AIMC Topic:
Learning

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Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment.

Neural computation Jan 14, 2022
Model-based control has great potential for use in real robots due to its high sampling efficiency. Nevertheless, dealing with physical contacts and generating accurate motions are inevitable for practical robot control tasks, such as precise manipul...
Neural Networks, Computer Learning Robotics Motion
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FP-nets as novel deep networks inspired by vision.

Journal of vision Jan 4, 2022
Feature-product networks (FP-nets) are inspired by end-stopped cortical cells with FP-units that multiply the outputs of two filters. We enhance state-of-the-art deep networks, such as the ResNet and MobileNet, with FP-units and show that the resulti...
Learning Deep Learning Neurons Artifacts Vision, Ocular
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Differentiable Visual Computing: Challenges and Opportunities.

IEEE computer graphics and applications Jan 1, 2022
Classical algorithms typically contain domain-specific insights. This makes them often more robust, interpretable, and efficient. On the other hand, deep-learning models must learn domain-specific insight from scratch from a large amount of data usin...
Neural Networks, Computer Algorithms Learning
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Learning by machines.

The journal of trauma and acute care surgery Jan 1, 2022
Artificial Intelligence Learning
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Graph representation learning for structural proteomics.

Emerging topics in life sciences Dec 21, 2021
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storin...
Machine Learning Learning Proteomics Proteins Databases, Protein
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Confidence-Controlled Hebbian Learning Efficiently Extracts Category Membership From Stimuli Encoded in View of a Categorization Task.

Neural computation Dec 15, 2021
In experiments on perceptual decision making, individuals learn a categorization task through trial-and-error protocols. We explore the capacity of a decision-making attractor network to learn a categorization task through reward-based, Hebbian-type ...
Humans Neural Networks, Computer Algorithms Learning Neurons Reward
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A Variational Encoder Framework for Decoding Behavior Choices from Neural Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
In this paper, using an adversarial variational encoder model, we propose a two-step data-driven approach to extract cross-subject feature representations from neural activity in order to decode subjects' behavior choices. First, various characterist...
Neural Networks, Computer Learning Animals Mice
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Learning Cellular Phenotypes through Supervision.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Image-based cell phenotyping is an important and open problem in computational pathology. The two principal challenges are: 1) making the cell cluster properties insensitive to experimental settings (like seed point and feature selection) and 2) ensu...
Phenotype Learning Supervised Machine Learning
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Facial Landmark Tracking in Videos of Individuals with Neurological Impairments: Is There a Trade-off Between Smoothness and Accuracyƒ.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Orofacial kinematics are valuable markers of function and progression in a variety of neurological disorders. Recent advances in facial landmark detection have been used to improve landmark tracking in video, for example by accounting for interframe ...
Humans Neural Networks, Computer Learning Face
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Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.

Neural computation Sep 16, 2021
Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics ("neural sequences") of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate ...
Humans Neural Networks, Computer Learning Neoplasms
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