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Found 6 papers, 0 papers with code
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction
This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process.
Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)
As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput.
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation
In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT.
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.
Visualizing and Understanding Deep Neural Networks in CTR Prediction
Recently, many works have been done on visualizing and analyzing the mechanism of deep neural networks in the areas of image processing and natural language processing.
Personalized Expertise Search at LinkedIn
As of writing this paper, these models serve nearly all live traffic for skills search on LinkedIn homepage as well as LinkedIn recruiter.
