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Found 6 papers, 1 papers with code
Neural Architecture Codesign for Fast Bragg Peak Analysis
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy.
Linear Mode Connectivity in Sparse Neural Networks
We find that distilled data, a synthetic summarization of the real data, paired with Iterative Magnitude Pruning (IMP) unveils a new class of sparse networks that are more stable to SGD noise on the real data, than either the dense model, or subnetworks found with real data in IMP.
UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification
Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modalities into a unified representation.
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification
Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning.
A Generalization of Continuous Relaxation in Structured Pruning
Structured pruning asserts that while large networks enable us to find solutions to complex computer vision problems, a smaller, computationally efficient sub-network can be derived from the large neural network that retains model accuracy but significantly improves computational efficiency.
Distilled Pruning: Using Synthetic Data to Win the Lottery
This work introduces a novel approach to pruning deep learning models by using distilled data.
