
|
https://arxiv.org/abs/2503.24184v1
|
https://arxiv.org/pdf/2503.24184v1.pdf
|
https://github.com/rfitzp/TJ
| true | false | false |
none
|
https://paperswithcode.com/paper/response-emergent-analogical-reasoning-in
|
Response: Emergent analogical reasoning in large language models
|
2308.16118
|
https://arxiv.org/abs/2308.16118v2
|
https://arxiv.org/pdf/2308.16118v2.pdf
|
https://github.com/hodeld/emergent_analogies_llm_fork
| true | true | false |
none
|
https://paperswithcode.com/paper/emergent-analogical-reasoning-in-large
|
Emergent Analogical Reasoning in Large Language Models
|
2212.09196
|
https://arxiv.org/abs/2212.09196v3
|
https://arxiv.org/pdf/2212.09196v3.pdf
|
https://github.com/hodeld/emergent_analogies_llm_fork
| false | false | true |
none
|
https://paperswithcode.com/paper/adaptive-multi-modalities-fusion-in
|
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
|
2308.15980
|
https://arxiv.org/abs/2308.15980v1
|
https://arxiv.org/pdf/2308.15980v1.pdf
|
https://github.com/holdenhu/mmsr
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/robust-affine-feature-matching-via-quadratic
|
Robust affine point matching via quadratic assignment on Grassmannians
|
2303.02698
|
https://arxiv.org/abs/2303.02698v5
|
https://arxiv.org/pdf/2303.02698v5.pdf
|
https://github.com/sashakolpakov/rag
| true | true | false |
none
|
https://paperswithcode.com/paper/on-the-pareto-front-of-multilingual-neural
|
On the Pareto Front of Multilingual Neural Machine Translation
| null |
https://openreview.net/forum?id=G7sQlfTzmY
|
https://openreview.net/pdf?id=G7sQlfTzmY
|
https://github.com/pkunlp-icler/paretomnmt
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object
|
FCOS: Fully Convolutional One-Stage Object Detection
|
1904.01355
|
https://arxiv.org/abs/1904.01355v5
|
https://arxiv.org/pdf/1904.01355v5.pdf
|
https://github.com/MindSpore-paper-code-3/code6/tree/main/adelaide_ea
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/semi-supervised-semantic-segmentation-with-7
|
Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
|
2208.11499
|
https://arxiv.org/abs/2208.11499v3
|
https://arxiv.org/pdf/2208.11499v3.pdf
|
https://github.com/jianlong-yuan/semi-mmseg
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/multi-robot-rendezvous-in-unknown-environment
|
Multi-Robot Rendezvous in Unknown Environment with Limited Communication
|
2405.08345
|
https://arxiv.org/abs/2405.08345v2
|
https://arxiv.org/pdf/2405.08345v2.pdf
|
https://github.com/KunSong-L/Distributed-Multi-Robot-Topological-Map
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/one-phase-batch-update-on-sparse-merkle-trees
|
One-Phase Batch Update on Sparse Merkle Trees for Rollups
|
2310.13328
|
https://arxiv.org/abs/2310.13328v1
|
https://arxiv.org/pdf/2310.13328v1.pdf
|
https://github.com/boqian-ma/one-phase-batch-update-smt
| true | true | true |
none
|
https://paperswithcode.com/paper/a-critical-evaluation-of-ai-feedback-for
|
A Critical Evaluation of AI Feedback for Aligning Large Language Models
|
2402.12366
|
https://arxiv.org/abs/2402.12366v1
|
https://arxiv.org/pdf/2402.12366v1.pdf
|
https://github.com/architsharma97/dpo-rlaif
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/weakly-supervised-learning-for-breast-cancer
|
Case-level Breast Cancer Prediction for Real Hospital Settings
|
2310.12677
|
https://arxiv.org/abs/2310.12677v2
|
https://arxiv.org/pdf/2310.12677v2.pdf
|
https://github.com/shreyasipathak/multiinstance-learning-mammography
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/aniportrait-audio-driven-synthesis-of
|
AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation
|
2403.17694
|
https://arxiv.org/abs/2403.17694v1
|
https://arxiv.org/pdf/2403.17694v1.pdf
|
https://github.com/zejun-yang/aniportrait
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unlocking-fine-grained-details-with-wavelet
|
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
|
2308.13442
|
https://arxiv.org/abs/2308.13442v2
|
https://arxiv.org/pdf/2308.13442v2.pdf
|
https://github.com/mindflow-institue/ssct
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/efficient-multi-view-graph-clustering-with
|
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
|
2309.00024
|
https://arxiv.org/abs/2309.00024v1
|
https://arxiv.org/pdf/2309.00024v1.pdf
|
https://github.com/wy1019/emvgc-lg
| true | true | false |
none
|
https://paperswithcode.com/paper/nemig-a-bilingual-news-collection-and
|
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
|
2309.00550
|
https://arxiv.org/abs/2309.00550v1
|
https://arxiv.org/pdf/2309.00550v1.pdf
|
https://github.com/andreeaiana/nemig
| true | true | false |
none
|
https://paperswithcode.com/paper/chart-what-i-say-exploring-cross-modality
|
Chart What I Say: Exploring Cross-Modality Prompt Alignment in AI-Assisted Chart Authoring
|
2404.05103
|
https://arxiv.org/abs/2404.05103v1
|
https://arxiv.org/pdf/2404.05103v1.pdf
|
https://github.com/cookielab-uoft/voice-chart-authoring-instructions-dataset
| true | true | true |
none
|
https://paperswithcode.com/paper/an-automatic-system-for-acoustic-microphone
|
An Automatic System for Acoustic Microphone Geometry Calibration based on Minimal Solvers
|
1610.02392
|
https://arxiv.org/abs/1610.02392v1
|
https://arxiv.org/pdf/1610.02392v1.pdf
|
https://github.com/kalleastrom/structurefromsound2
| false | false | true |
none
|
https://paperswithcode.com/paper/layout-and-task-aware-instruction-prompt-for
|
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
|
2306.00526
|
https://arxiv.org/abs/2306.00526v4
|
https://arxiv.org/pdf/2306.00526v4.pdf
|
https://github.com/wenjinw/latin-prompt
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/knowledge-graph-reasoning-with-relational
|
Knowledge Graph Reasoning with Relational Digraph
|
2108.06040
|
https://arxiv.org/abs/2108.06040v2
|
https://arxiv.org/pdf/2108.06040v2.pdf
|
https://github.com/lars-research/red-gnn
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/disentangling-long-short-term-state-under
|
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
|
2502.12603
|
https://arxiv.org/abs/2502.12603v1
|
https://arxiv.org/pdf/2502.12603v1.pdf
|
https://github.com/DMIRLAB-Group/LSTD
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/anovl-adapting-vision-language-models-for
|
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
|
2308.15939
|
https://arxiv.org/abs/2308.15939v2
|
https://arxiv.org/pdf/2308.15939v2.pdf
|
https://github.com/hq-deng/AnoVL
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/gmai-vl-r1-harnessing-reinforcement-learning
|
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
|
2504.01886
|
https://arxiv.org/abs/2504.01886v1
|
https://arxiv.org/pdf/2504.01886v1.pdf
|
https://github.com/uni-medical/gmai-vl-r1
| true | true | true |
none
|
https://paperswithcode.com/paper/large-window-based-mamba-unet-for-medical
|
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
|
2403.07332
|
https://arxiv.org/abs/2403.07332v2
|
https://arxiv.org/pdf/2403.07332v2.pdf
|
https://github.com/wjh892521292/lkm-unet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/toward-deep-drum-source-separation
|
Toward Deep Drum Source Separation
|
2312.09663
|
https://arxiv.org/abs/2312.09663v3
|
https://arxiv.org/pdf/2312.09663v3.pdf
|
https://github.com/polimi-ispl/larsnet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/mila-memory-based-instance-level-adaptation-1
|
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
|
2309.01086
|
https://arxiv.org/abs/2309.01086v1
|
https://arxiv.org/pdf/2309.01086v1.pdf
|
https://github.com/hitachi-rd-cv/MILA
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/conda-contrastive-domain-adaptation-for-ai
|
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
|
2309.03992
|
https://arxiv.org/abs/2309.03992v2
|
https://arxiv.org/pdf/2309.03992v2.pdf
|
https://github.com/amritabh/conda-gen-text-detection
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/scalable-learning-of-intrusion-responses
|
Scalable Learning of Intrusion Responses through Recursive Decomposition
|
2309.03292
|
https://arxiv.org/abs/2309.03292v2
|
https://arxiv.org/pdf/2309.03292v2.pdf
|
https://github.com/limmen/csle
| true | true | true |
none
|
https://paperswithcode.com/paper/complex-yolo-real-time-3d-object-detection-on
|
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
|
1803.06199
|
http://arxiv.org/abs/1803.06199v2
|
http://arxiv.org/pdf/1803.06199v2.pdf
|
https://github.com/wwooo/tensorflow_complex_yolo
| false | false | true |
tf
|
https://paperswithcode.com/paper/generalized-policy-improvement-algorithms
|
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
|
2206.13714
|
https://arxiv.org/abs/2206.13714v3
|
https://arxiv.org/pdf/2206.13714v3.pdf
|
https://github.com/jqueeney/gpi
| true | true | true |
tf
|
https://paperswithcode.com/paper/fibinet-combining-feature-importance-and
|
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
|
1905.09433
|
https://arxiv.org/abs/1905.09433v1
|
https://arxiv.org/pdf/1905.09433v1.pdf
|
https://github.com/HaSai666/rec_pangu
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/masknet-introducing-feature-wise
|
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
|
2102.07619
|
https://arxiv.org/abs/2102.07619v2
|
https://arxiv.org/pdf/2102.07619v2.pdf
|
https://github.com/HaSai666/rec_pangu
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/effective-lstms-for-target-dependent
|
Effective LSTMs for Target-Dependent Sentiment Classification
|
1512.01100
|
http://arxiv.org/abs/1512.01100v2
|
http://arxiv.org/pdf/1512.01100v2.pdf
|
https://github.com/mindspore-courses/ABSA-MindSpore
| false | false | true |
mindspore
|
https://paperswithcode.com/paper/interactive-attention-networks-for-aspect
|
Interactive Attention Networks for Aspect-Level Sentiment Classification
|
1709.00893
|
http://arxiv.org/abs/1709.00893v1
|
http://arxiv.org/pdf/1709.00893v1.pdf
|
https://github.com/mindspore-courses/ABSA-MindSpore
| false | false | true |
mindspore
|
https://paperswithcode.com/paper/aspect-level-sentiment-classification-with-1
|
Aspect Level Sentiment Classification with Deep Memory Network
|
1605.08900
|
http://arxiv.org/abs/1605.08900v2
|
http://arxiv.org/pdf/1605.08900v2.pdf
|
https://github.com/mindspore-courses/ABSA-MindSpore
| false | false | true |
mindspore
|
https://paperswithcode.com/paper/structured-stochastic-gradient-mcmc
|
Structured Stochastic Gradient MCMC
|
2107.09028
|
https://arxiv.org/abs/2107.09028v4
|
https://arxiv.org/pdf/2107.09028v4.pdf
|
https://github.com/ajboyd2/pytorch_lvi
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/lora-low-rank-adaptation-of-large-language
|
LoRA: Low-Rank Adaptation of Large Language Models
|
2106.09685
|
https://arxiv.org/abs/2106.09685v2
|
https://arxiv.org/pdf/2106.09685v2.pdf
|
https://github.com/qwenlm/qwen-vl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/ctrsvdd-a-benchmark-dataset-and-baseline
|
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
|
2406.02438
|
https://arxiv.org/abs/2406.02438v2
|
https://arxiv.org/pdf/2406.02438v2.pdf
|
https://github.com/svddchallenge/ctrsvdd2024_baseline
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/tilp-differentiable-learning-of-temporal
|
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs
|
2402.12309
|
https://arxiv.org/abs/2402.12309v1
|
https://arxiv.org/pdf/2402.12309v1.pdf
|
https://github.com/xiongsiheng/tilp
| true | true | false |
none
|
https://paperswithcode.com/paper/men-also-do-laundry-multi-attribute-bias
|
Men Also Do Laundry: Multi-Attribute Bias Amplification
|
2210.11924
|
https://arxiv.org/abs/2210.11924v3
|
https://arxiv.org/pdf/2210.11924v3.pdf
|
https://github.com/sonyresearch/multi_bias_amp
| true | true | true |
pytorch
|
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