Results 11 to 20 of about 102,050 (305)
Towards an Atlas of Computational Learning Theory. [PDF]
, 2016 A major part of our knowledge about Computational Learning stems from comparisons of the learning power of different learning criteria. These comparisons inform about trade-offs between learning restrictions and, more generally, learning settings; furthermore, they inform about what restrictions can be observed without losing learning power.
Timo Kötzing, Martin Schirneck
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IET Communications, 2022 Millimetre wave (mmWave) communications, that is, 30 to 300 GHz, have intermittent short‐range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts
Ehab Mahmoud Mohamed +5 more
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Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit
Sensors, 2020 Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway
Ehab Mahmoud Mohamed +4 more
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Theory-driven computational models of drug addiction in humans: Fruitful or futile?
Addiction Neuroscience, 2023 Maladaptive behavior in drug addiction is widely regarded as a result of neurocognitive dysfunctions. Recently, there has been a growing trend to adopt computational methods to study these dysfunctions in drug-addicted patients, not least because it ...
Tsen Vei Lim, Karen D Ersche
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Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders
Frontiers in Psychiatry, 2022 In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit ...
Brittany Liebenow +20 more
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Reliability of Decision-Making and Reinforcement Learning Computational Parameters
Computational Psychiatry, 2023 Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment.
Anahit Mkrtchian +2 more
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Budgeted Bandits for Power Allocation and Trajectory Planning in UAV-NOMA Aided Networks
Drones, 2023 On one hand combining Unmanned Aerial Vehicles (UAVs) and Non-Orthogonal Multiple Access (NOMA) is a remarkable direction to sustain the exponentially growing traffic requirements of the forthcoming Sixth Generation (6G) networks.
Ramez Hosny +4 more
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Consistent Sparse Deep Learning: Theory and Computation [PDF]
Journal of the American Statistical Association, 2021 Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, causing many difficulties in training, prediction and interpretation.
Yan Sun 0011, Qifan Song, Faming Liang
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Proceedings of the 2023 CLASP Conference on Learning with Small Data [PDF]
, 2023 The purpose of our conference is to bring together researchers from several areas of NLP, addressing datasets, methods and limits of effective (machine) learning with small data containing natural language and associated multi-modal information.
core
A Computational Theory of Learning Causal Relationships [PDF]
Cognitive Science, 1991 I present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples are consistent with a general theory of causality.
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