Search Results for author:
Found 9 papers, 3 papers with code
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system.
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
Image Augmentations for GAN Training
We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
Improving Differentially Private Models with Active Learning
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records.
Generating Natural Adversarial Examples
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.
