Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

Bunion

書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases年度引用




書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名

一再困擾

Foveated Neural Computationional burden. FCLs can be stacked into neural architectures and we evaluate them in several tasks, showing how they efficiently handle the information in the peripheral regions, eventually avoiding the development of misleading biases. When integrated with a model of human attention, FCL-based netwo

anarchist

Trigger Detection for?the?sPHENIX Experiment via?Bipartite Graph Networks with?Set Transformerits importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our mo

Infuriate

Understanding Difficulty-Based Sample Weighting with?a?Universal Difficulty Measuresed as a universal difficulty measure. Furthermore, we provide formal theoretical justifications on the role of difficulty-based weighting for deep learning, consequently revealing its positive influences on both the optimization dynamics and generalization performance of deep models, which is instr

傳染

Avoiding Forgetting and?Allowing Forward Transfer in?Continual Learning via?Sparse NetworksL using fix-capacity models. AFAF allocates a sub-network that enables . transfer of relevant knowledge to a new task while preserving past knowledge, . some of the previously allocated components to utilize the fixed-capacity, and addressing class-ambiguities when similarities exist. The experiment

LIKEN

PrUE: Distilling Knowledge from?Sparse Teacher Networkseffectiveness of the proposed method with experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet. Results indicate that student networks trained with sparse teachers achieve better performance. Besides, our method allows researchers to distill knowledge from deeper networks to improve students fur

Inscrutable

FROB: Few-Shot ROBust Model for?Joint Classification and?Out-of-Distribution Detectiondology for sample generation on the normal class distribution confidence boundary based on generative and discriminative models, including classification. FROB implicitly generates adversarial samples, and forces samples from OoD, including our boundary, to be less confident by the classifier. By in

聯(lián)想

PRoA: A Probabilistic Robustness Assessment Against Functional Perturbationsbilistic robustness of a model, ., the probability of failure encountered by the trained model after deployment. Our experiments demonstrate the effectiveness and flexibility of PRoA in terms of evaluating the probabilistic robustness against a broad range of functional perturbations, and PRoA can s

可轉(zhuǎn)變

Hypothesis Testing for?Class-Conditional Label Noiseor is approximately 1/2. The proposed hypothesis tests are built upon the asymptotic properties of Maximum Likelihood Estimators for Logistic Regression models. We establish the main properties of the tests, including a theoretical and empirical analysis of the dependence of the power on the test on

不溶解