Zobrazeno 1 - 10
of 498
pro vyhledávání: '"Gross, Warren J."'
Autor:
Tayaranian, Mohammadreza, Mozafari, Seyyed Hasan, Meyer, Brett H., Clark, James J., Gross, Warren J.
Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve this performance, these models are first pre-trained on general corpus and then fine-tuned on downstream tasks.
Externí odkaz:
http://arxiv.org/abs/2407.08887
GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized with achievable average and worst-case decoding latency. This paper introduces step-GRAND, a soft-input varian
Externí odkaz:
http://arxiv.org/abs/2307.07133
We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf point-based network, as a super-network to enable weight sharing and reduc
Externí odkaz:
http://arxiv.org/abs/2304.11207
We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To
Externí odkaz:
http://arxiv.org/abs/2303.16322
In this paper, we introduce stochastic simulated quantum annealing (SSQA) for large-scale combinatorial optimization problems. SSQA is designed based on stochastic computing and quantum Monte Carlo, which can simulate quantum annealing (QA) by using
Externí odkaz:
http://arxiv.org/abs/2302.12454
Knowledge distillation (KD) has gained a lot of attention in the field of model compression for edge devices thanks to its effectiveness in compressing large powerful networks into smaller lower-capacity models. Online distillation, in which both the
Externí odkaz:
http://arxiv.org/abs/2212.12965
Autor:
Vucetic, Danilo, Tayaranian, Mohammadreza, Ziaeefard, Maryam, Clark, James J., Meyer, Brett H., Gross, Warren J.
Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many prior works aim to improve inference efficiency via compression techniques, e.g., pruning, these works do not explicitly address the computational challe
Externí odkaz:
http://arxiv.org/abs/2208.02070
Autor:
Vucetic, Danilo, Tayaranian, Mohammadreza, Ziaeefard, Maryam, Clark, James J., Meyer, Brett H., Gross, Warren J.
Resource-constrained devices are increasingly the deployment targets of machine learning applications. Static models, however, do not always suffice for dynamic environments. On-device training of models allows for quick adaptability to new scenarios
Externí odkaz:
http://arxiv.org/abs/2205.01541
Publikováno v:
GLOBECOM 2022 Workshops
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND tries to guess the channel noise by generating test error patterns (TEPs), and the sequence of the TEPs is the m
Externí odkaz:
http://arxiv.org/abs/2205.00030
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by existing methods, we propose a new framework to learn the quantization i
Externí odkaz:
http://arxiv.org/abs/2202.12422