Demo Data from EDBT 2026 / reviewers in the wild / expert
Chenzhuo Zhu
dblp:190/4840
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2017
0000-0001-5933-6922ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-authorSystems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Efficient and distributed learning · 67% Robot manipulation · 33% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › tactile sensing › vision-based tactile sensing
gelsight sensor |
0.3 | 1 | 2017 | Shape-independent hardness estimation using deep learning and a GelSight tactile sensor · ICRA 2017 |
Machine learning › Efficient and distributed learning
model compression |
0.3 | 1 | 2017 | Trained Ternary Quantization · ICLR (Poster) 2017 |
Machine learning › Efficient and distributed learning › model compression
quantization |
0.3 | 1 | 2017 | Trained Ternary Quantization · ICLR (Poster) 2017 |
Machine learning › Efficient and distributed learning › model compression › quantization
quantized neural network |
0.3 | 1 | 2017 | Trained Ternary Quantization · ICLR (Poster) 2017 |
Robotics › Robot manipulation
tactile sensing |
0.3 | 1 | 2017 | Shape-independent hardness estimation using deep learning and a GelSight tactile sensor · ICRA 2017 |
Machine learning › Efficient and distributed learning › model compression › quantization
ternary quantization |
0.3 | 1 | 2017 | Trained Ternary Quantization · ICLR (Poster) 2017 |
Methods — techniques the papers use, named apart from their topics
recurrent neural network · 0.3quantization-aware training · 0.3deep convolutional neural network · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Trained Ternary Quantization
Chenzhuo Zhu, Song Han 0003, Huizi Mao, William J. Dally |
ICLR (Poster) | 1 |
| 2017 | Shape-independent hardness estimation using deep learning and a GelSight tactile sensorabstractHardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale. Wenzhen Yuan 0001, Chenzhuo Zhu, Andrew Owens, Mandayam A. Srinivasan, Edward H. Adelson |
ICRA | 2 |
| 2017 | Boundary-aware box refinement for object proposal generation
Xiaozhi Chen, Huimin Ma 0001, Chenzhuo Zhu, Xiang Wang 0003, Zhichen Zhao |
Neurocomputing | 3 |