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Found 10 papers, 2 papers with code
Incorporating Zoning Information into Argument Mining from Biomedical Literature
Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers.
Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction
Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values.
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories.
RUSH: Robust Contrastive Learning via Randomized Smoothing
In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing.
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm
We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks.
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods.
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.
Polarons in a ferromagnetic spinor Bose-Einstein condensates
We investigate the polarons formed by immersing a spinor impurity in a ferromagnetic state of $F=1$ spinor Bose-Einstein condensate.
Quantum Gases
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
Unsupervised anomaly detection plays a crucial role in many critical applications.
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.
