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---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/sequence-learning-using-equilibrium
|
Sequence Learning Using Equilibrium Propagation
|
2209.09626
|
https://arxiv.org/abs/2209.09626v4
|
https://arxiv.org/pdf/2209.09626v4.pdf
|
https://github.com/neurocomplab-psu/eqprop-seqlearning
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/syntax-driven-approach-for-semantic-role
|
Syntax-driven Approach for Semantic Role Labeling
| null |
https://aclanthology.org/2022.lrec-1.772
|
https://aclanthology.org/2022.lrec-1.772.pdf
|
https://github.com/synlp/srl-mm
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-japanese-dataset-for-subjective-and
|
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
| null |
https://aclanthology.org/2022.lrec-1.759
|
https://aclanthology.org/2022.lrec-1.759.pdf
|
https://github.com/ids-cv/wrime
| true | true | false |
none
|
https://paperswithcode.com/paper/dynamic-convolution-attention-over
|
Dynamic Convolution: Attention over Convolution Kernels
|
1912.03458
|
https://arxiv.org/abs/1912.03458v2
|
https://arxiv.org/pdf/1912.03458v2.pdf
|
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/DynamicConv.py
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/accurate-explanation-model-for-image
|
Accurate Explanation Model for Image Classifiers using Class Association Embedding
|
2406.07961
|
https://arxiv.org/abs/2406.07961v3
|
https://arxiv.org/pdf/2406.07961v3.pdf
|
https://github.com/xrt11/xai-code
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/multimodality-for-nlp-centered-applications
|
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers
| null |
https://aclanthology.org/2022.lrec-1.738
|
https://aclanthology.org/2022.lrec-1.738.pdf
|
https://github.com/drmuskangarg/multimodal-datasets
| true | true | false |
none
|
https://paperswithcode.com/paper/bat-lz-out-of-hell
|
BAT-LZ Out of Hell
|
2403.09893
|
https://arxiv.org/abs/2403.09893v2
|
https://arxiv.org/pdf/2403.09893v2.pdf
|
https://github.com/fmasillo/bat-lz
| true | true | true |
none
|
https://paperswithcode.com/paper/explainable-reinforcement-learning-via-model
|
Explainable Reinforcement Learning via Model Transforms
|
2209.12006
|
https://arxiv.org/abs/2209.12006v2
|
https://arxiv.org/pdf/2209.12006v2.pdf
|
https://github.com/sarah-keren/rlpe
| true | true | true |
none
|
https://paperswithcode.com/paper/a-free-open-source-morphological-analyser-and
|
A Free/Open-Source Morphological Analyser and Generator for Sakha
| null |
https://aclanthology.org/2022.lrec-1.550
|
https://aclanthology.org/2022.lrec-1.550.pdf
|
https://github.com/apertium/apertium-sah
| true | true | false |
none
|
https://paperswithcode.com/paper/hyperreel-high-fidelity-6-dof-video-with-ray
|
HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
|
2301.02238
|
https://arxiv.org/abs/2301.02238v2
|
https://arxiv.org/pdf/2301.02238v2.pdf
|
https://github.com/facebookresearch/hyperreel
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/basicvsr-the-search-for-essential-components
|
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
|
2012.02181
|
https://arxiv.org/abs/2012.02181v2
|
https://arxiv.org/pdf/2012.02181v2.pdf
|
https://github.com/XPixelGroup/BasicSR
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/evaluating-explainability-for-graph-neural
|
Evaluating Explainability for Graph Neural Networks
|
2208.09339
|
https://arxiv.org/abs/2208.09339v2
|
https://arxiv.org/pdf/2208.09339v2.pdf
|
https://github.com/mims-harvard/graphxai
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/reproducibility-report-contrastive-learning
|
Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations
|
2208.09284
|
https://arxiv.org/abs/2208.09284v1
|
https://arxiv.org/pdf/2208.09284v1.pdf
|
https://github.com/vita-epfl/social-nce
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/amr-similarity-metrics-from-principles
|
AMR Similarity Metrics from Principles
|
2001.10929
|
https://arxiv.org/abs/2001.10929v2
|
https://arxiv.org/pdf/2001.10929v2.pdf
|
https://github.com/flipz357/amr-metric-suite
| false | false | true |
none
|
https://paperswithcode.com/paper/dcsf-deep-convolutional-set-functions-for
|
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series
|
2208.11374
|
https://arxiv.org/abs/2208.11374v1
|
https://arxiv.org/pdf/2208.11374v1.pdf
|
https://github.com/yalavarthivk/dcsf
| true | true | false |
tf
|
https://paperswithcode.com/paper/entropy-solutions-of-non-local-scalar
|
Entropy solutions of non-local scalar conservation laws with congestion via deterministic particle method
|
2107.10760
|
https://arxiv.org/abs/2107.10760v2
|
https://arxiv.org/pdf/2107.10760v2.pdf
|
https://github.com/FedericoStra/cons-laws
| true | false | true |
none
|
https://paperswithcode.com/paper/semi-supervised-conditional-gan-for
|
Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective
|
2108.01852
|
https://arxiv.org/abs/2108.01852v3
|
https://arxiv.org/pdf/2108.01852v3.pdf
|
https://github.com/SharifAmit/Semi-supervised-Phishing-Detection-GAN
| true | true | true |
tf
|
https://paperswithcode.com/paper/decoding-demographic-un-fairness-from-indian
|
Decoding Demographic un-fairness from Indian Names
|
2209.03089
|
https://arxiv.org/abs/2209.03089v1
|
https://arxiv.org/pdf/2209.03089v1.pdf
|
https://github.com/vahini01/indiandemographics
| true | true | false |
none
|
https://paperswithcode.com/paper/social-media-engagement-and-cryptocurrency
|
Social Media Engagement and Cryptocurrency Performance
|
2209.02911
|
https://arxiv.org/abs/2209.02911v1
|
https://arxiv.org/pdf/2209.02911v1.pdf
|
https://github.com/kai-trading-bot/crypto_engagement
| true | true | false |
none
|
https://paperswithcode.com/paper/3d-bounding-box-estimation-using-deep
|
3D Bounding Box Estimation Using Deep Learning and Geometry
|
1612.00496
|
http://arxiv.org/abs/1612.00496v2
|
http://arxiv.org/pdf/1612.00496v2.pdf
|
https://github.com/ruhyadi/YOLO3D
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/constrained-update-projection-approach-to
|
Constrained Update Projection Approach to Safe Policy Optimization
|
2209.07089
|
https://arxiv.org/abs/2209.07089v2
|
https://arxiv.org/pdf/2209.07089v2.pdf
|
https://github.com/zmsn-2077/cup-safe-rl
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/nsnet-a-general-neural-probabilistic
|
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems
|
2211.03880
|
https://arxiv.org/abs/2211.03880v1
|
https://arxiv.org/pdf/2211.03880v1.pdf
|
https://github.com/zhaoyu-li/nsnet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-japanese-masked-language-model-for-academic
|
A Japanese Masked Language Model for Academic Domain
| null |
https://aclanthology.org/2022.sdp-1.16
|
https://aclanthology.org/2022.sdp-1.16.pdf
|
https://github.com/hirokiyamauch/academicroberta
| true | true | false |
none
|
https://paperswithcode.com/paper/transfer-learning-in-ecg-diagnosis-is-it
|
Transfer Learning in ECG Diagnosis: Is It Effective?
|
2402.02021
|
https://arxiv.org/abs/2402.02021v2
|
https://arxiv.org/pdf/2402.02021v2.pdf
|
https://github.com/cuongvng/transfer-learning-ecg-diagnosis
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/conformal-inference-for-cell-type-annotation
|
Conformal inference for cell type annotation with graph-structured constraints
|
2410.23786
|
https://arxiv.org/abs/2410.23786v1
|
https://arxiv.org/pdf/2410.23786v1.pdf
|
https://github.com/ccb-hms/scconform
| true | true | false |
none
|
https://paperswithcode.com/paper/the-surprising-effectiveness-of-mappo-in
|
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
|
2103.01955
|
https://arxiv.org/abs/2103.01955v4
|
https://arxiv.org/pdf/2103.01955v4.pdf
|
https://github.com/tjuhaoxiaotian/pymarl3
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-practical-guide-and-software-for-analysing
|
A practical guide and software for analysing pairwise comparison experiments
|
1712.03686
|
http://arxiv.org/abs/1712.03686v2
|
http://arxiv.org/pdf/1712.03686v2.pdf
|
https://github.com/mantiuk/pwcmp
| true | true | true |
none
|
https://paperswithcode.com/paper/lapred-lane-aware-prediction-of-multi-modal
|
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents
|
2104.00249
|
https://arxiv.org/abs/2104.00249v1
|
https://arxiv.org/pdf/2104.00249v1.pdf
|
https://github.com/bdokim/LaPred
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/generating-artificial-light-curves-revisited
|
Generating artificial light curves: Revisited and updated
|
1305.0304
|
https://arxiv.org/abs/1305.0304v1
|
https://arxiv.org/pdf/1305.0304v1.pdf
|
https://github.com/lena-lin/emmanoulopoulos
| false | false | true |
none
|
https://paperswithcode.com/paper/an-automated-process-for-2d-and-3d-finite
|
Automated 2D and 3D Finite Element Overclosure Adjustment and Mesh Morphing Using Generalized Regression Neural Networks
|
2209.06948
|
https://arxiv.org/abs/2209.06948v3
|
https://arxiv.org/pdf/2209.06948v3.pdf
|
https://github.com/thor-andreassen/femors
| true | true | false |
none
|
https://paperswithcode.com/paper/investigating-semantic-subspaces-of
|
Investigating semantic subspaces of Transformer sentence embeddings through linear structural probing
|
2310.11923
|
https://arxiv.org/abs/2310.11923v1
|
https://arxiv.org/pdf/2310.11923v1.pdf
|
https://github.com/macleginn/semantic-subspaces-code
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/introduction-to-astroml-machine-learning-for
|
Introduction to astroML: Machine Learning for Astrophysics
|
1411.5039
|
http://arxiv.org/abs/1411.5039v1
|
http://arxiv.org/pdf/1411.5039v1.pdf
|
https://github.com/LBJ-Wade/astroML
| false | false | true |
none
|
https://paperswithcode.com/paper/an-adaptive-kernel-estimator-for-the
|
An adaptive kernel estimator for the intensity function of spatio-temporal point processes
|
2208.12026
|
https://arxiv.org/abs/2208.12026v1
|
https://arxiv.org/pdf/2208.12026v1.pdf
|
https://github.com/jagm03/kernstadapt
| true | true | true |
none
|
https://paperswithcode.com/paper/bayesian-quadrature-for-probability-threshold
|
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
|
2210.02168
|
https://arxiv.org/abs/2210.02168v2
|
https://arxiv.org/pdf/2210.02168v2.pdf
|
https://github.com/fiveai/hgp_experiments
| true | true | true |
none
|
https://paperswithcode.com/paper/investigating-the-effect-of-circuit-cutting
|
Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
|
2302.01792
|
https://arxiv.org/abs/2302.01792v2
|
https://arxiv.org/pdf/2302.01792v2.pdf
|
https://github.com/anonym-scientist/cut-qaoa-paper
| true | true | false |
none
|
https://paperswithcode.com/paper/3d-vsg-long-term-semantic-scene-change
|
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
|
2209.07896
|
https://arxiv.org/abs/2209.07896v2
|
https://arxiv.org/pdf/2209.07896v2.pdf
|
https://github.com/ethz-asl/3d_vsg
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/estimation-of-optical-aberrations-in-3d
|
Estimation of Optical Aberrations in 3D Microscopic Bioimages
|
2209.07911
|
https://arxiv.org/abs/2209.07911v1
|
https://arxiv.org/pdf/2209.07911v1.pdf
|
https://github.com/kiraving/aberration
| true | true | false |
none
|
https://paperswithcode.com/paper/deep-learning-for-brain-metastasis-detection
|
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
|
2112.11833
|
https://arxiv.org/abs/2112.11833v5
|
https://arxiv.org/pdf/2112.11833v5.pdf
|
https://github.com/yixinghuang/deepmedicplus
| true | true | false |
tf
|
https://paperswithcode.com/paper/high-resolution-image-synthesis-with-latent
|
High-Resolution Image Synthesis with Latent Diffusion Models
|
2112.10752
|
https://arxiv.org/abs/2112.10752v2
|
https://arxiv.org/pdf/2112.10752v2.pdf
|
https://github.com/compvis/stable-diffusion
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/deep-attractor-network-for-single-microphone
|
Deep attractor network for single-microphone speaker separation
|
1611.08930
|
http://arxiv.org/abs/1611.08930v2
|
http://arxiv.org/pdf/1611.08930v2.pdf
|
https://github.com/KMASAHIRO/DANet
| false | false | true |
tf
|
https://paperswithcode.com/paper/serialized-interacting-mixed-membership
|
Serialized Interacting Mixed Membership Stochastic Block Model
|
2209.07813
|
https://arxiv.org/abs/2209.07813v1
|
https://arxiv.org/pdf/2209.07813v1.pdf
|
https://github.com/gaelpouxmedard/simsbm
| true | true | false |
none
|
https://paperswithcode.com/paper/comparison-of-high-dimensional-bayesian
|
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
|
2303.00890
|
https://arxiv.org/abs/2303.00890v3
|
https://arxiv.org/pdf/2303.00890v3.pdf
|
https://github.com/marialaurasantoni/ioh-profiler-hdbo-comparison
| true | true | false |
jax
|
https://paperswithcode.com/paper/robust-vocal-quality-feature-embeddings-for
|
Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
|
2211.09858
|
https://arxiv.org/abs/2211.09858v2
|
https://arxiv.org/pdf/2211.09858v2.pdf
|
https://github.com/vigor-jzhang/dysphonic-emb
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/d-d-learning-human-dynamics-from-dynamic
|
D&D: Learning Human Dynamics from Dynamic Camera
|
2209.08790
|
https://arxiv.org/abs/2209.08790v1
|
https://arxiv.org/pdf/2209.08790v1.pdf
|
https://github.com/jeff-sjtu/dnd
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/hybrik-a-hybrid-analytical-neural-inverse
|
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
|
2011.14672
|
https://arxiv.org/abs/2011.14672v4
|
https://arxiv.org/pdf/2011.14672v4.pdf
|
https://github.com/jeff-sjtu/dnd
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/cerberus-low-drift-visual-inertial-leg
|
Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion
|
2209.07654
|
https://arxiv.org/abs/2209.07654v1
|
https://arxiv.org/pdf/2209.07654v1.pdf
|
https://github.com/shuoyangrobotics/cerberus
| true | true | true |
none
|
https://paperswithcode.com/paper/monacobert-monotonic-attention-based-convbert
|
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
|
2208.12615
|
https://arxiv.org/abs/2208.12615v2
|
https://arxiv.org/pdf/2208.12615v2.pdf
|
https://github.com/codingchild2424/MonaCoBERT
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/rucola-russian-corpus-of-linguistic
|
RuCoLA: Russian Corpus of Linguistic Acceptability
|
2210.12814
|
https://arxiv.org/abs/2210.12814v1
|
https://arxiv.org/pdf/2210.12814v1.pdf
|
https://github.com/russiannlp/rucola
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/apollo-an-adaptive-parameter-wise-diagonal
|
Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization
|
2009.13586
|
https://arxiv.org/abs/2009.13586v6
|
https://arxiv.org/pdf/2009.13586v6.pdf
|
https://github.com/XuezheMax/fairseq-apollo
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/panoramic-panoptic-segmentation-towards
|
Panoramic Panoptic Segmentation: Towards Complete Surrounding Understanding via Unsupervised Contrastive Learning
|
2103.00868
|
https://arxiv.org/abs/2103.00868v2
|
https://arxiv.org/pdf/2103.00868v2.pdf
|
https://github.com/alexanderjaus/PPS
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/phabox-a-web-server-for-identifying-and
|
PhaBOX: A web server for identifying and characterizing phage contigs in metagenomic data
|
2303.15707
|
https://arxiv.org/abs/2303.15707v3
|
https://arxiv.org/pdf/2303.15707v3.pdf
|
https://github.com/kennthshang/phabox
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/thinking-hallucination-for-video-captioning
|
Thinking Hallucination for Video Captioning
|
2209.13853
|
https://arxiv.org/abs/2209.13853v1
|
https://arxiv.org/pdf/2209.13853v1.pdf
|
https://github.com/nasib-ullah/THVC
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/pushing-the-limits-of-the-wiener-filter-in
|
Pushing The Limits of the Wiener Filter in Image Denoising
|
2303.16640
|
https://arxiv.org/abs/2303.16640v1
|
https://arxiv.org/pdf/2303.16640v1.pdf
|
https://github.com/mrbled/icicp_2023_wiener
| true | true | false |
none
|
https://paperswithcode.com/paper/forward-mode-automatic-differentiation-in
|
Forward-Mode Automatic Differentiation in Julia
|
1607.07892
|
http://arxiv.org/abs/1607.07892v1
|
http://arxiv.org/pdf/1607.07892v1.pdf
|
https://github.com/jesse-sharp/sharp2021b
| false | false | true |
none
|
https://paperswithcode.com/paper/generating-astronomical-spectra-from
|
Generating astronomical spectra from photometry with conditional diffusion models
|
2211.05556
|
https://arxiv.org/abs/2211.05556v1
|
https://arxiv.org/pdf/2211.05556v1.pdf
|
https://github.com/larsdoorenbos/generate-spectra
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/cetn-contrast-enhanced-through-network-for
|
CETN: Contrast-enhanced Through Network for CTR Prediction
|
2312.09715
|
https://arxiv.org/abs/2312.09715v2
|
https://arxiv.org/pdf/2312.09715v2.pdf
|
https://github.com/salmon1802/cetn
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/ditto-quantization-aware-secure-inference-of
|
Ditto: Quantization-aware Secure Inference of Transformers upon MPC
|
2405.05525
|
https://arxiv.org/abs/2405.05525v1
|
https://arxiv.org/pdf/2405.05525v1.pdf
|
https://github.com/secretflow/spu
| true | true | false |
tf
|
https://paperswithcode.com/paper/a-concise-but-effective-network-for-image
|
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
|
2401.15902
|
https://arxiv.org/abs/2401.15902v2
|
https://arxiv.org/pdf/2401.15902v2.pdf
|
https://github.com/lmomoy/chnet
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/cairl-a-high-performance-reinforcement
|
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
|
2210.01235
|
https://arxiv.org/abs/2210.01235v1
|
https://arxiv.org/pdf/2210.01235v1.pdf
|
https://github.com/cair/rl
| true | true | false |
none
|
https://paperswithcode.com/paper/gantouch-an-attack-resilient-framework-for
|
GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System
|
2210.01594
|
https://arxiv.org/abs/2210.01594v1
|
https://arxiv.org/pdf/2210.01594v1.pdf
|
https://github.com/midas-research/gantouch-tbiom
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/hyperbolic-geometry-in-computer-vision-a
|
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
|
2303.15919
|
https://arxiv.org/abs/2303.15919v3
|
https://arxiv.org/pdf/2303.15919v3.pdf
|
https://github.com/kschwethelm/hyperboliccv
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/join-operation-for-the-bruhat-order-and-verma
|
Join operation for the Bruhat order and Verma modules
|
2109.01067
|
https://arxiv.org/abs/2109.01067v1
|
https://arxiv.org/pdf/2109.01067v1.pdf
|
https://github.com/rafael-mrden/BGG-Category-O-and-related-combinatorics
| false | false | true |
none
|
https://paperswithcode.com/paper/roaring-bitmaps-implementation-of-an
|
Roaring Bitmaps: Implementation of an Optimized Software Library
|
1709.07821
|
https://arxiv.org/abs/1709.07821v6
|
https://arxiv.org/pdf/1709.07821v6.pdf
|
https://github.com/RoaringBitmap/roaring
| false | false | true |
none
|
https://paperswithcode.com/paper/some-homological-properties-of-category
|
Some homological properties of category $\mathcal{O}$, V
|
2007.00342
|
http://arxiv.org/abs/2007.00342v3
|
http://arxiv.org/pdf/2007.00342v3.pdf
|
https://github.com/rafael-mrden/BGG-Category-O-and-related-combinatorics
| false | false | true |
none
|
https://paperswithcode.com/paper/entropy-based-active-learning-of-graph-neural
|
Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
|
2108.02077
|
https://arxiv.org/abs/2108.02077v2
|
https://arxiv.org/pdf/2108.02077v2.pdf
|
https://github.com/keeeto/gp-net
| true | false | false |
none
|
https://paperswithcode.com/paper/towards-interactive-and-learnable-cooperative
|
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
|
2409.12812
|
https://arxiv.org/abs/2409.12812v2
|
https://arxiv.org/pdf/2409.12812v2.pdf
|
https://github.com/fangshiyuu/codrivingllm
| true | true | true |
none
|
https://paperswithcode.com/paper/benchmarking-graphormer-on-large-scale
|
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
|
2203.04810
|
https://arxiv.org/abs/2203.04810v2
|
https://arxiv.org/pdf/2203.04810v2.pdf
|
https://github.com/lsj2408/Transformer-M
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/efficient-scale-invariant-generator-with
|
Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis
|
2303.14157
|
https://arxiv.org/abs/2303.14157v3
|
https://arxiv.org/pdf/2303.14157v3.pdf
|
https://github.com/vinairesearch/creps
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/core-to-core-x-ray-emission-spectra-from
|
Core-to-Core X-ray Emission Spectra from Wannier Based Multiplet Ligand Field Theory
|
2304.14582
|
https://arxiv.org/abs/2304.14582v1
|
https://arxiv.org/pdf/2304.14582v1.pdf
|
https://github.com/seidler-lab/crcl2kaxes_example
| true | true | false |
none
|
https://paperswithcode.com/paper/cascaded-debiasing-studying-the-cumulative
|
Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions
|
2202.03734
|
https://arxiv.org/abs/2202.03734v2
|
https://arxiv.org/pdf/2202.03734v2.pdf
|
https://github.com/bhavyaghai/cascaded-debiasing
| true | true | true |
none
|
https://paperswithcode.com/paper/incorporating-texture-information-into
|
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
|
2202.09179
|
https://arxiv.org/abs/2202.09179v2
|
https://arxiv.org/pdf/2202.09179v2.pdf
|
https://github.com/biovault/spidr
| true | true | true |
none
|
https://paperswithcode.com/paper/fcc-ee-polarimeter
|
FCC-ee polarimeter
|
1803.09595
|
https://arxiv.org/abs/1803.09595v1
|
https://arxiv.org/pdf/1803.09595v1.pdf
|
https://github.com/muchnoi/FCCee-Polarimeter
| false | false | true |
none
|
https://paperswithcode.com/paper/bi-directional-weakly-supervised-knowledge
|
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
|
2210.03664
|
https://arxiv.org/abs/2210.03664v2
|
https://arxiv.org/pdf/2210.03664v2.pdf
|
https://github.com/miccaiif/weno
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-joint-modeling-approach-to-study-the
|
A joint modeling approach to study the association between subject-level longitudinal marker variabilities and repeated outcomes
|
2309.08000
|
https://arxiv.org/abs/2309.08000v1
|
https://arxiv.org/pdf/2309.08000v1.pdf
|
https://github.com/realirena/jelo
| true | true | false |
none
|
https://paperswithcode.com/paper/longtonotes-ontonotes-with-longer-coreference
|
Longtonotes: OntoNotes with Longer Coreference Chains
|
2210.03650
|
https://arxiv.org/abs/2210.03650v1
|
https://arxiv.org/pdf/2210.03650v1.pdf
|
https://github.com/kumar-shridhar/longtonotes
| true | true | true |
none
|
https://paperswithcode.com/paper/hate-speech-and-offensive-language-detection-1
|
Hate Speech and Offensive Language Detection in Bengali
|
2210.03479
|
https://arxiv.org/abs/2210.03479v1
|
https://arxiv.org/pdf/2210.03479v1.pdf
|
https://github.com/hate-alert/bengali_hate
| true | true | true |
none
|
https://paperswithcode.com/paper/a-resnet-is-all-you-need-modeling-a-strong
|
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
|
2210.03180
|
https://arxiv.org/abs/2210.03180v1
|
https://arxiv.org/pdf/2210.03180v1.pdf
|
https://github.com/tomascast/sipaim-2022-resnet
| true | true | false |
none
|
https://paperswithcode.com/paper/self-supervised-visual-representation-2
|
Self-Supervised Visual Representation Learning with Semantic Grouping
|
2205.15288
|
https://arxiv.org/abs/2205.15288v2
|
https://arxiv.org/pdf/2205.15288v2.pdf
|
https://github.com/CVMI-Lab/SlotCon
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/integrating-random-effects-in-deep-neural
|
Integrating Random Effects in Deep Neural Networks
|
2206.03314
|
https://arxiv.org/abs/2206.03314v3
|
https://arxiv.org/pdf/2206.03314v3.pdf
|
https://github.com/gsimchoni/lmmnn
| true | true | true |
none
|
https://paperswithcode.com/paper/metadrive-composing-diverse-driving-scenarios
|
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
|
2109.12674
|
https://arxiv.org/abs/2109.12674v3
|
https://arxiv.org/pdf/2109.12674v3.pdf
|
https://github.com/metadriverse/metadrive
| true | true | true |
none
|
https://paperswithcode.com/paper/one-shot-federated-learning-without-server
|
One-shot Federated Learning without Server-side Training
|
2204.12493
|
https://arxiv.org/abs/2204.12493v2
|
https://arxiv.org/pdf/2204.12493v2.pdf
|
https://github.com/fudanvi/maecho
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/classical-simulators-as-quantum-error
|
Classical simulators as quantum error mitigators via circuit cutting
|
2212.07335
|
https://arxiv.org/abs/2212.07335v1
|
https://arxiv.org/pdf/2212.07335v1.pdf
|
https://github.com/revilooliver/cut4mitigation
| true | true | false |
none
|
https://paperswithcode.com/paper/online-incremental-non-gaussian-inference-for
|
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
|
2110.00876
|
https://arxiv.org/abs/2110.00876v2
|
https://arxiv.org/pdf/2110.00876v2.pdf
|
https://github.com/marineroboticsgroup/nf-isam
| true | true | true |
none
|
https://paperswithcode.com/paper/feta-a-benchmark-for-few-sample-task-transfer
|
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
|
2205.06262
|
https://arxiv.org/abs/2205.06262v2
|
https://arxiv.org/pdf/2205.06262v2.pdf
|
https://github.com/alon-albalak/tlidb
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/detection-of-dementia-through-3d
|
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET
| null |
https://ieeexplore.ieee.org/document/9660102
|
https://espositoandrea.github.io/assets/papers/Castellano2021Detection.pdf
|
https://github.com/espositoandrea/Detecting-Alzheimer-Using-Amiloyd-PET-Scans
| false | false | false |
none
|
https://paperswithcode.com/paper/skateformer-skeletal-temporal-transformer-for
|
SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
|
2403.09508
|
https://arxiv.org/abs/2403.09508v3
|
https://arxiv.org/pdf/2403.09508v3.pdf
|
https://github.com/KAIST-VICLab/SkateFormer
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/towards-few-shot-entity-recognition-in-1
|
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
|
2204.05819
|
https://arxiv.org/abs/2204.05819v1
|
https://arxiv.org/pdf/2204.05819v1.pdf
|
https://github.com/zlwang-cs/laser-release
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/panoramic-direct-lidar-assisted-visual
|
Panoramic Direct LiDAR-assisted Visual Odometry
|
2409.09287
|
https://arxiv.org/abs/2409.09287v1
|
https://arxiv.org/pdf/2409.09287v1.pdf
|
https://github.com/ZikangYuan/panoramic_lidar_dso
| true | false | true |
none
|
https://paperswithcode.com/paper/codonmt-modeling-cohesion-devices-for
|
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation
| null |
https://aclanthology.org/2022.coling-1.462
|
https://aclanthology.org/2022.coling-1.462.pdf
|
https://github.com/codeboy311/codonmt
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/robustness-certification-of-visual-perception
|
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
|
2210.04625
|
https://arxiv.org/abs/2210.04625v2
|
https://arxiv.org/pdf/2210.04625v2.pdf
|
https://github.com/hanjianghu/camera-motion-smoothing
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/subeventwriter-iterative-sub-event-sequence
|
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller
|
2210.06694
|
https://arxiv.org/abs/2210.06694v3
|
https://arxiv.org/pdf/2210.06694v3.pdf
|
https://github.com/hkust-knowcomp/subeventwriter
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/kerple-kernelized-relative-positional
|
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
|
2205.09921
|
https://arxiv.org/abs/2205.09921v2
|
https://arxiv.org/pdf/2205.09921v2.pdf
|
https://github.com/chijames/kerple
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/towards-efficient-3d-object-detection-with
|
Towards Efficient 3D Object Detection with Knowledge Distillation
|
2205.15156
|
https://arxiv.org/abs/2205.15156v3
|
https://arxiv.org/pdf/2205.15156v3.pdf
|
https://github.com/cvmi-lab/sparsekd
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/technical-debt-management-in-oss-projects-an
|
Technical Debt Management in OSS Projects: An Empirical Study on GitHub
|
2212.05537
|
https://arxiv.org/abs/2212.05537v1
|
https://arxiv.org/pdf/2212.05537v1.pdf
|
https://github.com/ypyixiuxiu/tdmdatasets
| true | true | false |
none
|
https://paperswithcode.com/paper/cabvit-cross-attention-among-blocks-for
|
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer
|
2211.07198
|
https://arxiv.org/abs/2211.07198v2
|
https://arxiv.org/pdf/2211.07198v2.pdf
|
https://github.com/hkzhang91/cabvit
| 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/SHI-Labs/OneFormer
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/fragment-based-molecular-generative-model
|
Fragment-based molecular generative model with high generalization ability and synthetic accessibility
|
2111.12907
|
https://arxiv.org/abs/2111.12907v1
|
https://arxiv.org/pdf/2111.12907v1.pdf
|
https://github.com/jaechang-hits/bbar-pytorch
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/quantum-approximate-optimization-algorithm-8
|
Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network
|
2211.09513
|
https://arxiv.org/abs/2211.09513v3
|
https://arxiv.org/pdf/2211.09513v3.pdf
|
https://github.com/NingyiXie/Parameter-to-Parameter-Convolutional-Neural-Network
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/towards-a-unified-conformer-structure-from
|
Towards A Unified Conformer Structure: from ASR to ASV Task
|
2211.07201
|
https://arxiv.org/abs/2211.07201v2
|
https://arxiv.org/pdf/2211.07201v2.pdf
|
https://github.com/Snowdar/asv-subtools
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/decoding-memes-a-comparative-study-of-machine
|
Decoding Memes: A Comparative Study of Machine Learning Models for Template Identification
|
2408.08126
|
https://arxiv.org/abs/2408.08126v1
|
https://arxiv.org/pdf/2408.08126v1.pdf
|
https://github.com/hsdslab/meme-research
| true | true | false |
pytorch
|
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