research
∙
09/15/2023
Cure the headache of Transformers via Collinear Constrained Attention
As the rapid progression of practical applications based on Large Langua...
share
research
∙
01/31/2023
BALANCE: Bayesian Linear Attribution for Root Cause Localization
Root Cause Analysis (RCA) plays an indispensable role in distributed dat...
research
∙
05/31/2022
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an imp...
research
∙
04/07/2022
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis
With the ever-growing communication demands and the unceasing miniaturiz...
research
∙
04/26/2021
Variational Pedestrian Detection
Pedestrian detection in a crowd is a challenging task due to a high numb...
research
∙
11/17/2020
Extreme Value Preserving Networks
Recent evidence shows that convolutional neural networks (CNNs) are bias...
research
∙
08/17/2020
AP-Loss for Accurate One-Stage Object Detection
One-stage object detectors are trained by optimizing classification-loss...
research
∙
06/13/2019
Amur Tiger Re-identification in the Wild
Monitoring the population and movements of endangered species is an impo...
research
∙
04/12/2019
Towards Accurate One-Stage Object Detection with AP-Loss
One-stage object detectors are trained by optimizing classification-loss...
research
∙
12/05/2018
Knowledge Distillation from Few Samples
Current knowledge distillation methods require full training data to dis...
research
∙
11/16/2018
Composite Binary Decomposition Networks
Binary neural networks have great resource and computing efficiency, whi...
research
∙
09/25/2018
Object Detection from Scratch with Deep Supervision
We propose Deeply Supervised Object Detectors (DSOD), an object detectio...
research
∙
08/16/2018
Network Decoupling: From Regular to Depthwise Separable Convolutions
Depthwise separable convolution has shown great efficiency in network de...
research
∙
07/29/2018
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Object detection has made great progress in the past few years along wit...
research
∙
06/13/2018
Learning Visual Knowledge Memory Networks for Visual Question Answering
Visual question answering (VQA) requires joint comprehension of images a...
research
∙
10/17/2017
Boosting Adversarial Attacks with Momentum
Deep neural networks are vulnerable to adversarial examples, which poses...
research
∙
08/22/2017
Learning Efficient Convolutional Networks through Network Slimming
The deployment of deep convolutional neural networks (CNNs) in many real...
research
∙
08/03/2017
DSOD: Learning Deeply Supervised Object Detectors from Scratch
We present Deeply Supervised Object Detector (DSOD), a framework that ca...
research
∙
08/03/2017
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
Low-bit deep neural networks (DNNs) become critical for embedded applica...
research
∙
04/05/2017
Weakly Supervised Dense Video Captioning
This paper focuses on a novel and challenging vision task, dense video c...
research
∙
09/08/2015
Deep Attributes from Context-Aware Regional Neural Codes
Recently, many researches employ middle-layer output of convolutional ne...
research
∙
07/06/2014
