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We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be…

Machine Learning · Statistics 2014-08-04 Truyen Tran , Dinh Phung , Svetha Venkatesh

We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory…

In recent years, Large Language Models (LLMs) have emerged as a prominent area of interest across various research domains, including Process Mining (PM). Current applications in PM have predominantly centered on prompt engineering…

Computation and Language · Computer Science 2025-09-04 Rafael Seidi Oyamada , Jari Peeperkorn , Jochen De Weerdt , Johannes De Smedt

Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code and data cleaning) remains suboptimal. Improving performance typically…

Computation and Language · Computer Science 2026-03-25 Junjie Xing , Yeye He , Mengyu Zhou , Haoyu Dong , Shi Han , Dongmei Zhang , Surajit Chaudhuri

In terms of signal samples, we propose and justify a new rank reduced multi-term transform, abbreviated as MTT, which, under certain conditions, may provide better-associated accuracy than that of known optimal rank reduced transforms. The…

Optimization and Control · Mathematics 2021-11-11 Pablo Soto-Quiros , Anatoli Torokhti

Set constraints provide a highly general way to formulate program analyses. However, solving arbitrary boolean combinations of set constraints is NEXPTIME-hard. Moreover, while theoretical algorithms to solve arbitrary set constraints…

Programming Languages · Computer Science 2020-03-03 Joseph Eremondi

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to…

Computation and Language · Computer Science 2023-12-14 Jinta Weng , Jiarui Zhang , Yue Hu , Daidong Fa , Xiaofeng Xuand , Heyan Huang

Recently, deep learning has driven significant advancements in multivariate time series forecasting (MTSF) tasks. However, much of the current research in MTSF tends to evaluate models from a holistic perspective, which obscures the…

Machine Learning · Computer Science 2025-09-23 Shuang Liang , Chaochuan Hou , Xu Yao , Shiping Wang , Minqi Jiang , Songqiao Han , Hailiang Huang

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the…

Machine Learning · Computer Science 2022-04-08 Zongmin Liu , Yitian Xu

This paper introduces Multidimensional Task Learning (MTL), a unified mathematical framework based on Generalized Einstein MLPs (GE-MLPs) that operate directly on tensors via the Einstein product. We argue that current computer vision task…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Alaa El Ichi , Khalide Jbilou

At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based…

Instrumentation and Detectors · Physics 2025-01-15 Samuel Bein , Patrick Connor , Kevin Pedro , Peter Schleper , Moritz Wolf

The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory footprint of LLMs on devices. However, low-bit LLMs…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-03-26 Jianyu Wei , Shijie Cao , Ting Cao , Lingxiao Ma , Lei Wang , Yanyong Zhang , Mao Yang

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the…

Machine Learning · Computer Science 2019-03-14 Babak Hosseini , Barbara Hammer

The increasing global prevalence of mental disorders, such as depression and PTSD, requires objective and scalable diagnostic tools. Traditional clinical assessments often face limitations in accessibility, objectivity, and consistency.…

Audio and Speech Processing · Electrical Eng. & Systems 2025-04-03 Abdelrahaman A. Hassan , Abdelrahman A. Ali , Aya E. Fouda , Radwa J. Hanafy , Mohammed E. Fouda

Selecting hyperparameters for unsupervised learning problems is challenging in general due to the lack of ground truth for validation. Despite the prevalence of this issue in statistics and machine learning, especially in clustering…

Machine Learning · Statistics 2020-02-04 Xinjie Fan , Yuguang Yue , Purnamrita Sarkar , Y. X. Rachel Wang

Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM),…

Large-scale transformer models have shown remarkable performance in language modelling tasks. However, such models feature billions of parameters, leading to difficulties in their deployment and prohibitive training costs from scratch. To…

Artificial Intelligence · Computer Science 2023-06-06 Viktoriia Chekalina , Georgii Novikov , Julia Gusak , Ivan Oseledets , Alexander Panchenko

Large language models (LLMs) are dominated by dense linear transformations, whose storage, memory and computational overheads hinder efficient adaptation and deployment while masking the functional impacts of structural simplification. Here…

Computation and Language · Computer Science 2026-05-26 Ying Lu , Peng-Fei Zhou , Qi-Xuan Fang , Pan Zhang , Shi-Ju Ran , Gang Su

We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to…

Machine Learning · Computer Science 2021-12-07 Carl Poelking , Felix A. Faber , Bingqing Cheng

Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the…

Numerical Analysis · Computer Science 2016-06-20 Qibin Zhao , Guoxu Zhou , Shengli Xie , Liqing Zhang , Andrzej Cichocki