27 papers · ranked by Valyu relevance
Yingshuai Wang, Dezheng Zhang, Aziguli Wulamu
Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is…
Yeshwant Singh, Anupam Biswas, Angshuman Bora, Debashish Malakar + 2 more
'Subham Chakraborty' 'Suman Bera'] Abstract- In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under…
Simon Vandenhende
A PhD is more than just another university degree. A PhD is about the pursuit of knowledge, the aspiration to figure things out and the love to explore new ideas. It is a personal journey filled with challenges, discoveries and encounters with extraordinary people. As I reflect on this incredible adventure, a feeling…
Muhammad Adeel Nisar, Kimiaki Shirahama, Muhammad Tausif Irshad, Xinyu Huang + 2 more
'Xinyu Huang' 'Marcin Grzegorzek' 'Carlos M. Travieso-González'] Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning…
Jared Strauch, Amir Asiaee
The development of models to predict sensitivity to anticancer drugs is an area of significant interest, given the diverse responses to treatment among patients and the considerable expense and time involved in anticancer drug development. Leveraging “omic” data and anticancer response information from the Cancer Cell…
Shiyuan He, Hanxuan Ye, Kejun He
This work studies the multi-task functional linear regression models where both the covariates and the unknown regression coefficients (called slope functions) are curves. For slope function estimation, we employ penalized splines to balance bias, variance, and computational complexity. The power of multi-task learning…
Pierre Laforgue, Andrea Della Vecchia, Nicolò Cesa‐Bianchi, Lorenzo Rosasco
We introduce and analyze AdaTask, a multitask online learning algorithm that adapts to the unknown structure of the tasks. When the N tasks are stochastically activated, we show that the regret of AdaTask is better, by a factor that can be as large as √ N, than the regret achieved by running N independent algorithms…
Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang + 13 more
Spanning Traditional, Deep, and Pretrained Foundation Model Eras Authors: ['Jun Yu' 'Yutong Dai' 'Xiaokang Liu' 'Jin Huang' 'Yishan Shen' 'Ke Zhang' 'Rong Zhou' 'Eashan Adhikarla' 'Wenxuan Ye' 'Yixin Liu' 'Zhaoming Kong' 'Kai Zhang' 'Yilong Yin' 'Vinod Namboodiri' 'Brian D. Davison' 'Jason H. Moore' 'Yong Chen']…
Ivan Olier, Oghenejokpeme I. Orhobor, Tirtharaj Dash, Andy M. Davis + 3 more
'Larisa N. Soldatova' 'Joaquin Vanschoren' 'Ross D. King'] Title: Significance Machine learning (ML) is the branch of artificial intelligence (AI) that develops computational systems that learn from experience. In supervised ML, the ML system generalizes from labelled examples to learn a model that can predict the…
Willem A.M. Wybo, Matthias C. Tsai, Viet Anh Khoa Tran, Bernd Illing + 3 more
While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we first demonstrate that thin dendritic branches are…
Run-Hsin Lin, Pinpin Lin, Chia-Chi Wang, Chun-Wei Tung
Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets…
Hanne Say, Suzan Ece Ada, Emre Ugur, Erhan Oztop
—Humans can continuously acquire new skills and knowledge by exploiting existing ones for improved learning, without forgetting them. Similarly, 'continual learning' in machine learning aims to learn new information while preserving the previously acquired knowledge. Existing research often overlooks the nature of…
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
Asiful Arefeen, Hassan Ghasemzadeh, Bashir Morshed
Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. Leveraging inductive knowledge transfer, these models mitigate the risk of overfitting on any specific task, leading to enhanced overall performance.…
Jiangtao Chen, Zijia Wang, Zheng Kou, Heming Jia + 2 more
'Xuewen Xia'] Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditional differential evolution (DE) and the genetic…
Maryam Astero, Anchen Li, Elena Casiraghi, Juho Rousu
Modeling chemical reactions requires connecting fine-grained atom–bond edits with broader semantic categories. Yet, most machine learning approaches model these aspects in isolation: atom mapping, reaction center identification, and reaction classification are treated as separate problems. This separation limits…
Kim Bjerge, Quentin Geissmann, Jamie Alison, Hjalte M. R. Mann + 3 more
Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology and monitoring of biodiversity. However, a major challenge is the diversity of insects and close resemblances of many species combined with computer vision are…
François Charih, Mullen Boulter, Kyle K. Biggar, James R. Green
Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role of this modification in epigenetics and gene regulation is relatively well understood, but our understanding of the extent and the role of lysine methylation of…
Rui Lu, Yang Yue, Andrew Zhao, Simon Du + 1 more
Multitask Representation Learning (MRL) has emerged as a prevalent technique to improve sample efficiency in Reinforcement Learning (RL). Empirical studies have found that training agents on multiple tasks simultaneously within online and transfer learning environments can greatly improve efficiency. Despite its…
Laura Driscoll, Krishna Shenoy, David Sussillo
Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned.…
Nicolas Y. Masse, Matthew C. Rosen, Doris Y. Tsao, David J. Freedman
The brains of all animals are plastic, allowing us to form new memories, adapt to new environments, and to learn new tasks. What is less clear is how much plasticity is required to perform these cognitive functions: does learning require widespread plasticity across the brain, or can learning occur with more rigid…
Stewart He, Sookyung Kim, Kevin S. McLoughlin, Hiranmayi Ranganathan + 2 more
Predicting molecular activity against protein targets is difficult because of the paucity of experimental data. Approaches like multitask modeling and collaborative filtering seek to improve model accuracy by leveraging results from multiple targets, but are limited because different compounds are measured with…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
The accurate prediction of fuel mixture properties is essential for the development of alternative fuels, yet remains challenging under data-scarce conditions due to the combinatorial complexity of multi-component systems. In this study, we present a systematic evaluation of three machine learning (ML)…