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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/shijie-wu/neural-transducer

공식 코드

Exact Hard Monotonic Attention for Character-Level Transduction

https://github.com/shijx12/XNM-Net

공식 코드

Explainable and Explicit Visual Reasoning over Scene Graphs

https://github.com/simingyan/iae

공식 코드

Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning

https://github.com/simpleinvariance/universalnetwork

공식 코드

A Simple and Universal Rotation Equivariant Point-cloud Network

https://github.com/sindhukrao/llm_temporal_bias

공식 코드

Unveiling Divergent Inductive Biases of LLMs on Temporal Data

https://github.com/siralex900/featureprogramming

공식 코드

Feature Programming for Multivariate Time Series Prediction

https://github.com/skeletondyh/gred

공식 코드

Recurrent Distance Filtering for Graph Representation Learning

https://github.com/slimgroup/deep_inference_with_tasks

공식 코드

Deep Bayesian inference for seismic imaging with tasks

https://github.com/smiles724/bottleneck

공식 코드

Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

https://github.com/snehjp2/gcnnmorphology

공식 코드

E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification

https://github.com/sofiabroome/temporal-shape-dataset

공식 코드

Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition

https://github.com/somepago/dbviz

공식 코드

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective

https://github.com/sonyresearch/svg_baseline

공식 코드

A Simple but Strong Baseline for Sounding Video Generation: Effective Adaptation of Audio and Video Diffusion Models for Joint Generation

https://github.com/spitis/deepnorms

공식 코드

An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

https://github.com/spozdn/pet

공식 코드

Probing the effects of broken symmetries in machine learning

https://github.com/sreejank/Abstract_Neural_Metamers

공식 코드

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

https://github.com/sriprabhar/km-maml

공식 코드

Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks

https://github.com/ssagawa/overparam_spur_corr

공식 코드

An Investigation of Why Overparameterization Exacerbates Spurious Correlations

https://github.com/sseung0703/Knowledge_distillation_via_TF2.0

공식 코드

Graph-based Knowledge Distillation by Multi-head Attention Network

https://github.com/stanis-morozov/prodige

공식 코드

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

https://github.com/stassa/louise

공식 코드

Meta-Interpretive Learning as Metarule Specialisation

https://github.com/statmixedml/hyper-trees

공식 코드

Forecasting with Hyper-Trees

https://github.com/summitgao/camixer

공식 코드

Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

https://github.com/sumuzhao/Investigate-BERT-Non-linearity-Commutativity

공식 코드

Of Non-Linearity and Commutativity in BERT

https://github.com/sunjin19126/caterpillar

공식 코드

Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation

https://github.com/sunyue93/rep-learning

공식 코드

Towards Sample-efficient Overparameterized Meta-learning

https://github.com/supertyd/XTrack

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XTrack: Multimodal Training Boosts RGB-X Video Object Trackers

https://github.com/symmetryadvantage/wyckofftransformer

공식 코드

Wyckoff Transformer: Generation of Symmetric Crystals

https://github.com/talented-q/sdmae

공식 코드

Masked autoencoders are effective solution to transformer data-hungry

https://github.com/tamarott/ASAPNet

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Spatially-Adaptive Pixelwise Networks for Fast Image Translation

https://github.com/tatsu-lab/mlm_inductive_bias

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On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

https://github.com/taucompling/mdl-reg-approach

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A Minimum Description Length Approach to Regularization in Neural Networks

https://github.com/tensorflow/tensor2tensor

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Universal Transformers

https://github.com/theilem/uavSim

공식 코드

Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning

https://github.com/theodorblackbird/lina-speech

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Small-E: Small Language Model with Linear Attention for Efficient Speech Synthesis

https://github.com/thuml/ModeRNN

공식 코드

ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning

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