Publications associées à Linear Complexity Self-Attention With 3rd Order Polynomials

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Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Linear Complexity Self-Attention With 3rd Order Polynomials

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Network time series forecasting in photovoltaics power production

The neural correlates of topographical disorientation-a lesion analysis study

Efficient Continual Finite-Sum Minimization

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Optimizing Dynamic Aperture Studies with Active Learning

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Network time series forecasting in photovoltaics power production

The neural correlates of topographical disorientation-a lesion analysis study

Efficient Continual Finite-Sum Minimization

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Optimizing Dynamic Aperture Studies with Active Learning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning