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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/thuml/Versatile-Domain-Adaptation

공식 코드

Minimum Class Confusion for Versatile Domain Adaptation

https://github.com/tianyao-aka/equad

공식 코드

Empowering Graph Invariance Learning with Deep Spurious Infomax

https://github.com/tiemink/MTL_TaskTensioner

공식 코드

Leveraging convergence behavior to balance conflicting tasks in multi-task learning

https://github.com/timkimd/rnntools.jl

공식 코드

Trainability, Expressivity and Interpretability in Gated Neural ODEs

https://github.com/tinkez/fap_cdfsc

공식 코드

Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting

https://github.com/tjujianyu/rfc

공식 코드

Rich Feature Construction for the Optimization-Generalization Dilemma

https://github.com/tkasarla/max-separation-as-inductive-bias

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Maximum Class Separation as Inductive Bias in One Matrix

https://github.com/tml-tuebingen/gibbs-prior-diagnostic

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Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference

https://github.com/toizzy/injecting-structural-hints

공식 코드

Injecting structural hints: Using language models to study inductive biases in language learning

https://github.com/tongnie/traffic_dynamics

공식 코드

Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

https://github.com/tonywu95/lime

공식 코드

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

https://github.com/tonyzyl/DiffusionReconstruct

공식 코드

Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations

https://github.com/toshihiro-ota/imixer

공식 코드

iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer

https://github.com/totony4real/deeprandomfeatures

공식 코드

Deep Random Features for Scalable Interpolation of Spatiotemporal Data

https://github.com/trais-lab/adversarial-attack-data-attribution

공식 코드

Adversarial Attacks on Data Attribution

https://github.com/tripdancer0916/probabilistic-inference-rnn

공식 코드

Dynamical Mechanism of Sampling-based Stochastic Inference under Probabilistic Population Codes

https://github.com/trustworthy-ai-uu-nki/cross-modal-segmentation

공식 코드

Enhancing Cross-Modal Medical Image Segmentation through Compositionality

https://github.com/ttthy/ure

공식 코드

Revisiting Unsupervised Relation Extraction

https://github.com/tudelft-iv/voteflow

공식 코드

VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow

https://github.com/tum-ai/number-token-loss

공식 코드

Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models

https://github.com/tusen-ai/sst

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FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels

https://github.com/twistedcubic/attention-rank-collapse

공식 코드

Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

https://github.com/txie-93/cdvae

공식 코드

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

https://github.com/ucaswangls/gap-ccot

공식 코드

Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer

https://github.com/uchidalab/vision-conformer

공식 코드

Vision Conformer: Incorporating Convolutions into Vision Transformer Layers

https://github.com/ucl-dark/skillhack

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Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

https://github.com/ukplab/eacl2023-xlingvqa

공식 코드

Delving Deeper into Cross-lingual Visual Question Answering

https://github.com/uqmarlonbran/tcs

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Transformer Compressed Sensing via Global Image Tokens

https://github.com/usinedepain/eq_aug_reg_release

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Data Augmentation and Regularization for Learning Group Equivariance

https://github.com/utahnlp/neural-logic

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Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study

https://github.com/uw-madison-lee-lab/languageinterfacedfinetuning

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LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks

https://github.com/uxhao-o/MLTrMR

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Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosis

https://github.com/val-iisc/DeiT-LT

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DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets

https://github.com/valeoai/rangevit

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RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving

https://github.com/vanderschaarlab/SMART_Testing

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Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models

https://github.com/vanderschaarlab/expertise

공식 코드

Defining Expertise: Applications to Treatment Effect Estimation

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