Privacy-preserving vector mean estimation is a crucial primitive in fede...
Deep learning classifiers are crucial in the age of artificial intellige...
Effective control of credit risk is a key link in the steady operation o...
We investigate polynomial-time approximation schemes for the classic 0-1...
Perception that involves multi-object detection and tracking, and trajec...
Multi-die FPGAs are crucial components in modern computing systems,
part...
Packing is a required step in a typical FPGA CAD flow. It has high impac...
We investigate pseudopolynomial-time algorithms for Bounded Knapsack and...
Most prior semantic segmentation methods have been developed for day-tim...
To serve the intricate and varied demands of image editing, precise and
...
Protecting user privacy is a major concern for many machine learning sys...
As robotics technology advances, dense point cloud maps are increasingly...
Vision transformers have achieved remarkable success in computer vision ...
Recently, cross-source point cloud registration from different sensors h...
A surge of interest has emerged in utilizing Transformers in diverse vis...
The problem of deep long-tailed learning, a prevalent challenge in the r...
Few-shot audio classification is an emerging topic that attracts more an...
Trajectory prediction for autonomous driving must continuously reason th...
Despite the remarkable progress in semantic segmentation tasks with the
...
In defect prediction community, many defect prediction models have been
...
We consider the SUBSET SUM problem and its important variants in this pa...
Much of the value that IoT (Internet-of-Things) devices bring to “smart”...
A new generation of aerial vehicles is hopeful to be the next frontier f...
Feature selection is the problem of selecting a subset of features for a...
Performing neural network inference on encrypted data without decryption...
Trajectory prediction has been a long-standing problem in intelligent sy...
In unsupervised domain adaptation (UDA), directly adapting from the sour...
Federated learning (FL) is a promising distributed framework for
collabo...
Package theft detection has been a challenging task mainly due to lack o...
Adversarial attacks on data-driven algorithms applied in pow-er system w...
The resilience of a voting system has been a central topic in computatio...
We study a bilevel optimization problem which is a zero-sum Stackelberg ...
Comparing test suite effectiveness metrics has always been a research
ho...
We propose a novel framework to learn 3D point cloud semantics from 2D
m...
Adversarial learning has achieved remarkable performances for unsupervis...
Recently, a variety of neural models have been proposed for lyrics
gener...
Balancing social utility and equity in distributing limited vaccines
rep...
Knowledge Distillation is becoming one of the primary trends among neura...
There are numerous types of programming languages developed in the last
...
Reversible data hiding in encrypted images is an eff ective technique fo...
We consider a classical scheduling problem on m identical machines. For ...
In this paper, we study feature cross search as a fundamental primitive ...
Different from static images, videos contain additional temporal and spa...
In this paper, we investigate the sample complexity of policy evaluation...
The boom of DL technology leads to massive DL models built and shared, w...
Background. Many mutation reduction strategies, which aim to reduce the
...
The label quality of defect data sets has a direct influence on the
reli...
This paper presents EDSC, a novel smart contract platform design based o...
To achieve good performance in face recognition, a large scale training
...
We propose and analyze an optimal mass transport method for a random gen...