Cover Image

Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Movahedi, Sajad, Orvieto, Antonio, Moosavi-Dezfooli, Seyed-Mohsen
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Revisiting DeepFool: generalization and improvement
by: Abdollahpoorrostam, Alireza, et al.
Published: (2023)

On the Anisotropy of Score-Based Generative Models
by: Floros, Andreas, et al.
Published: (2025)

Fixed-Point RNNs: Interpolating from Diagonal to Dense
by: Movahedi, Sajad, et al.
Published: (2025)

Trustworthy Image Super-Resolution via Generative Pseudoinverse
by: Floros, Andreas, et al.
Published: (2025)

Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost Asymmetry
by: Salmani, Mahdi, et al.
Published: (2025)

Thinking into the Future: Latent Lookahead Training for Transformers
by: Noci, Lorenzo, et al.
Published: (2026)

Selective Rotary Position Embedding
by: Movahedi, Sajad, et al.
Published: (2025)

Explaining Grokking in Transformers through the Lens of Inductive Bias
by: Singh, Jaisidh, et al.
Published: (2026)

Theoretical Analysis of Inductive Biases in Deep Convolutional Networks
by: Wang, Zihao, et al.
Published: (2023)

Characterising the Inductive Biases of Neural Networks on Boolean Data
by: Mingard, Chris, et al.
Published: (2025)

LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
by: Khodabandeh, Borna, et al.
Published: (2025)

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
by: Romero, David W.
Published: (2024)

Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
by: Mittal, Daksh, et al.
Published: (2025)

Demystifying the Hypercomplex: Inductive Biases in Hypercomplex Deep Learning
by: Comminiello, Danilo, et al.
Published: (2024)

Clustering Inductive Biases with Unrolled Networks
by: Huml, Jonathan, et al.
Published: (2023)

An Uncertainty Principle for Linear Recurrent Neural Networks
by: François, Alexandre, et al.
Published: (2025)

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
by: Goldblum, Micah, et al.
Published: (2023)

Language Models Need Inductive Biases to Count Inductively
by: Chang, Yingshan, et al.
Published: (2024)

Instilling Inductive Biases with Subnetworks
by: Zhang, Enyan, et al.
Published: (2023)

Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
by: Floros, Andreas, et al.
Published: (2024)

Adam Simplified: Bias Correction Debunked
by: Laing, Sam, et al.
Published: (2025)

Revisiting associative recall in modern recurrent models
by: Okpekpe, Destiny, et al.
Published: (2025)

Leveraging Geometric Visual Illusions as Perceptual Inductive Biases for Vision Models
by: Yang, Haobo, et al.
Published: (2025)

Transformers Are Born Biased: Structural Inductive Biases at Random Initialization and Their Practical Consequences
by: Li, Siquan, et al.
Published: (2026)

Inductive Biases for Zero-shot Systematic Generalization in Language-informed Reinforcement Learning
by: Dijujin, Negin Hashemi, et al.
Published: (2025)

Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
by: Kuciński, Łukasz, et al.
Published: (2021)

Stimulus-to-Stimulus Learning in RNNs with Cortical Inductive Biases
by: Vafidis, Pantelis, et al.
Published: (2024)

SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
by: Liu, Yang, et al.
Published: (2023)

Incorporating Inductive Biases to Energy-based Generative Models
by: Li, Yukun, et al.
Published: (2025)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
by: Orvieto, Antonio, et al.
Published: (2024)

In Search of Adam's Secret Sauce
by: Orvieto, Antonio, et al.
Published: (2025)

Super Consistency of Neural Network Landscapes and Learning Rate Transfer
by: Noci, Lorenzo, et al.
Published: (2024)

The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics
by: Bachmann, Gregor, et al.
Published: (2026)

Priors in Time: Missing Inductive Biases for Language Model Interpretability
by: Lubana, Ekdeep Singh, et al.
Published: (2025)

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
by: Kuang, Yilun, et al.
Published: (2025)

Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by: Zucchet, Nicolas, et al.
Published: (2024)

Universal Dynamics of Warmup Stable Decay: understanding WSD beyond Transformers
by: Belloni, Annalisa, et al.
Published: (2026)

Improved state mixing in higher-order and block diagonal linear recurrent networks
by: Dubinin, Igor, et al.
Published: (2026)

When, Where and Why to Average Weights?
by: Ajroldi, Niccolò, et al.
Published: (2025)

Lyapunov Stability Learning with Nonlinear Control via Inductive Biases
by: Lu, Yupu, et al.
Published: (2025)