github.com/xorvoid/sectorc
xorvoid/sectorc
A C Compiler that fits in the 512 byte boot sector of an x86 machine (⭐ 1832)
arxiv.org/abs/1805.11375v1
Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specified in the model. Determining the right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learnin...
arxiv.org/abs/1612.01058v1
Algorithmic Songwriting with ALYSIA
This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was used to create original...
arxiv.org/abs/2311.10822v2
Gradients and frequency profiles of quantum re-uploading models
Quantum re-uploading models have been extensively investigated as a form of machine learning within the context of variational quantum algorithms. Their trainability and expressivity are not yet fully understood and are critical to their performance....
arxiv.org/abs/2406.16989v2
Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs). Its modular and plug-and-play nature allows the integration of various domain-specific LoRAs, enhancing LLM capabilities. Open-source platforms like Hugging...
arxiv.org/abs/2404.09750v3
Layered Uploading for Quantum Convolutional Neural Networks
Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along th...
github.com/online-ml/river
online-ml/river
? Online machine learning in Python (⭐ 5735)
arxiv.org/abs/2203.13886v1
Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Le…
Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncer...
arxiv.org/abs/2109.14369v1
Preparation of quantum superposition using partial negation
The preparation of a quantum superposition is the key to the success of many quantum algorithms and quantum machine learning techniques. The preparation of an incomplete or a non-uniform quantum superposition with certain properties is a non-trivial...
en.wikipedia.org/wiki/Acer_campestre
Acer campestre - Wikipedia
Project: Acer campestre Archived 2007-09-28 at the Wayback Machine "Acer campestre". Flora Europaea. Retrieved August 29, 2007. Flora of NW Europe: Acer campestre[permanent
arxiv.org/abs/2409.08183v2
Converting sWeights to Probabilities with Density Ratios
The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real ex...
arxiv.org/abs/2212.06566v4
How to select an objective function using information theory
In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? Information theory gives one answer: To maximize the information in the model, select the objective func...
arxiv.org/abs/1908.04998v1
Interpretable Encrypted Searchable Neural Networks
In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper pr...
arxiv.org/abs/2505.17684v1
5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5…
Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods degrades significantly when environmental conditions change, thereby hi...
arxiv.org/abs/2107.13751v1
The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
Despite advances in neural machine translation, cross-lingual retrieval tasks in which queries and documents live in different natural language spaces remain challenging. Although neural translation models may provide an intuitive approach to tackle...
arxiv.org/abs/2411.08992v2
IDCIA: Immunocytochemistry Dataset for Cellular Image Analysis
We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Cell counting is an important step in cell analysis. Typically, domain experts manually count cells in...
github.com/microsoft/ML-For-Beginners
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all (⭐ 84175)
arxiv.org/abs/2112.00760v2
Biology and medicine in the landscape of quantum advantages
Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulate...
arxiv.org/abs/2007.03647v2
Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brus…
Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models to create visual mediums on c...
arxiv.org/abs/2012.03744v1
Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neu…
Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate c...