25 papers · ranked by Valyu relevance
Kourosh T. Baghaei, Amirreza Payandeh, Pooya Fayyazsanavi, Shahram Rahimi + 2 more
'Shahram Rahimi' 'Zhiqian Chen' 'Somayeh Bakhtiari Ramezani'] Abstract—Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms' performance is greatly influenced by the representations that are derived from the data in the learning process. The…
Constantin Ahlmann-Eltze, Florian Barkmann, Jan Lause, Valentina Boeva + 1 more
Single-cell RNA sequencing (scRNA-seq) has become a cornerstone experimental technique in tissue biology, with gene expression data for over 100 million cells available in public repositories. The high dimensionality, sparsity, and technical noise inherent to scRNA-seq data have motivated the development of a broad…
Yonatan Harnik, Anat Milo
Molecular representation learning (MRL) is a specialized field in which deep-learning models condense essential molecular information into a vectorized form. Whereas recent research has predominantly emphasized drug discovery and bioactivity applications, MRL holds significant potential for diverse chemical properties…
Jonathan Baxter
Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt. In this paper a mechanism for…
Hlynur Davíð Hlynsson
| Name of the author: | Hlynur Davíð Hlynsson | | --- | --- | | Place of birth: | Reykjavík | | First supervisor: | Prof. Dr. Laurenz Wiskott | | | Ruhr-Universität Bochum, Germany | | Second supervisor: | Prof. Dr. Tobias Glasmachers | | | Ruhr-Universität Bochum, Germany | | Year of thesis submission: | 2021 | | Date…
Han Wang, Erfan Miahi, Martha White, Marlos C. Machado + 4 more
'Zaheer Abbas' 'Raksha Kumaraswamy' 'Vincent Liu' 'Adam White'] In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought…
Kento Nozawa, Issei Sato
Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine…
Lorenzo Ferrone, Fabio Massimo Zanzotto
Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete symbols are fading away, erased by vectors or tensors called distributed and distributional…
Maxime Come, Arnaud Lespart, Aylin Gulmez, Loussineh Keshishian + 7 more
Flexible decision-making requires not only updating values, but redefining which features constitute an action in a given context. We recorded nucleus accumbens (NAc) dopamine release while mice navigated a three-target intracranial self-stimulation foraging task in which outcomes were evaluated under three distinct…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve + 2 more
'Mattia Rigotti' 'Eric Shea-Brown'] Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity - i.e., in the learned neural representations.…
Lijuan Yuan, Hongming Li, Shiman Fu, Zizai Zhang
With the development of various network technologies and the spread of coronavirus disease 2019, many online learning platforms have been built. However, some of them may negatively impact student learning outcomes. Therefore, this study aims to improve the online learning effect of students by comprehensively…
Niall O’ Mahony, Sean Campbell, Lenka Krpalkova, Anderson Carvalho + 10 more
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many…
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
Hiroaki Sasaki, Takashi Takenouchi
Unsupervised representation learning is one of the most important problems in machine learning. A recent promising approach is contrastive learning: A feature representation of data is learned by solving a pseudo classification problem where class labels are automatically generated from unlabelled data. However, it is…
Julian Zilly, Lorenz Hetzel, Andrea Censi, Emilio Frazzoli
We examine the influence of input data representations on learning complexity. For learning, we posit that each model implicitly uses a candidate model distribution for unexplained variations in the data, its noise model. If the model distribution is not well aligned to the true distribution, then even relevant…
Jim Jing-Yan Wang, Halima Bensmail
In the database retrieval and nearest neighbor classification tasks, the two basic problems are to represent the query and database objects, and to learn the ranking scores of the database objects to the query. Many studies have been conducted for the representation learning and the ranking score learning problems…
Maria Boulougouri, Pierre Vandergheynst, Daniel Probst
Computational representation of molecules can take many forms, including graphs, stringencodings of graphs, binary vectors, or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine-learning models.…
Alon B Baram, Timothy H Muller, Hamed Nili, Mona Garvert + 1 more
Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalise it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new…
Rishabh Raj, Dar Dahlen, Kyle Duyck, C. Ron Yu
The brain has a remarkable ability to recognize objects from noisy or corrupted sensory inputs. How this cognitive robustness is achieved computationally remains unknown. We present a coding paradigm, which encodes structural dependence among features of the input and transforms various forms of the same input into the…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Karel Lenc, Andrea Vedaldi
Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aimed at filling this gap, we investigate two key mathematical properties of representations: equivariance and equivalence.…
Anthony Onwuli, Keith T. Butler, Aron Walsh
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and…
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…
Derek van Tilborg, Francesca Grisoni
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening…