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Found 14 papers, 3 papers with code
Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT
Our technique presents valuable insights into extending the state of the art of Aspect-Based Sentiment Analysis on newer datasets, where such analysis is not hampered by the length of the text data.
CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models
Online news and information sources are convenient and accessible ways to learn about current issues.
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks
Our work quantitatively demonstrates that combining compression methods can synergistically reduce model size, and that practitioners should prioritize (1) quantization, (2) knowledge distillation, and (3) pruning to maximize accuracy vs. model size tradeoffs.
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning
Differentiating if a text message belongs to hate speech and offensive language is a key challenge in automatic detection of toxic text content.
Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services
We propose a combination of unsupervised and supervised model with minimum human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pretrained language model for artefact prediction on a large amount of conversation data.
Ensembling Low Precision Models for Binary Biomedical Image Segmentation
Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent.
Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers' Conversations in Hybrid Cloud Operations
Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents.
Volumetric landmark detection with a multi-scale translation equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Volumetric landmark detection with a multi-scale shift equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings
In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time.
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm.
Parameterized Principal Component Analysis
We introduce a novel manifold approximation method, parameterized principal component analysis (PPCA) that models data with linear subspaces that change continuously according to the extra parameter of contextual information (e. g. age), instead of ad-hoc atlases.
Icon Based Information Retrieval and Disease Identification in Agriculture
Recent developments in the ICT industry in past few decades has enabled the quick and easy access to the information available on the internet.
