Search Results for author:
Found 8 papers, 4 papers with code
Variational Optimization for Quantum Problems using Deep Generative Networks
Optimization is one of the keystones of modern science and engineering.
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation
Large-scale Pre-Training Vision-Language Model such as CLIP has demonstrated outstanding performance in zero-shot classification, e. g. achieving 76. 3% top-1 accuracy on ImageNet without seeing any example, which leads to potential benefits to many tasks that have no labeled data.
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
The era of edge computing has arrived.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias.
Ranked #10 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking
Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.
NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision
These systems usually run multiple applications concurrently and their available resources at runtime are dynamic due to events such as starting new applications, closing existing applications, and application priority changes.
