In currently available literature, no tracking-by-detection (TBD)
paradi...
Magnetic resonance imaging (MRI) always suffered from the problem of lon...
Reinforcement learning is still struggling with solving long-horizon sur...
The 4D Millimeter wave (mmWave) radar is a promising technology for vehi...
Multi-label classification (MLC) suffers from the inevitable label noise...
The 6G Internet poses intense demands for intelligent and customized des...
In view of the classical visual servoing trajectory planning method whic...
Task automation of surgical robot has the potentials to improve surgical...
Motion artifact reduction is one of the most concerned problems in magne...
Surgical robot automation has attracted increasing research interest ove...
Radar, the only sensor that could provide reliable perception capability...
Efficient trajectory generation in complex dynamic environment stills re...
Unmanned surface vessels (USVs) are widely used in ocean exploration and...
Vision transformers (ViTs) are usually considered to be less light-weigh...
To break the bottlenecks of mainstream cloud-based machine learning (ML)...
Unlike existing knowledge distillation methods focus on the baseline
set...
The Internet of intelligence is conceived as an emerging networking para...
High-resolution (HR) MRI is critical in assisting the doctor's diagnosis...
Stimulated by the analysis of a dataset from China about Covid-19, we pr...
Structural re-parameterization (Rep) methods achieve noticeable improvem...
Evaluation metrics in machine learning are often hardly taken as loss
fu...
IEEE 802.1Qbv (TAS) is the most widely used technique in Time-Sensitive
...
Learning informative representations from image-based observations is of...
Training a good supernet in one-shot NAS methods is difficult since the
...
In this paper, we propose a gradient boosting algorithm for large-scale
...
One-shot neural architecture search (NAS) methods significantly reduce t...
In coded aperture snapshot spectral imaging (CASSI) system, the real-wor...
Searching for network width is an effective way to slim deep neural netw...
In the last few years, image denoising has benefited a lot from the fast...
Differentiable neural architecture search (DARTS) has gained much succes...
Graph convolutional networks (GCNs) have been employed as a kind of
sign...
To deploy a well-trained CNN model on low-end computation edge devices, ...
Quantum error mitigation techniques are at the heart of quantum computat...
In light of recent work studying massive functional/longitudinal data, s...
Training a supernet matters for one-shot neural architecture search (NAS...
The proliferating number of devices with short payloads as well as low p...
To provide proactive fault tolerance for modern cloud data centers, exte...
This paper is concerned with an important issue in finite mixture modell...