We propose a novel combination of a variant beam search algorithm and a learned heuristic for solving the general orienteering problem.
The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively.
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
In this work, we explore the option of backdoor attacks to automatic speech recognition systems where we inject inaudible triggers.
The large-scale OCTA dataset is available at https://doi. org/10. 5281/zenodo. 5111975, https://doi. org/10. 5281/zenodo. 5111972.
Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context.
Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior.
For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature.
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.
In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands.
In optics, various photonic topological circuits have been developed, which were based on classical emulation of either quantum spin Hall effect or quantum valley Hall effect.
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently.
Negative longitudinal magnetoresistance (NLMR) has been reported in a variety of materials and has attracted extensive attention as an electrotransport hallmark of topological Weyl semimetals.
Materials Science Mesoscale and Nanoscale Physics
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
We also demonstrate that the perturbation budget generator can produce semantically-meaningful budgets, which implies that the generator can capture contextual information and the sensitivity of different features in a given image.
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks, e. g., image classification.
Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs).
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted.
In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver.
Building open-domain chatbots is a challenging area for machine learning research.
On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive.
In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images.
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
Function transformation, such as f(x, y) and f(x, y, z), can transform two, three, or multiple input/observation variables (in biology, it generally refers to the observed/measured value of biomarkers, biological characteristics, or other indicators) into a new output variable (new characteristics or indicators).
AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC).
Constructing effective representations is a critical but challenging problem in multimedia understanding.
For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model.
Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the capacities of the agents involved.