data

through the lens of density and, interestingly, observe that locally sparse regions tend to have more informative samples than dense regions.
To reduce the computational bottlenecks in estimating the density, we introduce a fresh density approximation based on locality-sensitive hashing.
Experimental results demonstrate the efficacy of DACS in both classification and regression tasks and specifically show that DACS can produce state-of-the-art performance in a practical scenario.
Since DACS is weakly dependent on architectures, we also present a simple yet effective combination solution to show that the existing methods can be beneficially combined with DACS.

• In the network pruning task, the input of the current convolutional layer is determined by the output of the previous convolutional layer.
• Meanwhile, we also design an auxiliary task called graph regularization to improve the schema information mentioned in the schema-linking graph.
• The technique proposed by Han et al. applies this method, with 5 iterations between pruning and fine-tuning, to weight magnitude pruning.
• Bi-hardNCE has both forward contrastive estimation and backward contrastive estimation, which forces model to tell apart the real symptom from negative symptoms and meanwhile distinguish true query from negative queries.

The dataset contains 870,000 jets, balanced across all classes and put into 472,500 jets for training, 157,500 jets for validation, and 240,000 jets for testing.
Adopting the same baseline architecture as in Duarte et al. , we consider a fully-connected NN comprising three hidden layers with rectified linear unit (Nair and Hinton, 2010; Glorot et al., 2011) activation functions, shown in Figure 1.
The output layer has five nodes, yielding a probability for every of the five classes through a softmax activation function.

Learning Task-relevant Representations For Generalization Via Characteristic Functions Of Reward Sequence Distributions

Minus the direct supervision and instruction of teachers, online education is definitely worried about potential distractions and misunderstandings.
Learning Style Classification is proposed to analyze the learning behavior patterns of online learning users, based on which personalized learning paths are generated to greatly help them learn and maintain their interests.

First, we devise a taxonomy of affects that was small yet covers the important nuances needed for the application.
Second, to get training data for our models, we balance between signals which are already open to us and data we collected through a carefully crafted human annotation effort on 800k posts.
We demonstrate that affective response information learned out of this dataset improves a module in the recommendation system by a lot more than 8%.

Systems

Over the past two years, Greykite forecasts have been trusted by Finance, Engineering, and Product teams for resource planning and allocation, target setting and progress tracking, anomaly detection and real cause analysis.
We expect Greykite to be beneficial to forecast practitioners with similar applications who need accurate, interpretable forecasts that capture complex dynamics common to time series linked to human activity.
Federated learning is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server.
Existing defenses mainly depend on Byzantine-robust or provably robust FL methods, which try to learn an accurate global model even if some clients are malicious.

• This confirms that DuARE is a practical and industrial-grade solution for large-scale cost-effective road extraction from
• We rigorously show that, both theoretically and empirically, this property results in training instability which could cause severe practical issues.
• Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL.

The first challenge is determining the perfect rank of all the layers and the second reason is training the neural network into a compression-friendly form.
To overcome the two challenges, we propose BSR (Beam-search and Stable Rank), a low-rank compression algorithm that embodies an efficient rank-selection method and a unique compression-friendly training method.
For the rank selection, BSR employs a modified beam search that can perform a joint optimization of the rank allocations over all the layers in contrast to the previously used heuristic methods.
For the compression-friendly training, BSR adopts a regularization loss derived from a modified stable rank, which can control the rank while incurring minimal harm in performance.

We hope that our approach will enable ANNs having vast amounts of neurons and evolved topologies to manage to handling complex real-world tasks that are intractable using state-of-the-art methods.
Network pruning is an important research field aiming at reducing computational costs of neural networks.
Conventional approaches follow a fixed paradigm which first trains a large and redundant network, and determines which units (e.g., channels) are less important and thus can be removed.

QAP is really a promising technique to build efficient NN implementations and would benefit from further study on additional benchmark tasks.
Future investigation of QAP, variations on the task, and combination with complementary methods may lead to even greater NN efficiency gains and could provide insights into what the NN is learning.
Finally, the accuracy and neural efficiency of the best accuracy models from the BO procedure in Section 5.2 are represented as stars in the most notable row of Figure 7.
They have slightly lower neural efficiencies because the width of each hidden layer is bigger than in the QAP models while the entropy remains relatively similar to those same models.
The BO models, as seen in the upper left graph of Figure 7, are no better at generalizing under increasing class randomization fractions compared to the QAP models.