Search Results for author:
Found 12 papers, 3 papers with code
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations.
A novel channel pruning method for deep neural network compression
In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm.
Action Machine: Rethinking Action Recognition in Trimmed Videos
On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97. 2% and 94. 3% on cross-view and cross-subject respectively.
Ranked #1 on
Action Recognition
on UTD-MHAD
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks
In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.
Multi-loss-aware Channel Pruning of Deep Networks
Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one.
Cluster Regularized Quantization for Deep Networks Compression
Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and computational cost.
Angle-based Search Space Shrinking for Neural Architecture Search
Comprehensive experiments show that ABS can dramatically enhance existing NAS approaches by providing a promising shrunk search space.
DPUV3INT8: A Compiler View to programmable FPGA Inference Engines
We have a FPGA design, we make it fast, efficient, and tested for a few important examples.
Exploring the impact of weather on Metro demand forecasting using machine learning method
Urban rail transit provides significant comprehensive benefits such as large traffic volume and high speed, serving as one of the most important components of urban traffic construction management and congestion solution.
Masked Autoencoders Are Robust Neural Architecture Search Learners
Neural Architecture Search (NAS) currently relies heavily on labeled data, which is both expensive and time-consuming to acquire.
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices.
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance.
