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We propose Wake-Sleep Consolidated Learning (WSCL), a learning strategy leveraging Complementary Learning System theory and the wake-sleep phases of the human brain to improve the performance of deep neural networks for visual…

Neural and Evolutionary Computing · Computer Science 2024-01-18 Amelia Sorrenti , Giovanni Bellitto , Federica Proietto Salanitri , Matteo Pennisi , Simone Palazzo , Concetto Spampinato

On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices. However, today's large ML models must be…

Human-Computer Interaction · Computer Science 2024-04-05 Fred Hohman , Mary Beth Kery , Donghao Ren , Dominik Moritz

We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, the suggested architecture…

Computation and Language · Computer Science 2019-11-04 Jihun Choi , Taeuk Kim , Sang-goo Lee

Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative…

Machine Learning · Computer Science 2020-04-29 Mina Karzand , Robert D. Nowak

The rapid growth of wearable sensor technologies holds substantial promise for the field of personalized and context-aware Human Activity Recognition. Given the inherently decentralized nature of data sources within this domain, the…

Signal Processing · Electrical Eng. & Systems 2023-11-09 Ahmad Esmaeili , Zahra Ghorrati , Eric T. Matson

The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the…

Machine Learning · Computer Science 2023-04-25 Zichang Liu , Zhiqiang Tang , Xingjian Shi , Aston Zhang , Mu Li , Anshumali Shrivastava , Andrew Gordon Wilson

The burgeoning volume of digital content across diverse modalities necessitates efficient storage and retrieval methods. Conventional approaches struggle to cope with the escalating complexity and scale of multimedia data. In this paper, we…

Artificial Intelligence · Computer Science 2024-04-17 Jixiang Luo

Literature analysis facilitates researchers to acquire a good understanding of the development of science and technology. The traditional literature analysis focuses largely on the literature metadata such as topics, authors, abstracts,…

Artificial Intelligence · Computer Science 2021-01-29 Linlin Hou , Ji Zhang , Ou Wu , Ting Yu , Zhen Wang , Zhao Li , Jianliang Gao , Yingchun Ye , Rujing Yao

Modern scientific simulations, observations, and large-scale experiments generate data at volumes that often exceed the limits of storage, processing, and analysis. This challenge drives the development of data reduction methods that…

Machine Learning · Computer Science 2025-11-18 Minh Vu , Andrey Lokhov

Biological multimodal large language models (MLLMs) have emerged as powerful foundation models for scientific discovery. However, existing models are specialized to a single modality, limiting their ability to solve inherently cross-modal…

Machine Learning · Computer Science 2026-03-17 Wonbin Lee , Dongki Kim , Sung Ju Hwang

The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in…

Computation and Language · Computer Science 2025-10-09 Yike Zhao , Simin Guo , Ziqing Yang , Shifan Han , Dahua Lin , Fei Tan

As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation…

Machine Learning · Computer Science 2022-12-09 Zhendong Liu , Wenyu Jiang , Min guo , Chongjun Wang

Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information. This flexibility is inherited from the Neural Process framework and…

Machine Learning · Computer Science 2021-04-30 Ben Day , Alexander Norcliffe , Jacob Moss , Pietro Liò

Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The widespread adoption of test-time scaling further intensifies…

Artificial Intelligence · Computer Science 2026-03-11 Cheng Yuan , Jiawei Shao , Xuelong Li

Weak supervision combines the advantages of training on real data with the ability to exploit signal properties. However, training a neural network using weak supervision often requires an excessive amount of signal data, which severely…

High Energy Physics - Phenomenology · Physics 2024-12-23 Zong-En Chen , Cheng-Wei Chiang , Feng-Yang Hsieh

Knowledge transfer from a complex high performing model to a simpler and potentially low performing one in order to enhance its performance has been of great interest over the last few years as it finds applications in important problems…

Machine Learning · Computer Science 2022-09-09 Amit Dhurandhar , Tejaswini Pedapati

The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance in modern machine learning. While machine learning has long…

High Energy Physics - Experiment · Physics 2026-02-18 Matthias Vigl , Nicole Hartman , Michael Kagan , Lukas Heinrich

A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sampling from thermodynamic ensembles and hoping for an…

Materials Science · Physics 2025-09-30 Tigany Zarrouk , Miguel A. Caro

With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods to the exchange-correlation functional of density functional theory. In this chapter, we review how the ML tools are used…

Materials Science · Physics 2022-07-01 Ryo Nagai , Ryosuke Akashi

Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important…

Machine Learning · Computer Science 2018-12-27 Dianbo Liu , Nestor Sepulveda , Ming Zheng
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