Cheng Chen

Xiaofeng Yang, Cheng Chen, Xulei Yang et al.

Large-scale diffusion models have achieved remarkable performance in generative tasks. Beyond their initial training applications, these models have proven their ability to function as versatile plug-and-play priors. For instance, 2D diffusion models can serve as loss functions to optimize 3D implicit models. Rectified flow, a novel class of generative models, enforces a linear progression from the source to the target distribution and has demonstrated superior performance across various domains. Compared to diffusion-based methods, rectified flow approaches surpass in terms of generation quality and efficiency, requiring fewer inference steps. In this work, we present theoretical and experimental evidence demonstrating that rectified flow based methods offer similar functionalities to diffusion models - they can also serve as effective priors. Besides the generative capabilities of diffusion priors, motivated by the unique time-symmetry properties of rectified flow models, a variant of our method can additionally perform image inversion. Experimentally, our rectified flow-based priors outperform their diffusion counterparts - the SDS and VSD losses - in text-to-3D generation. Our method also displays competitive performance in image inversion and editing.

Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu et al.

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.

Jun Chen, Cheng Chen, Huayue Zhang et al.

Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campaign performance to optimize delivery strategies in advance, resulting in a waste of labour and budget for feedback adjustments. In this paper, we aim to forecast key performance indicators for new campaigns given any certain criteria. Interpretable and accurate results could enable advertisers to manage and optimize their campaign criteria. There are several challenges for this very task. First, platforms usually offer advertisers various criteria when they plan an advertising campaign, it is difficult to estimate campaign performance unifiedly because of the great difference among bidding types. Furthermore, complex strategies applied in bidding system bring great fluctuation on campaign performance, making estimation accuracy an extremely tough problem. To address above challenges, we propose a novel Campaign Performance Forecasting framework, which firstly reproduces campaign performance on historical logs under various bidding types with a unified replay algorithm, in which essential auction processes like match and rank are replayed, ensuring the interpretability on forecast results. Then, we innovatively introduce a multi-task learning method to calibrate the deviation of estimation brought by hard-to-reproduce bidding strategies in replay. The method captures mixture calibration patterns among related forecast indicators to map the estimated results to the true ones, improving both accuracy and efficiency significantly. Experiment results on a dataset from Taobao.com demonstrate that the proposed framework significantly outperforms other baselines by a large margin, and an online A/B test verifies its effectiveness in the real world.