Zobrazeno 1 - 10
of 249
pro vyhledávání: '"CUMMINGS, DANIEL"'
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. Traditional approaches, notably pseudo-Voigt fitting, demand significant computational resource
Externí odkaz:
http://arxiv.org/abs/2312.05978
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. While such fusion promises a holi
Externí odkaz:
http://arxiv.org/abs/2310.18812
Autor:
McDermott, Luke, Cummings, Daniel
With the rise in interest of sparse neural networks, we study how neural network pruning with synthetic data leads to sparse networks with unique training properties. We find that distilled data, a synthetic summarization of the real data, paired wit
Externí odkaz:
http://arxiv.org/abs/2310.18769
Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning. Current models, although promising, reveal scalability limitations with increasing modalities as they rely he
Externí odkaz:
http://arxiv.org/abs/2310.16856
Autor:
McDermott, Luke, Cummings, Daniel
This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenari
Externí odkaz:
http://arxiv.org/abs/2307.03364
Autor:
Cummings, Daniel, Sarah, Anthony, Sridhar, Sharath Nittur, Szankin, Maciej, Munoz, Juan Pablo, Sundaresan, Sairam
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. While considerable effort has been employed towards improv
Externí odkaz:
http://arxiv.org/abs/2205.10358
Autor:
Sarah, Anthony, Cummings, Daniel, Sridhar, Sharath Nittur, Sundaresan, Sairam, Szankin, Maciej, Webb, Tristan, Munoz, J. Pablo
Recent advances in Neural Architecture Search (NAS) which extract specialized hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic "super-network" have become increasingly popular. While considerable effort has been employed
Externí odkaz:
http://arxiv.org/abs/2202.12954
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
Externí odkaz:
http://arxiv.org/abs/2202.12934
Autor:
Cummings, Daniel, Nassar, Marcel
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and pra
Externí odkaz:
http://arxiv.org/abs/2104.02562
Autor:
Kipp, Kaylee, Cummings, Daniel B., Goehl, Dan, Wade, H.H., Davidson, John M., Renter, David, Verocai, Guilherme G., Rash, Lea
Publikováno v:
In Veterinary Parasitology July 2023 319
