標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc [打印本頁] 作者: 生長變吼叫 時(shí)間: 2025-3-21 19:04
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋學(xué)科排名
作者: 夾死提手勢 時(shí)間: 2025-3-21 21:01
https://doi.org/10.1007/978-3-031-72359-9artificial intelligence; classification; deep learning; generative models; graph neural networks; image p作者: 東西 時(shí)間: 2025-3-22 03:09
978-3-031-72358-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 倔強(qiáng)一點(diǎn) 時(shí)間: 2025-3-22 07:54
Michael Giretzlehner,Lars-Peter Kamolz identifying hits. Hence, there is a clear need for ‘big data’ compatible chemoinformatics methods to analyze such vast combinatorial compound collections. For example, a library can be characterized by its data distribution on a 2D map. Generative Topographic Mapping (GTM) is particularly well-suit作者: Implicit 時(shí)間: 2025-3-22 11:56 作者: FOLLY 時(shí)間: 2025-3-22 13:23 作者: 反饋 時(shí)間: 2025-3-22 20:01 作者: 省略 時(shí)間: 2025-3-23 00:46
Language, Morality, and Legitimacyy. Data splitting is crucial for better benchmarking of such AI models. Traditional random data splits produce similar molecules between training and test sets, conflicting with the reality of VS libraries which mostly contain structurally distinct compounds. Scaffold split, grouping molecules by sh作者: adipose-tissue 時(shí)間: 2025-3-23 03:46
Handbook of Business LegitimacyThese libraries have grown over the years and currently count several billions commercially available compounds. This raises the need for high-throughput virtual screening approaches that can handle these sizes in a reasonable amount of time. In this paper we introduce our Target-Aware Drug Activity作者: Melodrama 時(shí)間: 2025-3-23 05:43 作者: Meander 時(shí)間: 2025-3-23 13:25 作者: Foolproof 時(shí)間: 2025-3-23 14:38 作者: 專心 時(shí)間: 2025-3-23 18:41
Ania Lian,Neary Lay,Andrew Lianus chaotic dynamical systems. However, the prediction horizon is limited owing to the instability of the reservoir-computing system. In this study, to suppress this instability, oscillations were fed into the reservoir network, which exhibited chaotic behavior. In response to oscillations, the reser作者: CRACK 時(shí)間: 2025-3-24 00:40 作者: Myelin 時(shí)間: 2025-3-24 02:59 作者: dictator 時(shí)間: 2025-3-24 09:14
Mapping Pre-primary CLIL in Russiacomputational power. To address this, we plan to combine reservoirs operating on different timescales together to create a heterogeneous reservoir. We simulate this using a new multiple timescale ESN model. We also introduce “mock materials” so that future works may focus on combining different mate作者: LASH 時(shí)間: 2025-3-24 11:48
Forecasting CO2 Prices in the EU ETS,arty providers to label their unlabeled data. This practice is widely regarded as secure, even in cases where some annotated errors occur, as the impact of these minor inaccuracies on the final performance of the models is negligible and existing backdoor attacks require attacker’s ability to poison作者: pineal-gland 時(shí)間: 2025-3-24 15:23 作者: REP 時(shí)間: 2025-3-24 21:49
Christian Faber,Patrick Heinemannnetworks have been found vulnerable to multiple kinds of natural, artificial, and adversarial image perturbations. In contrast, the human visual system has a remarkable robustness against a wide range of perturbations. At present, it is still unclear what mechanisms underlie this robustness. To bett作者: 諂媚于性 時(shí)間: 2025-3-25 00:28
Artificial Neural Networks and Machine Learning – ICANN 2024978-3-031-72359-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Pepsin 時(shí)間: 2025-3-25 04:06 作者: 想象 時(shí)間: 2025-3-25 09:14 作者: Cytology 時(shí)間: 2025-3-25 14:55
Elucidation of Molecular Substructures from Nuclear Magnetic Resonance Spectra Using Gradient Boostierse problem. Typically, the QSAR process uses the molecule structure features to produce a predictive model for the molecular activity. In case of the inverse problem, features derived from the molecular activity are used to produce a predictive model for the molecular structure. This work demonstr作者: 神圣在玷污 時(shí)間: 2025-3-25 19:24
Neural SHAKE: Geometric Constraints in?Graph Generative Modelshe number of degrees of freedom. Incorporating prior information about geometric patterns, such as distances, angles, and dihedrals, is crucial for ensuring the accurate physical characteristics of molecules by increasing the likelihood of sampling low-energy conformations. These geometric patterns 作者: indemnify 時(shí)間: 2025-3-25 21:29
Scaffold Splits Overestimate Virtual Screening Performancey. Data splitting is crucial for better benchmarking of such AI models. Traditional random data splits produce similar molecules between training and test sets, conflicting with the reality of VS libraries which mostly contain structurally distinct compounds. Scaffold split, grouping molecules by sh作者: 收到 時(shí)間: 2025-3-26 04:12 作者: monochromatic 時(shí)間: 2025-3-26 06:00 作者: Cerumen 時(shí)間: 2025-3-26 10:57 作者: arthroplasty 時(shí)間: 2025-3-26 15:13 作者: 未完成 時(shí)間: 2025-3-26 20:05
Oscillation-Driven Reservoir Computing for?Long-Term Replication of?Chaotic Time Seriesus chaotic dynamical systems. However, the prediction horizon is limited owing to the instability of the reservoir-computing system. In this study, to suppress this instability, oscillations were fed into the reservoir network, which exhibited chaotic behavior. In response to oscillations, the reser作者: 射手座 時(shí)間: 2025-3-26 21:29
Prediction of?Reaching Movements with?Target Information Towards Trans-humeral Prosthesis Control Usjoints need to be reconstructed, and the less kinetic information is available in the residual limb. By exploiting contextual information, such as the position and orientation of a target in a reaching task, we aim to reconstruct the natural dynamics of the distal joints using recurrent neural netwo作者: 聚集 時(shí)間: 2025-3-27 01:32 作者: overweight 時(shí)間: 2025-3-27 07:48 作者: Offstage 時(shí)間: 2025-3-27 13:03 作者: 線 時(shí)間: 2025-3-27 14:04
MADE: A Universal Fine-Tuning Framework to?Enhance Robustness of?Machine Reading Comprehension. However, they suffer from poor generalization ability and appear vulnerable facing even trivial attacks. We propose a novel MADE framework for automatically detecting potential biases in MRC models. Furthermore, by employing a three-stage enhanced fine-tuning method, we relieve the susceptibility 作者: Dorsal 時(shí)間: 2025-3-27 21:21 作者: 無價(jià)值 時(shí)間: 2025-3-27 23:30
Michael Giretzlehner,Lars-Peter Kamolzodel which uses a Graph Convolutional Network (GCN) to predict data projections on GTM, solely based on the information about the reaction and BB sets used for the library preparation. Ten DNA-Encoded Combinatorial Libraries (DELs), each containing one million compounds, were used to train and evalu作者: reptile 時(shí)間: 2025-3-28 02:59 作者: 大洪水 時(shí)間: 2025-3-28 07:00
Religion, Culture, and Business Legitimacyeak 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作者: NOMAD 時(shí)間: 2025-3-28 11:00 作者: Landlocked 時(shí)間: 2025-3-28 18:37 作者: 無辜 時(shí)間: 2025-3-28 19:17 作者: 公理 時(shí)間: 2025-3-29 02:54 作者: innovation 時(shí)間: 2025-3-29 04:24
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作者: 強(qiáng)制性 時(shí)間: 2025-3-29 09:19
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作者: ALOFT 時(shí)間: 2025-3-29 13:03 作者: Offbeat 時(shí)間: 2025-3-29 19:13
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作者: 迷住 時(shí)間: 2025-3-29 21:49
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作者: paroxysm 時(shí)間: 2025-3-30 01:41 作者: 漸變 時(shí)間: 2025-3-30 06:52
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作者: 不感興趣 時(shí)間: 2025-3-30 09:12 作者: 西瓜 時(shí)間: 2025-3-30 12:39 作者: 剛開始 時(shí)間: 2025-3-30 17:38
Clean-Image Backdoor Attacksa 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作者: WATER 時(shí)間: 2025-3-30 21:41 作者: 蛙鳴聲 時(shí)間: 2025-3-31 01:55
Conference proceedings 2024 detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision...Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intel作者: disrupt 時(shí)間: 2025-3-31 07:18
0302-9743 : generative methods; and topics in computer vision...Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intel978-3-031-72358-2978-3-031-72359-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 有偏見 時(shí)間: 2025-3-31 11:58
