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Found 16 papers, 11 papers with code
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
BIOT: Cross-data Biosignal Learning in the Wild
Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that \method outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats.
ManyDG: Many-domain Generalization for Healthcare Applications
Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e. g., 3. 7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.
MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction
We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML.
GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion
Our variant GOCPTE shows up to 1:2% and 5:5% fitness improvement on two datasets with about 20% speedup compared to the best model.
Self-supervised EEG Representation Learning for Automatic Sleep Staging
Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
Change Matters: Medication Change Prediction with Recurrent Residual Networks
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions.
Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US
We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i. e., in lieu of social distancing).
Physics and Society Computers and Society Social and Information Networks
Revisiting Over-smoothing in Deep GCNs
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs).
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
In the synthetic dataset, our model reduces error by 40%.
An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
We develop a novel induced relational graph convolutional network (IR-GCN) framework to address the question.
