Latest Articles for Neural Networks

Explore the strengths and weaknesses of RNNs and Transformers in natural language processing.

Introducing Mixture of Low-Rank Experts for improved multi-task learning performance.

A new method streamlines neural architecture design across multiple goals.

New techniques boost the speed and efficiency of MLPs on Intel GPUs.

TransFusion improves contrastive learning with structured attention and effective data processing.

A new method enhances accuracy and speed in estimating Lipschitz constants for deep networks.

This article discusses methods for training two-layer ReLU neural networks efficiently.

This article discusses the importance of parameter initialization in neural networks.

A new method uses reinforcement learning to prune CNNs while training.

D'OH offers new ways to represent signals efficiently.

MambaMixer enhances deep learning efficiency for images and time series forecasting.

This paper discusses the costs and improvements for low-precision neural networks.

A new method enhances how models depict object relationships in generated images.

Explore techniques enhancing full-duplex communication in mmWave MIMO systems.

A look into Hopfield networks for pattern recognition and memory storage.

A new neural network approach improves modeling of fast-slow dynamic systems.

An exploration of how language models predict future tokens compared to human thinking.

Exploring how quantum computing enhances diffusion models for better image generation.

A new method addresses key issues in continual learning: plasticity and forgetting.

Examining performance challenges faced by rotation invariant algorithms in noisy and sparse data scenarios.

Understanding the impact of adversarial attacks on machine learning models.

A look into the various types of cortical interneurons and their roles.

A look into how agents adapt their learning for food gathering.

New methods improve data insights from complex datasets.

New device enhances light-based computing for AI tasks.

Introducing HeMeNet for efficient protein multi-task learning using 3D structures.

Examining GNNs for classifying particle collision events.

Stacking improves efficiency in training deep neural networks by leveraging existing knowledge.

Explore how momentum boosts efficiency in training neural networks.

SpikExplorer simplifies the design of energy-efficient Spiking Neural Networks for portable devices.

Study investigates how near-interpolating models perform on unseen data.

Exploring how transformers favor low sensitivity functions for improved performance.

A new perspective on how neural networks learn features through expert-like paths.

A method to improve machine learning models' knowledge retention during new task training.

Structurally Flexible Neural Networks improve adaptability for diverse tasks.

A new approach to reduce CNN complexity while maintaining performance.

This study examines how small weight initializations impact neural network training.

Research focuses on improving neural network verification with minimal NAP specifications.

A look into how parameter adjustments shape neural network training.

This research reveals task vectors that enhance visual model performance without extra examples.