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…
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…
Juan Jovel, Russell Greiner
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of…
Mohammad Abbasi, Connor Sanderford, Narendian Raghu, Benjamin B Bartelle
We have developed representation learning methods, specifically to address the constraints and advantages of complex spatial data. Sparse filtering (SFt), uses principles of sparsity and mutual information to build representations from both global and local features from a minimal list of samples. Critically, 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…
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…
Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner, Marc Höftmann + 4 more
'Eric Upschulte' 'Sebastian Konietzny' 'Maike Behrendt' 'Stefan Harmeling'] Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image representations without supervision. These representations can…
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.…
Pegah Mavaie, Lawrence Holder, Michael K. Skinner
Background The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in…
Honglin Liu, Chao Sun, Peng Hu, Yunfan Li + 1 more
Honglin Liu 1 , Chao Sun 2 , Peng Hu 1 , Yunfan Li1 ∗ , Xi Peng1,3 ∗ 1 College of Computer Science, Sichuan University, Chengdu, China 2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China 3 National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical…
N. Menghi, W. J. Johnston, S. Vigano’, M. A. B. Hinrichs + 3 more
The complexity of our environment poses significant challenges for adaptive behavior. Recognizing shared structures across tasks can theoretically improve learning through generalization. However, how such shared representations emerge and influence performance remains poorly understood. Contrary to expectations, our…
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…
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…
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…
Irina Higgins, Sébastien Racanière, Danilo Rezende
Biological intelligence is remarkable in its ability to produce complex behavior in many diverse situations through data efficient, generalizable, and transferable skill acquisition. It is believed that learning “good” sensory representations is important for enabling this, however there is little agreement as to what…
Qianyi Li, Ben Sorscher, Haim Sompolinsky
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribution and out-of-distribution contexts. We introduce two hypotheses…
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.…
Eamon Duede
This paper aims to clarify the representational status of Deep Learning Models (DLMs). While commonly referred to as 'representations', what this entails is ambiguous due to a conflation of functional and relational conceptions of representation. This paper argues that while DLMs represent their targets in a relational…
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…
Janis Keck, Caswell Barry, Christian F. Doeller, Jürgen Jost
In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. In particular, hippocampal place cells are hypothesized to encode the SR. Here, we investigate how varying the temporal symmetry in learning…
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…