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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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發(fā)表于 2025-3-28 18:37:04 | 只看該作者
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發(fā)表于 2025-3-29 04:24:27 | 只看該作者
Forecasting CO2 Prices in the EU ETS,a backdoor class. The backdoor will be finally implanted into the target model after it is trained on the poisoned data. During the inference phase, the attacker can activate the backdoor in two ways: slightly modifying the input image to obtain the trigger feature, or taking an image that naturally
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發(fā)表于 2025-3-29 09:19:05 | 只看該作者
Christian Faber,Patrick Heinemannn, as well as feedback excitation and inhibition, and a spike-based neural network that focuses on a high degree of biologically plausible excitatory as well as inhibitory spike-timing-dependent plasticity. Both networks have been trained on natural scenes and have been earlier demonstrated to learn
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Drug Design – Do We Really Want to Be “Original”?t seen in the seed compounds. However, these compounds are difficult to synthesize, and project partners considered the trustworthiness of the models that selected them to be insufficient to justify the high synthesis costs. This highlights the actual bottleneck limiting the breakthrough of . compou
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發(fā)表于 2025-3-29 21:49:10 | 只看該作者
Elucidation of Molecular Substructures from Nuclear Magnetic Resonance Spectra Using Gradient Boostieak intensities. XGBoost classifiers were trained to correlate these spectroscopic signature matrices with molecular substructures represented as MACCS keys. We evaluated the model performance on the full dataset and on constrained chemical space subset. The results indicated that the model’s capaci
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發(fā)表于 2025-3-30 01:41:22 | 只看該作者
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發(fā)表于 2025-3-30 06:52:06 | 只看該作者
Scaffold Splits Overestimate Virtual Screening Performance with approximately 30,000 to 50,000 molecules tested on a different cancer cell line. Each dataset was split with three methods: scaffold, Butina clustering and the more accurate Uniform Manifold Approximation and Projection (UMAP) clustering. Regardless of the model, model performance is much wors
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