https://github.com/liuzywen/hrtransnet
공식 코드HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection
Papers with Code 아카이브
global-optimization
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ltu-rai/opt-key
공식 코드https://github.com/lumos-xiao/leap
공식 코드https://github.com/luoxinlongroger/cnmge
공식 코드Continuation Newton methods with deflation techniques for global optimization problems
https://github.com/maxc01/addtree
공식 코드https://github.com/mbinois/RRembo
공식 코드On the choice of the low-dimensional domain for global optimization via random embeddings
https://github.com/mida-group/cross_sim_ngf
공식 코드https://github.com/mjzhu-p/pwas
공식 코드Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates
https://github.com/mradaideh/neorl
공식 코드NEORL: NeuroEvolution Optimization with Reinforcement Learning
https://github.com/novonordisk-research/BEE-BO
공식 코드Batched Energy-Entropy acquisition for Bayesian Optimization
https://github.com/nvlabs/glamr
공식 코드GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
https://github.com/paulsonlab/constrained_upper_quantile_bound
공식 코드https://github.com/phantrdat/neuralbo
공식 코드Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
https://github.com/philipp238/parfam
공식 코드ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
https://github.com/phrsampaio/deft-funnel
공식 코드https://github.com/ponimatkin/ssl-vos
공식 코드A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
https://github.com/priesemann-group/infomorphic_networks
공식 코드What should a neuron aim for? Designing local objective functions based on information theory
https://github.com/princeton-vl/multislam_diffpose
공식 코드Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
https://github.com/purushottamkar/agglio
공식 코드https://github.com/pySRURGS/pySRURGS
공식 코드https://github.com/pytorch/botorch
공식 코드BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
https://github.com/qingshufan/ga-evlru
공식 코드https://github.com/rafael-a-monteiro-math/landscape_sketch_and_step
공식 코드Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems
https://github.com/ranr01/miniBOP
공식 코드New Heuristics for Parallel and Scalable Bayesian Optimization
https://github.com/rubbish001/co-fix3d
공식 코드Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement
https://github.com/samuelkim314/DeepBO
공식 코드Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
https://github.com/sebastianament/covariancefunctions.jl
공식 코드Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
https://github.com/sisidai/InterFusion
공식 코드InterFusion: Text-Driven Generation of 3D Human-Object Interaction
https://github.com/sixuli/fedcbo
공식 코드https://github.com/snt-arg/lidar_situational_graphs
공식 코드S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM
https://github.com/spengliang/sudo
공식 코드SUDO: Enhancing Text-to-Image Diffusion Models with Self-Supervised Direct Preference Optimization
https://github.com/stanfordcbcl/blnm.jl
공식 코드https://github.com/step2-technology/ga-llm
공식 코드https://github.com/sysal-bruce-publication/CRaSZe-AntS
공식 코드On Solving Close Enough Orienteering Problems with Overlapped Neighborhoods
https://github.com/tding1/Neural-Collapse
공식 코드A Geometric Analysis of Neural Collapse with Unconstrained Features
https://github.com/tusen-ai/anchor3dlane
공식 코드Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.