Search Results for author:
Found 10 papers, 3 papers with code
Semantic Communication-Enabled Wireless Adaptive Panoramic Video Transmission
In this paper, we propose an adaptive panoramic video semantic transmission (APVST) network built on the deep joint source-channel coding (Deep JSCC) structure for the efficient end-to-end transmission of panoramic videos.
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets
In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets.
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph
Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks.
Learning Deep Neural Networks under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance.
Provable Robust Learning under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance.
Robust Collaborative Learning with Noisy Labels
In this paper, we study the underlying mechanism of how disagreement and agreement between networks can help reduce the noise in gradients and develop a novel framework called Robust Collaborative Learning (RCL) that leverages both disagreement and agreement among networks.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients.
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling.
