28 papers · ranked by Valyu relevance
Sebastian Ruder
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for…
Yu Zhang, Dit‐Yan Yeung
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a…
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…
Christian Widmer, Nora C Toussaint, Yasemin Altun, Gunnar Rätsch
Background The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In…
Nicholas Menghi, Kemal Kacar, Will Penny, Samuel J. Gershman
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this…
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…
Fang Su, Hai-Yang Shang, Jing-Yan Wang
In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep…
Nicholas Menghi, Kemal Kacar, Will Penny
This paper uses constructs from the field of multitask machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the…
Yu Zhang, Ying Wei, Qiang Yang
Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called learning to multitask (L2MT). To achieve the goal, L2MT exploits historical…
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…
Yongxin Yang, Timothy M. Hospedales
In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different…
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…
Shao-Yen Tseng, Brian Baucom, Panayiotis Georgiou, Diego Amancio
Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain…
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…
Sachin Ravi, Sebastian Musslick, Maia Hamin, Theodore L. Willke + 1 more
'Jonathan D. Cohen'] The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. In contrast, multitasking is used to indicate, especially in the cognitive…
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…
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']…
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.…
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.…
Ren Yi, Kyunghyun Cho, Richard Bonneau
Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs…
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…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
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…
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)…