Search Results for author:
Found 10 papers, 2 papers with code
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models
Our results indicate three key findings: (1) using generative compression, it is feasible to leverage highly compressed data while incurring a negligible impact on machine perceptual quality; (2) machine perceptual quality correlates strongly with deep similarity metrics, indicating a crucial role of these metrics in the development of machine-oriented codecs; and (3) using lossy compressed datasets, (e. g. ImageNet) for pre-training can lead to counter-intuitive scenarios where lossy compression increases machine perceptual quality rather than degrading it.
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.
Distilled Pruning: Using Synthetic Data to Win the Lottery
This work introduces a novel approach to pruning deep learning models by using distilled data.
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network.
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms
Neural architecture search (NAS), the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural language processing, has seen rapid growth in the machine learning research community.
A Hardware-Aware System for Accelerating Deep Neural Network Optimization
These methods decouple the super-network training from the sub-network search and thus decrease the computational burden of specializing to different hardware platforms.
Structured Citation Trend Prediction Using Graph Neural Networks
Academic citation graphs represent citation relationships between publications across the full range of academic fields.
