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Found 13 papers, 5 papers with code
Multi-scale Generative Modeling for Fast Sampling
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models have shown that transitioning to the wavelet domain offers a promising alternative.
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, whereas their adaptation on image restoration, particularly within computational microscopy remains theoretically and empirically underexplored.
A structure-aware framework for learning device placements on computation graphs
To bridge the gap between encoder-placer and grouper-placer techniques, we propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning.
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems.
Exploring Neuron Interactions and Emergence in LLMs: From the Multifractal Analysis Perspective
Prior studies on the emergence in large models have primarily focused on how the functional capabilities of large language models (LLMs) scale with model size.
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
Discovering Malicious Signatures in Software from Structural Interactions
Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space.
Leveraging Reinforcement Learning and Large Language Models for Code Optimization
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives
Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles.
Neuro-Inspired Hierarchical Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world.
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
Non-Linear Operator Approximations for Initial Value Problems
The Padé exponential operator uses a $\textit{recurrent structure with shared parameters}$ to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession.
Multiwavelet-based Operator Learning for Differential Equations
The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space.
