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Regularization of polynomial networks for image recognition
Abstract Deep Neural Networks (DNNs) have obtained impressive performance across
tasks, however they still remain as black boxes, eg, hard to theoretically analyze. At the …
tasks, however they still remain as black boxes, eg, hard to theoretically analyze. At the …
Deep neural networks tend to extrapolate predictably
Conventional wisdom suggests that neural network predictions tend to be unpredictable and
overconfident when faced with out-of-distribution (OOD) inputs. Our work reassesses this …
overconfident when faced with out-of-distribution (OOD) inputs. Our work reassesses this …
Linear Complexity Self-Attention With Order Polynomials
Self-attention mechanisms and non-local blocks have become crucial building blocks for
state-of-the-art neural architectures thanks to their unparalleled ability in capturing long …
state-of-the-art neural architectures thanks to their unparalleled ability in capturing long …
Comparing spectral bias and robustness for two-layer neural networks: Sgd vs adaptive random fourier features
We present experimental results highlighting two key differences resulting from the choice of
training algorithm for two-layer neural networks. The spectral bias of neural networks is well …
training algorithm for two-layer neural networks. The spectral bias of neural networks is well …
Quadratic residual multiplicative filter neural networks for efficient approximation of complex sensor signals
In this research, we present an innovative Quadratic Residual Multiplicative Filter Neural
Network (QRMFNN) to effectively learn extremely complex sensor signals as a low …
Network (QRMFNN) to effectively learn extremely complex sensor signals as a low …
Polynomial Implicit Neural Framework for Promoting Shape Awareness in Generative Models
Polynomial functions have been employed to represent shape-related information in 2D and
3D computer vision, even from the very early days of the field. In this paper, we present a …
3D computer vision, even from the very early days of the field. In this paper, we present a …
The extrapolation power of implicit models
In this paper, we investigate the extrapolation capabilities of implicit deep learning models in
handling unobserved data, where traditional deep neural networks may falter. Implicit …
handling unobserved data, where traditional deep neural networks may falter. Implicit …
TMPNN: high-order polynomial regression based on Taylor map factorization
The paper presents Taylor Map Polynomial Neural Network (TMPNN), a novel form of very
high-order polynomial regression, in which the same coefficients for a lower-to-moderate …
high-order polynomial regression, in which the same coefficients for a lower-to-moderate …
Polynomial Neural Barrier Certificate Synthesis of Hybrid Systems via Counterexample Guidance
This article presents a novel approach to the safety verification of hybrid systems by
synthesizing neural barrier certificates (BCs) via counterexample-guided neural network …
synthesizing neural barrier certificates (BCs) via counterexample-guided neural network …
Architecture Design: From Neural Networks to Foundation Models
Historically, we are taught to use task-dependent architecture design and objectives to
tackle data science tasks. Counter intuitively, this dogma has been proven (partly) wrong by …
tackle data science tasks. Counter intuitively, this dogma has been proven (partly) wrong by …