A5032345308- Advanced Neural Network Applications
- Recommender Systems and Techniques
- Advanced Bandit Algorithms Research
- Brain Tumor Detection and Classification
- Neural Networks and Applications
- Receptor Mechanisms and Signaling
- Advanced Image and Video Retrieval Techniques
- Neuropeptides and Animal Physiology
- Domain Adaptation and Few-Shot Learning
- Diabetes Treatment and Management
- Advanced Text Analysis Techniques
- Stochastic Gradient Optimization Techniques
Google (United States)
2023-2024
Intel (United Kingdom)
2018
ConfometRx (United States)
2017
High throughput and low latency inference of deep neural networks are critical for the deployment learning applications. This paper presents efficient techniques IntelCaffe, first Intel(R) optimized framework that supports 8-bit precision model optimization convolutional on Xeon(R) Scalable Processors. The is automatically generated with a calibration process from FP32 without need fine-tuning or retraining. We show ResNet-50, Inception-v3 SSD improved by 1.38X-2.9X 1.35X-3X respectively...
Sequential recommenders have been widely used in industry due to their strength modeling user preferences. While these models excel at learning a user's positive interests, less attention has paid from negative feedback. Negative feedback is an important lever of control, and comes with expectation that should respond quickly reduce similar recommendations the user. However, signals are often ignored training objective sequential retrieval models, which primarily aim predicting interactions....
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact relevant metrics. This ``gold standard'' comes at high cost, however, terms cycle time, user and potential retention. In developing for ``onboarding'' users, these costs can be especially problematic, since on-boarding occurs only once. this work, we describe simulation methodology used augment (and reduce) the use experiments. We illustrate its...
High throughput and low latency inference of deep neural networks are critical for the deployment learning applications. This paper presents efficient techniques IntelCaffe, first Intel optimized framework that supports 8-bit precision model optimization convolutional on Xeon Scalable Processors. The is automatically generated with a calibration process from FP32 without need fine-tuning or retraining. We show ResNet-50, Inception-v3 SSD improved by 1.38X-2.9X 1.35X-3X respectively...