Search Results for author:
Found 9 papers, 4 papers with code
AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding
We evaluated AURORA both on a set of 12 apps with known tarpits from prior work, and on a new set of five of the most popular apps from the Google Play store.
One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP.
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion
Our work presents a new lens to understand these models better by focusing on their handling of visual illusions -- a complex interplay of perception and logic.
Breaking the Curse of Quality Saturation with User-Centric Ranking
A key puzzle in search, ads, and recommendation is that the ranking model can only utilize a small portion of the vastly available user interaction data.
Ignorance Is Bliss: The Screening Effect of (Noisy) Information
While more precise information allows the firm to make ex-post more efficient investment decisions, noisier information has an ex-ante screening effect that allows the firm to attract on-average better managers.
Learning to Dub Movies via Hierarchical Prosody Models
Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference.
LS-GAN: Iterative Language-based Image Manipulation via Long and Short Term Consistency Reasoning
To address this issue, we propose a novel Long and Short term consistency reasoning Generative Adversarial Network (LS-GAN), which enhances the awareness of previous objects with current instruction and better maintains the consistency with the user's intent under the continuous iterations.
Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method
In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems.
Testing Untestable Neural Machine Translation: An Industrial Case
To fill the gap of lacking test oracle for in-vivo testing of an NMT system, in this paper, we propose a new approach for automatically identifying translation failures, without requiring reference translations for a translation task; our approach can directly serve as a test oracle for in-vivo testing.
