Search Results for author:
Found 14 papers, 5 papers with code
Generative AI for Hate Speech Detection: Evaluation and Findings
In addition, we explore and compare the performance of the finetuned LLMs with zero-shot hate detection using a GPT-3. 5 model.
Deep Compressed Learning for 3D Seismic Inversion
We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources.
An F-ratio-Based Method for Estimating the Number of Active Sources in MEG
Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.
Encoder-Decoder Architecture for 3D Seismic Inversion
This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys.
Brain Source Localization by Alternating Projection
We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals.
Character-level HyperNetworks for Hate Speech Detection
Our results show that the proposed HyperNetworks achieve performance that is competitive, and better in some cases, than these pretrained language models, while being smaller by orders of magnitude.
Data-driven Taylor-Galerkin finite-element scheme for convection problems
High-fidelity large-eddy simulations (LES) of high Reynolds number flows are essential to design low-carbon footprint energy conversion devices.
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech
Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization.
MEG Source Localization via Deep Learning
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals.
Towards Hate Speech Detection at Large via Deep Generative Modeling
Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence.
Localization of MEG and EEG Brain Signals by Alternating Projection
We present a novel solution to the problem of localization of MEG and EEG brain signals.
Deep Learning of Compressed Sensing Operators with Structural Similarity Loss
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.
Compressed Learning: A Deep Neural Network Approach
Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal.
A Deep Learning Approach to Block-based Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.
