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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/cogito233/causal-sa

공식 코드

Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis

https://github.com/cognitive-robots/sem-gat

공식 코드

SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention

https://github.com/computri/learnable-weight-sharing

공식 코드

Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors

https://github.com/coursekevin/weakformghnn

공식 코드

Learning Neural Hamiltonian Dynamics: A Methodological Overview

https://github.com/csinva/imodels

공식 코드

Fast Interpretable Greedy-Tree Sums

https://github.com/cvignac/SMP

공식 코드

Building powerful and equivariant graph neural networks with structural message-passing

https://github.com/cvxgrp/cvxpylayers

공식 코드

Differentiable Convex Optimization Layers

https://github.com/cyrusneary/compositional_port_hamiltonian_nns

공식 코드

Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks

https://github.com/danfoa/morphosymm

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On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis

https://github.com/danfoa/robotequivariantnn

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On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis

https://github.com/danielecastellana22/feature-structure-interplay-graph-learning

공식 코드

Investigating the Interplay between Features and Structures in Graph Learning

https://github.com/danmiku/monotonenetworks

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Size and depth of monotone neural networks: interpolation and approximation

https://github.com/davnords/octic-vits

공식 코드

Stronger ViTs With Octic Equivariance

https://github.com/dchen48/e3ac

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${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

https://github.com/ddz16/TUT

공식 코드

Do we really need temporal convolutions in action segmentation?

https://github.com/dedeswim/vits-robustness-torch

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A Light Recipe to Train Robust Vision Transformers

https://github.com/deepmind/boxoban-levels

공식 코드

An investigation of model-free planning

https://github.com/deepmind/graph_nets

공식 코드

Relational inductive biases, deep learning, and graph networks

https://github.com/descriptinc/cargan

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Chunked Autoregressive GAN for Conditional Waveform Synthesis

https://github.com/dit-generalization/dit-generalization.github.io

공식 코드

On Inductive Biases That Enable Generalization of Diffusion Transformers

https://github.com/dojeon-ai/CoIn

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Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control

https://github.com/dongsky/few-shot-vit

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Self-Promoted Supervision for Few-Shot Transformer

https://github.com/dreamfold/foldflow

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Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

https://github.com/dukebw/SSTVOS

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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

https://github.com/dvruette/figaro

공식 코드

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

https://github.com/edmundlth/scalable_learning_coefficient_with_sgld

공식 코드

The Local Learning Coefficient: A Singularity-Aware Complexity Measure

https://github.com/eigenvivek/polypose

공식 코드

PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms

https://github.com/ekinakyurek/lexsym

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Compositionality as Lexical Symmetry

https://github.com/eleutherai/scalable-elicitation

공식 코드

Balancing Label Quantity and Quality for Scalable Elicitation

https://github.com/elicassion/StARformer

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StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning

https://github.com/emannix/tintem-teacher-in-the-middle

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Faithful Label-free Knowledge Distillation

https://github.com/emicore/emicore

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Physics-Informed Bayesian Optimization of Variational Quantum Circuits

https://github.com/epfl-dlab/distribution-inference-risks

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Distribution inference risks: Identifying and mitigating sources of leakage

https://github.com/epfl-dlab/property-inference-attacks

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Distribution inference risks: Identifying and mitigating sources of leakage

https://github.com/epflneuroailab/scaling-primate-vvs

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Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream

https://github.com/erg0dic/gnn-sg

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Systematic Reasoning About Relational Domains With Graph Neural Networks

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