This work aims at decreasing the end-to-end generation latency of large
...
Voxel-based methods have achieved state-of-the-art performance for 3D ob...
Diffusion probabilistic models (DPMs) are a new class of generative mode...
This paper investigates the multi-agent navigation problem, which requir...
We consider the problem of cooperative exploration where multiple robots...
Performing data-intensive tasks in the von Neumann architecture is
chall...
Data quantization is an effective method to accelerate neural network
tr...
Learning discriminative representations for subtle localized details pla...
Graph convolutional network (GCN), an emerging algorithm for graph compu...
One-shot Neural Architecture Search (NAS) has been widely used to discov...
Intellectual property (IP) piracy has become a non-negligible problem as...
Computing-in-memory (CiM) is a promising technique to achieve high energ...
Compute-in-memory (CiM) is a promising approach to improving the computi...
Autonomous exploration and mapping of unknown terrains employing single ...
Discovering hazardous scenarios is crucial in testing and further improv...
We introduce a curriculum learning algorithm, Variational Automatic
Curr...
We consider the task of visual indoor exploration with multiple agents, ...
Adversarial attacks have rendered high security risks on modern deep lea...
Convolutional neural networks (CNNs) are vulnerable to adversarial examp...
Neural Architecture Search (NAS) has received extensive attention due to...
Binary Neural Networks (BNNs) have received significant attention due to...
Learning depth and ego-motion from unlabeled videos via self-supervision...
Resistive Random Access Memory (RRAM) is an emerging device for
processi...
Neural architecture search (NAS) recently received extensive attention d...
In this paper, we tackle the issue of physical adversarial examples for
...
Graph Neural Networks (GNNs) have achieved significant improvements in
v...
Convolutional Neural Networks (CNNs) have been widely used in many field...
Budgeted pruning is the problem of pruning under resource constraints. I...
This work proposes a novel Graph-based neural ArchiTecture Encoding Sche...
FPGAs have shown great potential in providing low-latency and
energy-eff...
With the fast evolvement of embedded deep-learning computing systems,
ap...
This work focuses on combining nonparametric topic models with Auto-Enco...
Recently, Deep Learning (DL), especially Convolutional Neural Network (C...
Fingertip detection plays an important role in human computer interactio...
Accurate 3D hand pose estimation plays an important role in Human Machin...
Recent researches on neural network have shown great advantage in comput...
Hand pose estimation from monocular depth images is an important and
cha...
Long Short-Term Memory (LSTM) is widely used in speech recognition. In o...
With many advantageous features, softness and better biocompatibility,
f...
Physical computing is a technology utilizing the nature of electronic de...
This paper develops a new video compression approach based on underdeter...