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Found 12 papers, 7 papers with code
Dual-disentangled Deep Multiple Clustering
In the E-step, the disentanglement learning module employs coarse-grained and fine-grained disentangled representations to obtain a more diverse set of latent factors from the data.
AugDMC: Data Augmentation Guided Deep Multiple Clustering
Thereafter, multiple clusterings based on different aspects of the data can be obtained.
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients
Recently, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e. g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic for deployment in clinical settings (e. g., creating training instances using a fixed time to onset, where the time of onset needs to be known apriori).
Multi-Subset Approach to Early Sepsis Prediction
However, clinical scores like Sequential Organ Failure Assessment (SOFA) are not applicable for early prediction, while machine learning algorithms can help capture the progressing pattern for early prediction.
Improved Visual Fine-tuning with Natural Language Supervision
Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with limited training examples.
Enhancing Peak Network Traffic Prediction via Time-Series Decomposition
For network administration and maintenance, it is critical to anticipate when networks will receive peak volumes of traffic so that adequate resources can be allocated to service requests made to servers.
Unsupervised Visual Representation Learning by Online Constrained K-Means
Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
Ranked #5 on
Unsupervised Image Classification
on CIFAR-10
Improved Knowledge Distillation via Full Kernel Matrix Transfer
Recently, a number of works propose to transfer the pairwise similarity between examples to distill relative information.
Weakly Supervised Representation Learning with Coarse Labels
To mitigate this challenge, we propose an algorithm to learn the fine-grained patterns for the target task, when only its coarse-class labels are available.
Hierarchically Robust Representation Learning
Experiments on benchmark data sets demonstrate the effectiveness of the robust deep representations.
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
The set of triplet constraints has to be sampled within the mini-batch.
Ranked #21 on
Metric Learning
on CUB-200-2011
(using extra training data)
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.
