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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/aigc-apps/amformer

공식 코드

Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning

https://github.com/akshitac8/ow-detr

공식 코드

OW-DETR: Open-world Detection Transformer

https://github.com/albertgassol1/vf-nerf

공식 코드

VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction

https://github.com/albietz/ckn_kernel

공식 코드

On the Inductive Bias of Neural Tangent Kernels

https://github.com/alexnowakvila/DiCoNet

공식 코드

Divide and Conquer Networks

https://github.com/ali-forootani/compactformer

공식 코드

Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks

https://github.com/alicanbekar/mb_pddo-sindy

공식 코드

Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator

https://github.com/alinlab/OAMixer

공식 코드

OAMixer: Object-aware Mixing Layer for Vision Transformers

https://github.com/alinlab/remixer

공식 코드

OAMixer: Object-aware Mixing Layer for Vision Transformers

https://github.com/allanyangzhou/nfn

공식 코드

Permutation Equivariant Neural Functionals

https://github.com/allemanenti/Learning-Calibrated-Structures

공식 코드

Learning Latent Graph Structures and their Uncertainty

https://github.com/allenai/container

공식 코드

Container: Context Aggregation Network

https://github.com/alterapars/drought_classification

공식 코드

On the Generalization of Agricultural Drought Classification from Climate Data

https://github.com/alvinwen428/featurecp

공식 코드

Predictive Inference with Feature Conformal Prediction

https://github.com/amathislab/dmap

공식 코드

DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body

https://github.com/andreacini/sparse-graph-learning

공식 코드

Sparse Graph Learning from Spatiotemporal Time Series

https://github.com/ankitkv/sparo-clip

공식 코드

SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision

https://github.com/antn2023/antn

공식 코드

ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation

https://github.com/arichibald/grafourierformer

공식 코드

Graph Fourier Transformer with Structure-Frequency Information

https://github.com/arieseirack/dhvt

공식 코드

Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets

https://github.com/arnab39/EquivariantDQN

공식 코드

Group Equivariant Deep Reinforcement Learning

https://github.com/arnavkj1995/vsg

공식 코드

Learning Robust Dynamics through Variational Sparse Gating

https://github.com/arraystream/fftoptionlib

공식 코드

Gated Neural Networks for Option Pricing: Rationality by Design

https://github.com/arthurkosmala/EwaldMP

공식 코드

Ewald-based Long-Range Message Passing for Molecular Graphs

https://github.com/aryandeshwal/ladder

공식 코드

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

https://github.com/asappresearch/compositional-inductive-bias

공식 코드

Neural networks can understand compositional functions that humans do not, in the context of emergent communication

https://github.com/aschein/PRGDS

공식 코드

Poisson-Randomized Gamma Dynamical Systems

https://github.com/ask-berkeley/physics-nns-hard-constraints

공식 코드

Scaling physics-informed hard constraints with mixture-of-experts

https://github.com/askerlee/segtran

공식 코드

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/atavakol/action-hypergraph-networks

공식 코드

Learning to Represent Action Values as a Hypergraph on the Action Vertices

https://github.com/autonomousvision/carla_garage

공식 코드

Hidden Biases of End-to-End Driving Models

https://github.com/awni00/abstractor

공식 코드

Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers

https://github.com/azencot-group/skd

공식 코드

Multifactor Sequential Disentanglement via Structured Koopman Autoencoders

https://github.com/b04901014/FG-transformer-TTS

공식 코드

Fine-grained style control in Transformer-based Text-to-speech Synthesis

https://github.com/baaivision/eve

공식 코드

Unveiling Encoder-Free Vision-Language Models

https://github.com/badripatro/heracles

공식 코드

Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis

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