56 research outputs found
A NOVEL SPECIALIZED SEARCH ENGINE FOR AI-MODELS AND THEIR COMPARISON
In recent years, the world of AI has tremendously increased in size and depth. Both new and old researchers are facing the problem of fast emerging AI researches, models and services. One needs to continuously read complete papers to understand the idea behind any novel research. This work presents a novel AI service that removes the burdens of long text reading and uncategorized search. It consists of a website that categorizes all the AI researches in a well-designed database. The users just have to select the models they are interested in, and the website will return a table containing the technical data in addition to a graph that shows visual relationships between the AI models, features and datasets. Future work will emphasize on developing the tool by applying NLP in two directions: one on the search box to retrieve the main keyword to search for, and the other on research papers to automatically extract the data into the website categorized database
Application of Naive Bayes Model, SVM and Deep Learning Predicting
The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (name, name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings.
Keywords—Payments, Algorithm, Performanc
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network
platforms nowadays, aiming to maximize user satisfaction along with other key
business objectives. Twitter is no exception. Despite the fact that Twitter
data has been extensively used to understand socioeconomic and political
phenomena and user behaviour, the implicit feedback provided by users on Tweets
through their engagements on the Home Timeline has only been explored to a
limited extent. At the same time, there is a lack of large-scale public social
network datasets that would enable the scientific community to both benchmark
and build more powerful and comprehensive models that tailor content to user
interests. By releasing an original dataset of 160 million Tweets along with
engagement information, Twitter aims to address exactly that. During this
release, special attention is drawn on maintaining compliance with existing
privacy laws. Apart from user privacy, this paper touches on the key challenges
faced by researchers and professionals striving to predict user engagements. It
further describes the key aspects of the RecSys 2020 Challenge that was
organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table
Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX
With the advent of big data era and the development of artificial
intelligence and other technologies, data security and privacy protection have
become more important. Recommendation systems have many applications in our
society, but the model construction of recommendation systems is often
inseparable from users' data. Especially for deep learning-based recommendation
systems, due to the complexity of the model and the characteristics of deep
learning itself, its training process not only requires long training time and
abundant computational resources but also needs to use a large amount of user
data, which poses a considerable challenge in terms of data security and
privacy protection. How to train a distributed recommendation system while
ensuring data security has become an urgent problem to be solved. In this
paper, we implement two schemes, Horizontal Federated Learning and Secure
Distributed Training, based on Intel SGX(Software Guard Extensions), an
implementation of a trusted execution environment, and TensorFlow framework, to
achieve secure, distributed recommendation system-based learning schemes in
different scenarios. We experiment on the classical Deep Learning
Recommendation Model (DLRM), which is a neural network-based machine learning
model designed for personalization and recommendation, and the results show
that our implementation introduces approximately no loss in model performance.
The training speed is within acceptable limits.Comment: 5 pages, 8 figure
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and
storage costs of modern deep learning applications. This paper evaluates
Microscaling (MX) data formats that combine a per-block scaling factor with
narrow floating-point and integer types for individual elements. MX formats
balance the competing needs of hardware efficiency, model accuracy, and user
friction. Empirical results on over two dozen benchmarks demonstrate
practicality of MX data formats as a drop-in replacement for baseline FP32 for
AI inference and training with low user friction. We also show the first
instance of training generative language models at sub-8-bit weights,
activations, and gradients with minimal accuracy loss and no modifications to
the training recipe
A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings
Item representation holds significant importance in recommendation systems,
which encompasses domains such as news, retail, and videos. Retrieval and
ranking models utilise item representation to capture the user-item
relationship based on user behaviours. While existing representation learning
methods primarily focus on optimising item-based mechanisms, such as attention
and sequential modelling. However, these methods lack a modelling mechanism to
directly reflect user interests within the learned item representations.
Consequently, these methods may be less effective in capturing user interests
indirectly. To address this challenge, we propose a novel Interest-aware
Capsule network (IaCN) recommendation model, a model-agnostic framework that
directly learns interest-oriented item representations. IaCN serves as an
auxiliary task, enabling the joint learning of both item-based and
interest-based representations. This framework adopts existing recommendation
models without requiring substantial redesign. We evaluate the proposed
approach on benchmark datasets, exploring various scenarios involving different
deep neural networks, behaviour sequence lengths, and joint learning ratios of
interest-oriented item representations. Experimental results demonstrate
significant performance enhancements across diverse recommendation models,
validating the effectiveness of our approach.Comment: Accepted Paper under LBR track in the Seventeenth ACM Conference on
Recommender Systems (RecSys) 202
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