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Inductive Bias

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mariuskrumm/manybodymeps

공식 코드

Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias

https://github.com/martius-lab/diffcp-lpgd

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LPGD: A General Framework for Backpropagation through Embedded Optimization Layers

https://github.com/martius-lab/gatel0rd

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Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

https://github.com/matouse/rot-invariance-analysis

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Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?

https://github.com/matthewvowels1/DisentanglingSequences

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VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts

https://github.com/maximilianigl/DVRL

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Deep Variational Reinforcement Learning for POMDPs

https://github.com/maxwelltsai/neural-symplectic-integrator

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Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem

https://github.com/mdrpanwar/icl-bayesian-prism

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In-Context Learning through the Bayesian Prism

https://github.com/meaningful96/satkgc

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Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning

https://github.com/meliao/rotation-invariant-random-features

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Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

https://github.com/mengyuanchen21/cvpr2022-ftcl

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Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization

https://github.com/mewispool/mewispool

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Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks

https://github.com/mfinzi/LieConv

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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

https://github.com/michaelaerni/iclr23-inductivebiasesharmlessinterpolation

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Strong inductive biases provably prevent harmless interpolation

https://github.com/microsoft/SeqML/tree/main/ContiFormer

공식 코드

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling

https://github.com/mihirp1998/Slot-TTA

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Test-time Adaptation with Slot-Centric Models

https://github.com/mjagadeesan/inductive-bias-multi-channel-CNN

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Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm

https://github.com/mkhodak/relax

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Rethinking Neural Operations for Diverse Tasks

https://github.com/ml-jku/hopfield-layers

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Bird-Eye Transformers for Text Generation Models

https://github.com/ml-jku/mc-lstm

공식 코드

MC-LSTM: Mass-Conserving LSTM

https://github.com/mli-lab/imaging_mlps

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Image-to-Image MLP-mixer for Image Reconstruction

https://github.com/mmcdermott/structure_inducing_pre-training

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Structure Inducing Pre-Training

https://github.com/mods333/energy-based-scene-graph

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Energy-Based Learning for Scene Graph Generation

https://github.com/mravanelli/SincNet

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Speech and Speaker Recognition from Raw Waveform with SincNet

https://github.com/mravanelli/pytorch-kaldi

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Interpretable Convolutional Filters with SincNet

https://github.com/mrhaipi/eit

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Deep Transformers Thirst for Comprehensive-Frequency Data

https://github.com/mschulth/rhc

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Receding Horizon Curiosity

https://github.com/mseitzer/neurips2019-disentanglement-challenge

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NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps

https://github.com/mshahbazi72/nerf-gan-distillation

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NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions

https://github.com/mttr2021/MTTR

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End-to-End Referring Video Object Segmentation with Multimodal Transformers

https://github.com/mucdk/genot

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GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics

https://github.com/mungomeng/registration-corrmlp

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Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

https://github.com/n-gao/pesnet

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Sampling-free Inference for Ab-Initio Potential Energy Surface Networks

https://github.com/nairouz/cvgae_pr

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A Contrastive Variational Graph Auto-Encoder for Node Clustering

https://github.com/namednil/multiset-perm

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Compositional Generalization without Trees using Multiset Tagging and Latent Permutations

https://github.com/namednil/sip

공식 코드

SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.