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https://paperswithcode.com/paper/bayes-catsi-a-variational-bayesian-approach
|
Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
|
2410.01847
|
https://arxiv.org/abs/2410.01847v2
|
https://arxiv.org/pdf/2410.01847v2.pdf
|
https://github.com/pingala-institute/Bayes-medicaldataimputation
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/flash-tb-integrating-arc-flags-and-trip-based
|
FLASH-TB: Integrating Arc-Flags and Trip-Based Public Transit Routing
|
2312.13146
|
https://arxiv.org/abs/2312.13146v3
|
https://arxiv.org/pdf/2312.13146v3.pdf
|
https://github.com/TransitRouting/FLASH-TB
| true | false | false |
none
|
https://paperswithcode.com/paper/cerebrum-aios-sdk-a-platform-for-agent
|
Cerebrum (AIOS SDK): A Platform for Agent Development, Deployment, Distribution, and Discovery
|
2503.11444
|
https://arxiv.org/abs/2503.11444v1
|
https://arxiv.org/pdf/2503.11444v1.pdf
|
https://github.com/agiresearch/cerebrum
| true | true | false |
none
|
https://paperswithcode.com/paper/hifi-sr-a-unified-generative-transformer
|
HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution
|
2501.10045
|
https://arxiv.org/abs/2501.10045v1
|
https://arxiv.org/pdf/2501.10045v1.pdf
|
https://github.com/modelscope/ClearerVoice-Studio
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/low-rank-autoregressive-tensor-completion-for-1
|
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
|
2104.14936
|
https://arxiv.org/abs/2104.14936v1
|
https://arxiv.org/pdf/2104.14936v1.pdf
|
https://github.com/xinychen/transdim
| true | true | true |
tf
|
https://paperswithcode.com/paper/scalable-low-rank-autoregressive-tensor
|
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
|
2008.03194
|
https://arxiv.org/abs/2008.03194v3
|
https://arxiv.org/pdf/2008.03194v3.pdf
|
https://github.com/xinychen/transdim
| true | true | true |
tf
|
https://paperswithcode.com/paper/a-nonconvex-low-rank-tensor-completion-model
|
A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
|
2003.10271
|
https://arxiv.org/abs/2003.10271v2
|
https://arxiv.org/pdf/2003.10271v2.pdf
|
https://github.com/xinychen/transdim
| true | true | true |
tf
|
https://paperswithcode.com/paper/towards-high-resolution-validated-and-open
|
Towards high resolution, validated and open global wind power assessments
|
2501.07937
|
https://arxiv.org/abs/2501.07937v2
|
https://arxiv.org/pdf/2501.07937v2.pdf
|
https://github.com/fzj-iek3-vsa/reskit
| true | true | true |
none
|
https://paperswithcode.com/paper/concise-network-models-of-memory-dynamics
|
Concise network models of memory dynamics reveal explainable patterns in path data
|
2501.08302
|
https://arxiv.org/abs/2501.08302v1
|
https://arxiv.org/pdf/2501.08302v1.pdf
|
https://github.com/rohit-sahasrabuddhe/concise-networks
| true | true | true |
none
|
https://paperswithcode.com/paper/event-based-motion-segmentation-with-spatio
|
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
|
2012.08730
|
https://arxiv.org/abs/2012.08730v3
|
https://arxiv.org/pdf/2012.08730v3.pdf
|
https://github.com/hkust-aerial-robotics/emsgc
| true | true | true |
none
|
https://paperswithcode.com/paper/the-long-tail-of-context-does-it-exist-and
|
The Long Tail of Context: Does it Exist and Matter?
|
2210.01023
|
https://arxiv.org/abs/2210.01023v1
|
https://arxiv.org/pdf/2210.01023v1.pdf
|
https://github.com/sb-ai-lab/RePlay
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/experimental-evidence-of-quantum-drude
|
Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Probabilistic Iterative Boltzmann Inversion
|
2501.06501
|
https://arxiv.org/abs/2501.06501v2
|
https://arxiv.org/pdf/2501.06501v2.pdf
|
https://github.com/hoepfnergroup/SOPR
| true | true | false |
none
|
https://paperswithcode.com/paper/unsupervised-motion-segmentation-for
|
Motion Segmentation for Neuromorphic Aerial Surveillance
|
2405.15209
|
https://arxiv.org/abs/2405.15209v2
|
https://arxiv.org/pdf/2405.15209v2.pdf
|
https://github.com/samiarja/ev_deep_motion_segmentation
| true | false | true |
none
|
https://paperswithcode.com/paper/a-deep-learning-based-approach-for-mangrove
|
A Deep Learning-Based Approach for Mangrove Monitoring
|
2410.05443
|
https://arxiv.org/abs/2410.05443v1
|
https://arxiv.org/pdf/2410.05443v1.pdf
|
https://github.com/svjlucas/mangroveai
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/multi-task-adversarial-variational
|
Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging
|
2411.10100
|
https://arxiv.org/abs/2411.10100v1
|
https://arxiv.org/pdf/2411.10100v1.pdf
|
https://github.com/engrussman/MAVAE
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/the-spatial-complexity-of-optical-computing
|
The Spatial Complexity of Optical Computing and How to Reduce It
|
2411.10435
|
https://arxiv.org/abs/2411.10435v2
|
https://arxiv.org/pdf/2411.10435v2.pdf
|
https://github.com/lyd5039/The-Spatial-Complexity-of-Optical-Computing
| true | false | true |
none
|
https://paperswithcode.com/paper/pursuing-overall-welfare-in-federated
|
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
|
2405.20821
|
https://arxiv.org/abs/2405.20821v2
|
https://arxiv.org/pdf/2405.20821v2.pdf
|
https://github.com/vaseline555/aaggff
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/cas-vit-convolutional-additive-self-attention
|
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
|
2408.03703
|
https://arxiv.org/abs/2408.03703v2
|
https://arxiv.org/pdf/2408.03703v2.pdf
|
https://github.com/tianfang-zhang/cas-vit
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/api-bank-a-benchmark-for-tool-augmented-llms
|
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
|
2304.08244
|
https://arxiv.org/abs/2304.08244v2
|
https://arxiv.org/pdf/2304.08244v2.pdf
|
https://github.com/MadeAgents/Hammer
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/building-interpretable-climate-emulators-for
|
Building Interpretable Climate Emulators for Economics
|
2411.10768
|
https://arxiv.org/abs/2411.10768v2
|
https://arxiv.org/pdf/2411.10768v2.pdf
|
https://github.com/ClimateChangeEcon/Building_Interpretable_Climate_Emulators_forEconomics
| true | false | true |
none
|
https://paperswithcode.com/paper/toolalpaca-generalized-tool-learning-for
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
2306.05301
|
https://arxiv.org/abs/2306.05301v2
|
https://arxiv.org/pdf/2306.05301v2.pdf
|
https://github.com/MadeAgents/Hammer
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/seal-tools-self-instruct-tool-learning
|
Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark
|
2405.08355
|
https://arxiv.org/abs/2405.08355v1
|
https://arxiv.org/pdf/2405.08355v1.pdf
|
https://github.com/MadeAgents/Hammer
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/am-radio-agglomerative-model-reduce-all
|
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
|
2312.06709
|
https://arxiv.org/abs/2312.06709v5
|
https://arxiv.org/pdf/2312.06709v5.pdf
|
https://github.com/nvlabs/radio
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/sensorbench-benchmarking-llms-in-coding-based
|
SensorBench: Benchmarking LLMs in Coding-Based Sensor Processing
|
2410.10741
|
https://arxiv.org/abs/2410.10741v3
|
https://arxiv.org/pdf/2410.10741v3.pdf
|
https://github.com/nesl/llm_sensor_processing
| true | true | true |
none
|
https://paperswithcode.com/paper/newclid-a-user-friendly-replacement-for
|
Newclid: A User-Friendly Replacement for AlphaGeometry
|
2411.11938
|
https://arxiv.org/abs/2411.11938v1
|
https://arxiv.org/pdf/2411.11938v1.pdf
|
https://github.com/lmcrc/newclid
| true | true | false |
none
|
https://paperswithcode.com/paper/multi-adversarial-domain-adaptation
|
Multi-Adversarial Domain Adaptation
|
1809.02176
|
http://arxiv.org/abs/1809.02176v1
|
http://arxiv.org/pdf/1809.02176v1.pdf
|
https://github.com/MindCode-4/code-12/tree/main/multi-adversarial-domain-adaptation
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/distributionally-robust-self-supervised
|
Distributionally robust self-supervised learning for tabular data
|
2410.08511
|
https://arxiv.org/abs/2410.08511v5
|
https://arxiv.org/pdf/2410.08511v5.pdf
|
https://github.com/amazon-science/distributionally-robust-self-supervised-learning-for-tabular-data
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/residual-kolmogorov-arnold-network-for
|
Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
|
2410.05500
|
https://arxiv.org/abs/2410.05500v1
|
https://arxiv.org/pdf/2410.05500v1.pdf
|
https://github.com/withray/residualkan
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/medformer-a-multi-granularity-patching
|
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
|
2405.19363
|
https://arxiv.org/abs/2405.19363v2
|
https://arxiv.org/pdf/2405.19363v2.pdf
|
https://github.com/dl4mhealth/medts_evaluation
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical
|
U-Net: Convolutional Networks for Biomedical Image Segmentation
|
1505.04597
|
http://arxiv.org/abs/1505.04597v1
|
http://arxiv.org/pdf/1505.04597v1.pdf
|
https://github.com/zh320/medical-segmentation-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/optimizing-kv-cache-eviction-in-llms-adaptive
|
Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
|
2407.11550
|
https://arxiv.org/abs/2407.11550v4
|
https://arxiv.org/pdf/2407.11550v4.pdf
|
https://github.com/NVIDIA/kvpress
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/imputation-using-training-labels-and
|
Imputation using training labels and classification via label imputation
|
2311.16877
|
https://arxiv.org/abs/2311.16877v5
|
https://arxiv.org/pdf/2311.16877v5.pdf
|
https://github.com/thunguyen177/iul-cbmi
| true | true | true |
none
|
https://paperswithcode.com/paper/wilddesed-an-llm-powered-dataset-for-wild
|
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
|
2407.03656
|
https://arxiv.org/abs/2407.03656v3
|
https://arxiv.org/pdf/2407.03656v3.pdf
|
https://github.com/swagshaw/wilddesed
| true | true | true |
tf
|
https://paperswithcode.com/paper/infogent-an-agent-based-framework-for-web
|
Infogent: An Agent-Based Framework for Web Information Aggregation
|
2410.19054
|
https://arxiv.org/abs/2410.19054v1
|
https://arxiv.org/pdf/2410.19054v1.pdf
|
https://github.com/gangiswag/infogent
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/pdl-a-declarative-prompt-programming-language
|
PDL: A Declarative Prompt Programming Language
|
2410.19135
|
https://arxiv.org/abs/2410.19135v1
|
https://arxiv.org/pdf/2410.19135v1.pdf
|
https://github.com/IBM/prompt-declaration-language
| true | false | true |
none
|
https://paperswithcode.com/paper/telesim-a-network-aware-testbed-and-benchmark
|
TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications
|
2507.04425
|
https://arxiv.org/abs/2507.04425v1
|
https://arxiv.org/pdf/2507.04425v1.pdf
|
https://github.com/ConnectedRoboticsLab/TeleSim
| true | false | true |
none
|
https://paperswithcode.com/paper/inteliplan-interactive-lightweight-llm-based
|
InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy
|
2409.14506
|
https://arxiv.org/abs/2409.14506v2
|
https://arxiv.org/pdf/2409.14506v2.pdf
|
https://github.com/kimtienly/inteliplan_docker
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/discriminative-finetuning-of-generative-large
|
Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
|
2502.18679
|
https://arxiv.org/abs/2502.18679v2
|
https://arxiv.org/pdf/2502.18679v2.pdf
|
https://github.com/optimization-ai/dft
| true | true | true |
none
|
https://paperswithcode.com/paper/positive-augmented-constrastive-learning-for
|
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation
|
2303.12112
|
https://arxiv.org/abs/2303.12112v3
|
https://arxiv.org/pdf/2303.12112v3.pdf
|
https://github.com/aimagelab/pacscore
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/matrix-encoding-networks-for-neural
|
Matrix Encoding Networks for Neural Combinatorial Optimization
|
2106.11113
|
https://arxiv.org/abs/2106.11113v2
|
https://arxiv.org/pdf/2106.11113v2.pdf
|
https://github.com/kaist-silab/symmetric_replay
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/sample-efficiency-matters-a-benchmark-for
|
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
|
2206.12411
|
https://arxiv.org/abs/2206.12411v2
|
https://arxiv.org/pdf/2206.12411v2.pdf
|
https://github.com/kaist-silab/symmetric_replay
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/seg-r1-segmentation-can-be-surprisingly
|
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
|
2506.22624
|
https://arxiv.org/abs/2506.22624v1
|
https://arxiv.org/pdf/2506.22624v1.pdf
|
https://github.com/geshang777/FOCUS
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/multimodal-latent-language-modeling-with-next
|
Multimodal Latent Language Modeling with Next-Token Diffusion
|
2412.08635
|
https://arxiv.org/abs/2412.08635v1
|
https://arxiv.org/pdf/2412.08635v1.pdf
|
https://github.com/microsoft/unilm/tree/master/LatentLM
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/ferrari-federated-feature-unlearning-via
|
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
|
2405.17462
|
https://arxiv.org/abs/2405.17462v4
|
https://arxiv.org/pdf/2405.17462v4.pdf
|
https://github.com/ongwinkent/federated-feature-unlearning
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/artificial-intelligence-based-triaging-of
|
Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions
|
2410.10509
|
https://arxiv.org/abs/2410.10509v1
|
https://arxiv.org/pdf/2410.10509v1.pdf
|
https://github.com/rtlucassen/melanocytic_lesion_triaging
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/livexiv-a-multi-modal-live-benchmark-based-on
|
LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content
|
2410.10783
|
https://arxiv.org/abs/2410.10783v2
|
https://arxiv.org/pdf/2410.10783v2.pdf
|
https://github.com/nimrodshabtay/livexiv
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/longhalqa-long-context-hallucination
|
LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
|
2410.09962
|
https://arxiv.org/abs/2410.09962v2
|
https://arxiv.org/pdf/2410.09962v2.pdf
|
https://github.com/hanqiu-hq/longhalqa
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/data-centric-foundation-models-in
|
Data-Centric Foundation Models in Computational Healthcare: A Survey
|
2401.02458
|
https://arxiv.org/abs/2401.02458v2
|
https://arxiv.org/pdf/2401.02458v2.pdf
|
https://github.com/yunkun-zhang/data-centric-fm-healthcare
| true | true | true |
none
|
https://paperswithcode.com/paper/attribute-or-abstain-large-language-models-as
|
Attribute or Abstain: Large Language Models as Long Document Assistants
|
2407.07799
|
https://arxiv.org/abs/2407.07799v2
|
https://arxiv.org/pdf/2407.07799v2.pdf
|
https://github.com/ukplab/arxiv2024-attribute-or-abstain
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/apollo-sgd-like-memory-adamw-level
|
APOLLO: SGD-like Memory, AdamW-level Performance
|
2412.05270
|
https://arxiv.org/abs/2412.05270v4
|
https://arxiv.org/pdf/2412.05270v4.pdf
|
https://github.com/zhuhanqing/APOLLO
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/datatractor-metadata-automation-and
|
Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
|
2410.18839
|
https://arxiv.org/abs/2410.18839v2
|
https://arxiv.org/pdf/2410.18839v2.pdf
|
https://github.com/datatractor/yard
| true | true | true |
none
|
https://paperswithcode.com/paper/counter-current-learning-a-biologically
|
Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning
|
2409.19841
|
https://arxiv.org/abs/2409.19841v2
|
https://arxiv.org/pdf/2409.19841v2.pdf
|
https://github.com/iandrover/ccl-neurips24
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/high-quality-animatable-eyelid-shapes-from
|
High-quality Animatable Eyelid Shapes from Lightweight Captures
|
2410.01360
|
https://arxiv.org/abs/2410.01360v1
|
https://arxiv.org/pdf/2410.01360v1.pdf
|
https://github.com/storymy/anieyelid
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-unified-pseudo-c_ell-framework
|
A unified pseudo-$C_\ell$ framework
|
1809.09603
|
http://arxiv.org/abs/1809.09603v2
|
http://arxiv.org/pdf/1809.09603v2.pdf
|
https://github.com/j-hw-wong/swept
| false | false | true |
none
|
https://paperswithcode.com/paper/cosmosis-modular-cosmological-parameter
|
CosmoSIS: modular cosmological parameter estimation
|
1409.3409
|
https://arxiv.org/abs/1409.3409v2
|
https://arxiv.org/pdf/1409.3409v2.pdf
|
https://github.com/j-hw-wong/swept
| false | false | true |
none
|
https://paperswithcode.com/paper/cams-as-shapley-value-based-explainers
|
CAMs as Shapley Value-based Explainers
|
2501.06261
|
https://arxiv.org/abs/2501.06261v1
|
https://arxiv.org/pdf/2501.06261v1.pdf
|
https://github.com/caihuaiguang/pytorch-shapley-cam
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/bandits-with-preference-feedback-a
|
Bandits with Preference Feedback: A Stackelberg Game Perspective
|
2406.16745
|
https://arxiv.org/abs/2406.16745v2
|
https://arxiv.org/pdf/2406.16745v2.pdf
|
https://github.com/lasgroup/maxminlcb
| true | true | true |
jax
|
https://paperswithcode.com/paper/pyrregular-a-unified-framework-for-irregular
|
PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
|
2505.06047
|
https://arxiv.org/abs/2505.06047v1
|
https://arxiv.org/pdf/2505.06047v1.pdf
|
https://github.com/fspinna/pyrregular
| true | true | true |
none
|
https://paperswithcode.com/paper/synomaly-noise-and-multi-stage-diffusion-a
|
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging
|
2411.04004
|
https://arxiv.org/abs/2411.04004v1
|
https://arxiv.org/pdf/2411.04004v1.pdf
|
https://github.com/yuan-12138/synomaly
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/decision-focused-uncertainty-quantification
|
Decision-Focused Uncertainty Quantification
|
2410.01767
|
https://arxiv.org/abs/2410.01767v1
|
https://arxiv.org/pdf/2410.01767v1.pdf
|
https://github.com/cmpatino/utility_driven_prediction
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/distilling-knowledge-for-designing
|
Distilling Knowledge for Designing Computational Imaging Systems
|
2501.17898
|
https://arxiv.org/abs/2501.17898v1
|
https://arxiv.org/pdf/2501.17898v1.pdf
|
https://github.com/leonsuarez24/dkdcis
| true | true | true |
none
|
https://paperswithcode.com/paper/mmearth-exploring-multi-modal-pretext-tasks
|
MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
|
2405.02771
|
https://arxiv.org/abs/2405.02771v2
|
https://arxiv.org/pdf/2405.02771v2.pdf
|
https://github.com/vishalned/MMEarth-data
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/chain-of-thought-prompting-elicits-reasoning
|
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
|
2201.11903
|
https://arxiv.org/abs/2201.11903v6
|
https://arxiv.org/pdf/2201.11903v6.pdf
|
https://github.com/yinzhangyue/AoR
| false | false | true |
none
|
https://paperswithcode.com/paper/enhancing-imbalance-learning-a-novel-slack
|
Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach
|
2411.17128
|
https://arxiv.org/abs/2411.17128v1
|
https://arxiv.org/pdf/2411.17128v1.pdf
|
https://github.com/mtanveer1/isffsvm
| true | true | false |
none
|
https://paperswithcode.com/paper/neur2ro-neural-two-stage-robust-optimization
|
Deep Learning for Two-Stage Robust Integer Optimization
|
2310.04345
|
https://arxiv.org/abs/2310.04345v3
|
https://arxiv.org/pdf/2310.04345v3.pdf
|
https://github.com/khalil-research/neur2ro
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/concept-replacer-replacing-sensitive-concepts
|
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization
|
2412.01244
|
https://arxiv.org/abs/2412.01244v2
|
https://arxiv.org/pdf/2412.01244v2.pdf
|
https://github.com/zhang-lingyun/ConceptReplacer
| true | false | true |
jax
|
https://paperswithcode.com/paper/2407-21315
|
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances
|
2407.21315
|
https://arxiv.org/abs/2407.21315v4
|
https://arxiv.org/pdf/2407.21315v4.pdf
|
https://github.com/zehuiwu/SpeechCueLLM
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/embedding-logical-queries-on-knowledge-graphs
|
Embedding Logical Queries on Knowledge Graphs
|
1806.01445
|
https://arxiv.org/abs/1806.01445v4
|
https://arxiv.org/pdf/1806.01445v4.pdf
|
https://github.com/MindSpore-scientific/code-13/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/fht-map-feature-based-hierarchical
|
FHT-Map: Feature-based Hierarchical Topological Map for Relocalization and Path Planning
|
2310.13899
|
https://arxiv.org/abs/2310.13899v1
|
https://arxiv.org/pdf/2310.13899v1.pdf
|
https://github.com/KunSong-L/Distributed-Multi-Robot-Topological-Map
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/spa-bench-a-comprehensive-benchmark-for
|
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation
|
2410.15164
|
https://arxiv.org/abs/2410.15164v3
|
https://arxiv.org/pdf/2410.15164v3.pdf
|
https://github.com/ai-agents-2030/SPA-Bench
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/wicked-a-simple-method-to-make-multiple
|
WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging
|
2502.18316
|
https://arxiv.org/abs/2502.18316v1
|
https://arxiv.org/pdf/2502.18316v1.pdf
|
https://github.com/ahmedselhady/wicked-benchmarks
| true | true | true |
none
|
https://paperswithcode.com/paper/eltex-a-framework-for-domain-driven-synthetic
|
ELTEX: A Framework for Domain-Driven Synthetic Data Generation
|
2503.15055
|
https://arxiv.org/abs/2503.15055v2
|
https://arxiv.org/pdf/2503.15055v2.pdf
|
https://github.com/1712n/eltex
| true | false | true |
none
|
https://paperswithcode.com/paper/not-all-data-are-unlearned-equally
|
Not All Data Are Unlearned Equally
|
2504.05058
|
https://arxiv.org/abs/2504.05058v2
|
https://arxiv.org/pdf/2504.05058v2.pdf
|
https://github.com/McGill-NLP/unequal-unlearning
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/pytics-an-iterative-method-for-photometric
|
PyTICS: An Iterative Method for Photometric Lightcurve Intercalibration using Comparison Stars
|
2505.23328
|
https://arxiv.org/abs/2505.23328v2
|
https://arxiv.org/pdf/2505.23328v2.pdf
|
https://github.com/astroberta/pytics
| true | true | true |
none
|
https://paperswithcode.com/paper/comparative-analysis-of-transfer-learning
|
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
|
2408.16859
|
https://arxiv.org/abs/2408.16859v2
|
https://arxiv.org/pdf/2408.16859v2.pdf
|
https://github.com/saniaesk/Breast-Cancer-Classification
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/cloud-classification-with-unsupervised-deep
|
Cloud Classification with Unsupervised Deep Learning
|
2209.15585
|
https://arxiv.org/abs/2209.15585v1
|
https://arxiv.org/pdf/2209.15585v1.pdf
|
https://github.com/rdcep/clouds
| false | false | false |
tf
|
https://paperswithcode.com/paper/mst-adaptive-multi-scale-tokens-guided
|
MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation
|
2401.04403
|
https://arxiv.org/abs/2401.04403v2
|
https://arxiv.org/pdf/2401.04403v2.pdf
|
https://github.com/hahamyt/mst
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/accurate-fourier-space-statistics-for-line
|
Accurate Fourier-space statistics for line intensity mapping: Cartesian grid sampling without aliased power
|
2312.07289
|
https://arxiv.org/abs/2312.07289v2
|
https://arxiv.org/pdf/2312.07289v2.pdf
|
https://github.com/stevecunnington/gridimp
| true | true | true |
none
|
https://paperswithcode.com/paper/a-statistical-framework-for-ranking-llm-based
|
A Statistical Framework for Ranking LLM-Based Chatbots
|
2412.18407
|
https://arxiv.org/abs/2412.18407v1
|
https://arxiv.org/pdf/2412.18407v1.pdf
|
https://github.com/suquark/leaderbot
| true | true | true |
none
|
https://paperswithcode.com/paper/exact-and-soft-boundary-conditions-in-physics
|
Exact and soft boundary conditions in Physics-Informed Neural Networks for the Variable Coefficient Poisson equation
|
2310.02548
|
https://arxiv.org/abs/2310.02548v1
|
https://arxiv.org/pdf/2310.02548v1.pdf
|
https://github.com/sebbas/poisson-pinn
| true | true | true |
none
|
https://paperswithcode.com/paper/a-fast-and-accurate-implementation-of-the
|
A Fast and Accurate Implementation of the Effective Fluid Approximation for Ultralight Axions
|
2501.13662
|
https://arxiv.org/abs/2501.13662v1
|
https://arxiv.org/pdf/2501.13662v1.pdf
|
https://github.com/adammoss/axicamb
| true | true | false |
none
|
https://paperswithcode.com/paper/unveiling-the-threat-of-fraud-gangs-to-graph
|
Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors
|
2412.18370
|
https://arxiv.org/abs/2412.18370v3
|
https://arxiv.org/pdf/2412.18370v3.pdf
|
https://github.com/bdi-lab/monti
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/influence-of-inertial-confinement-on-laser
|
Influence of inertial confinement on laser-induced bubble generation and shock wave emission
|
2501.13749
|
https://arxiv.org/abs/2501.13749v1
|
https://arxiv.org/pdf/2501.13749v1.pdf
|
https://github.com/X-X-Liang/LIBDAR
| true | false | false |
none
|
https://paperswithcode.com/paper/segearth-ov-towards-traning-free-open
|
SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
|
2410.01768
|
https://arxiv.org/abs/2410.01768v2
|
https://arxiv.org/pdf/2410.01768v2.pdf
|
https://github.com/earth-insights/samroadplus
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/scalable-realistic-recommendation-datasets
|
Scalable Realistic Recommendation Datasets through Fractal Expansions
|
1901.08910
|
http://arxiv.org/abs/1901.08910v3
|
http://arxiv.org/pdf/1901.08910v3.pdf
|
https://github.com/facebookresearch/generative-recommenders
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/mtsa-snn-a-multi-modal-time-series-analysis
|
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network
|
2402.05423
|
https://arxiv.org/abs/2402.05423v2
|
https://arxiv.org/pdf/2402.05423v2.pdf
|
https://github.com/chenngzz/mtsa-snn
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/point-deeponet-a-deep-operator-network
|
Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions
|
2412.18362
|
https://arxiv.org/abs/2412.18362v1
|
https://arxiv.org/pdf/2412.18362v1.pdf
|
https://github.com/jangseop-park/point-deeponet
| true | true | false |
none
|
https://paperswithcode.com/paper/domain-constraints-in-feature-space
|
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
|
2205.15128
|
https://arxiv.org/abs/2205.15128v4
|
https://arxiv.org/pdf/2205.15128v4.pdf
|
https://github.com/HamidBostani2021/robust-Android-malware-detector
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/b-vllm-a-vision-large-language-model-with
|
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens
|
2412.09919
|
https://arxiv.org/abs/2412.09919v1
|
https://arxiv.org/pdf/2412.09919v1.pdf
|
https://github.com/zhuqianglu/b-vllm
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/end-to-end-instance-image-goal-navigation
|
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
|
2309.16634
|
https://arxiv.org/abs/2309.16634v1
|
https://arxiv.org/pdf/2309.16634v1.pdf
|
https://github.com/naver/debit
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/inferring-tie-strength-in-temporal-networks
|
Inferring Tie Strength in Temporal Networks
|
2206.11705
|
https://arxiv.org/abs/2206.11705v2
|
https://arxiv.org/pdf/2206.11705v2.pdf
|
https://gitlab.com/tgpublic/tgstc
| true | true | true |
none
|
https://paperswithcode.com/paper/hacksynth-llm-agent-and-evaluation-framework
|
HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
|
2412.01778
|
https://arxiv.org/abs/2412.01778v1
|
https://arxiv.org/pdf/2412.01778v1.pdf
|
https://github.com/aielte-research/HackSynth
| true | false | true |
none
|
https://paperswithcode.com/paper/probabilistic-graph-rewiring-via-virtual
|
Probabilistic Graph Rewiring via Virtual Nodes
|
2405.17311
|
https://arxiv.org/abs/2405.17311v3
|
https://arxiv.org/pdf/2405.17311v3.pdf
|
https://github.com/chendiqian/IPR-MPNN
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/decentralization-of-ethereum-s-builder-market
|
Decentralization of Ethereum's Builder Market
|
2405.01329
|
https://arxiv.org/abs/2405.01329v5
|
https://arxiv.org/pdf/2405.01329v5.pdf
|
https://github.com/hackingdecentralized/Decentralization-of-Ethereum-Builder-Market
| true | false | true |
none
|
https://paperswithcode.com/paper/dualopt-a-dual-divide-and-optimize-algorithm
|
DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem
|
2501.08565
|
https://arxiv.org/abs/2501.08565v1
|
https://arxiv.org/pdf/2501.08565v1.pdf
|
https://github.com/learning4optimization-hust/dualopt
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/cosmos-cross-modality-self-distillation-for
|
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
|
2412.01814
|
https://arxiv.org/abs/2412.01814v2
|
https://arxiv.org/pdf/2412.01814v2.pdf
|
https://github.com/ExplainableML/cosmos
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/gnn4eeg-a-benchmark-and-toolkit-for
|
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
|
2309.15515
|
https://arxiv.org/abs/2309.15515v1
|
https://arxiv.org/pdf/2309.15515v1.pdf
|
https://github.com/MindSpore-scientific/code-1/tree/main/RGNN
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/on-the-statistical-capacity-of-deep
|
On the Statistical Capacity of Deep Generative Models
|
2501.07763
|
https://arxiv.org/abs/2501.07763v1
|
https://arxiv.org/pdf/2501.07763v1.pdf
|
https://github.com/edrictam/generative_capacity
| true | true | false |
none
|
https://paperswithcode.com/paper/change3d-revisiting-change-detection-and
|
Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective
|
2503.18803
|
https://arxiv.org/abs/2503.18803v1
|
https://arxiv.org/pdf/2503.18803v1.pdf
|
https://github.com/zhuduowang/Change3D
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/multimodal-deep-learning-for-subtype
|
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
|
2503.02849
|
https://arxiv.org/abs/2503.02849v1
|
https://arxiv.org/pdf/2503.02849v1.pdf
|
https://github.com/AminHonarmandiShandiz/cancerpredict
| true | false | false |
none
|
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