github.com/probml/pyprobml
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy (⭐ 7031)
arxiv.org/abs/2204.08096v3
Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors…
This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehic...
arxiv.org/abs/2305.15723v1
Learning across Data Owners with Joint Differential Privacy
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018]. In this setting, the model trained for each data owner $j$ uses $j$...
arxiv.org/abs/2310.01428v1
Unraveling Post-COVID-19 Immune Dysregulation Using Machine Learning-based Immun…
The COVID-19 pandemic has left a significant mark on global healthcare, with many individuals experiencing lingering symptoms long after recovering from the acute phase of the disease, a condition often referred to as "long COVID." This study delves...
arxiv.org/abs/1511.06931v6
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang...
arxiv.org/abs/1604.06045v7
Dialog-based Language Learning
A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (taggin...
arxiv.org/abs/2003.12660v1
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nig…
Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural mach...
github.com/femioladeji/License-Plate-Recognition-Nigerian-vehicles
femioladeji/License-Plate-Recognition-Nigerian-vehicles
A python program that uses the concept of OCR using machine learning to identify the characters on a Nigerian license plate (⭐ 277)
arxiv.org/abs/1805.02400v4
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this wo...
arxiv.org/abs/2209.06346v2
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Appr…
Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket...
github.com/hanmq/MachineLearning_Zhouzhihua_ProblemSets
hanmq/MachineLearning_Zhouzhihua_ProblemSets
Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题，个人解答。各算法都拿numpy和pandas实现了一遍 (⭐ 1669)
en.wikipedia.org/wiki/K%C3%B6sefak%C4%B1l%C4%B1%2C_Sar%C4%B1%C3%A7am
Kösefakılı, Sarıçam - Wikipedia
Its population is 50 (2022). Before 2008, it was part of the district of Yüreğir. Mahalle Archived 2015-07-06 at the Wayback Machine, Turkey Civil Administration
arxiv.org/abs/2512.12590v1
Automatic Wire-Harness Color Sequence Detector
Wire harness inspection process remains a labor-intensive process prone to errors in the modern Electronics Manufacturing Services (EMS) industry. This paper introduces a semiautomated machine vision system capable of verifying correct wire positioni...
github.com/actionml/harness
actionml/harness
Harness is a Machine Learning/AI Server with plugins for many algorithms including the Universal Recommender (⭐ 286)
arxiv.org/abs/1602.08124v3
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Net…
The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different mac...
arxiv.org/abs/2001.08277v1
Intermittent Pulling with Local Compensation for Communication-Efficient Federat…
Federated Learning is a powerful machine learning paradigm to cooperatively train a global model with highly distributed data. A major bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) algorithm for large-scale Federated...
www.reddit.com/r/korea/comments/1jcwtf4/february_1945_news_report_painted_korean/
February 1945 news report painted Korean passengers as ‘loud’ and ‘unruly’ while…
By early 1945, the Pacific War had dragged on for over three years, and Imperial Japan’s war machine was straining under the immense pressure. Nowhere was this more evident than in Korea’s crumbli...
arxiv.org/abs/1812.04606v3
Deep Anomaly Detection with Outlier Exposure
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time...
arxiv.org/abs/2007.01667v1
Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfe…
Reading comprehension is a well studied task, with huge training datasets in English. This work focuses on building reading comprehension systems for Czech, without requiring any manually annotated Czech training data. First of all, we automatically...
arxiv.org/abs/cs/0009003v1
Automatic Extraction of Subcategorization Frames for Czech
We present some novel machine learning techniques for the identification of subcategorization information for verbs in Czech. We compare three different statistical techniques applied to this problem. We show how the learning algorithm can be used...