Search Results for author:
Found 8 papers, 0 papers with code
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions.
Effects of Dynamic and Stochastic Travel Times on the Operation of Mobility-on-Demand Services
As travel times in real street networks are dynamic and stochastic, assigned routes considered feasible by the control algorithm in one time step might become infeasible in the next.
Autonomous Vehicles as a Sensor: Simulating Data Collection Process
Nevertheless, the relentless progression in autonomous driving technology has catalyzed an increasing interest in capitalizing on the extensive potential of on-board sensor data, giving rise to a novel concept known as "Autonomous Vehicles as a Sensor" (AVaaS).
Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis
Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN).
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis
To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN.
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming
Adaptive Bit Rate (ABR) decision plays a crucial role for ensuring satisfactory Quality of Experience (QoE) in video streaming applications, in which past network statistics are mainly leveraged for future network bandwidth prediction.
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation
To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real images to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator's feature space.
Practical Issues of Action-conditioned Next Image Prediction
Our major finding is that action tiling encoding is the most important factor leading to the remarkable performance of the CDNA model.
