Papers Inductive Bias
“Inductive Bias” 태그가 달린 논문 1,529편 · 필터 해제
$π^3$: Scalable Permutation-Equivariant Visual Geometry Learning
We introduce $\pi^3$, a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Previous methods often anchor their recons…
Camera Pose EstimationDepth EstimationInductive BiasPose EstimationWhat Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the discovery of Newtonian mechanics. Howev…
Inductive BiasLearning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due to domain shifts and imbalanced disparity…
Inductive BiasMixture-of-ExpertsStereo MatchingAGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
Brain-computer interface (BCI) technology utilizing electroencephalography (EEG) marks a transformative innovation, empowering motor-impaired individuals to engage with their environment on equal footing. Despite its pro…
Brain Computer InterfaceEEGInductive BiasMotor ImageryLearning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obt…
AnatomyInductive BiasLesion DetectionSelf-Supervised LearningEnhancing Image Restoration Transformer via Adaptive Translation Equivariance
Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoration transformers undermine this property, …
Image RestorationInductive BiasTranslationResNets Are Deeper Than You Think
Residual connections remain ubiquitous in modern neural network architectures nearly a decade after their introduction. Their widespread adoption is often credited to their dramatically improved trainability: residual ne…
Inductive BiasDirected Acyclic Graph Convolutional Networks
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a…
Causal InferenceComputational EfficiencyGraph Neural NetworkInductive Bias+2The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images without Any 3D Knowledge
We consider the problem of generalizable novel view synthesis (NVS), which aims to generate photorealistic novel views from sparse or even unposed 2D images without per-scene optimization. This task remains fundamentally…
3DGSGeneralizable Novel View SynthesisInductive BiasNeRF+1BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogma of molecular biology (information flow…
Inductive BiasMolecular Property PredictionMultiple Instance LearningProperty PredictionSegment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior work and increasing efficiency by decrea…
FoveationImage SegmentationInductive BiasSegmentation+1Parameter-free approximate equivariance for tasks with finite group symmetry
Equivariant neural networks incorporate symmetries through group actions, embedding them as an inductive bias to improve performance on a wide variety of tasks. However, existing equivariant methods can be computationall…
Inductive Bias4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos
We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic comp…
Inductive BiasPositional Encoding meets Persistent Homology on Graphs
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) …
Graph ClassificationGraph Representation LearningInductive BiasOut-of-Distribution Generalization+2Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery
Cryo-EM is a transformational paradigm in molecular biology where computational methods are used to infer 3D molecular structure at atomic resolution from extremely noisy 2D electron microscope images. At the forefront o…
3D ReconstructionInductive BiasInformation Locality as an Inductive Bias for Neural Language Models
Inductive biases are inherent in every machine learning system, shaping how models generalize from finite data. In the case of neural language models (LMs), debates persist as to whether these biases align with or diverg…
Inductive BiasThe Hippocampal Place Field Gradient: An Eigenmode Theory Linking Grid Cell Projections to Multiscale Learning
The hippocampus encodes space through a striking gradient of place field sizes along its dorsal-ventral axis, yet the principles generating this continuous gradient from discrete grid cell inputs remain debated. We propo…
Few-Shot LearningHippocampusInductive BiasRelational reasoning and inductive bias in transformers trained on a transitive inference task
Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning in different learning regimes remain poorly understood. In this work, we investigate how trans…
In-Context LearningInductive BiasRelational ReasoningRodrigues Network for Learning Robot Actions
Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers lack inductive biases that reflect the underlying kinematic structure of articu…
Imitation LearningInductive Biasmotion predictionLearning to cluster neuronal function
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could pot…
ClusteringInductive Bias