5 citations · 14 across the 12 of their papers we have counts for
12 papers
Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation using Knowledge Soft Integration
Kai Ouyang, Chen Tang, Zenghao Chai +4
A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions…
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
Kai Ouyang, Wenhao Zheng, Chen Tang +2
Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suff…
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices
Chen Tang, Li Lyna Zhang, Huiqiang Jiang +6
Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-lat…
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Chen Tang, Kai Ouyang, Zenghao Chai +4
Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using lar…
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images
Patrick Grady, Jeremy A. Collins, Chengcheng Tang +4
Touch plays a fundamental role in manipulation for humans; however, machine perception of contact and pressure typically requires invasive sensors. Recent research has shown that d…
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos
Mathias Parger, Chengcheng Tang, Thomas Neff +4
Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only process…
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach
Chen Tang, Haoyu Zhai, Kai Ouyang +3
Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent "recognition difficulty" differences between various s…
Visual Pressure Estimation and Control for Soft Robotic Grippers
Patrick Grady, Jeremy A. Collins, Samarth Brahmbhatt +4
Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant…
PressureVision: Estimating Hand Pressure from a Single RGB Image
Patrick Grady, Chengcheng Tang, Samarth Brahmbhatt +4
People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the enviro…
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
Chen Tang, Kai Ouyang, Zhi Wang +4
The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort t…
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
Mathias Parger, Chengcheng Tang, Christopher D. Twigg +3
Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a v…
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs
Chen Tang, Wenyu Sun, Zhuqing Yuan +1
To accelerate deep CNN models, this paper proposes a novel spatially adaptive framework that can dynamically generate pixel-wise sparsity according to the input image. The sparse s…