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Found 11 papers, 4 papers with code
On the Impact of Knowledge Distillation for Model Interpretability
Several recent studies have elucidated why knowledge distillation (KD) improves model performance.
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions
Therefore, it can provide the high predictive performance and interpretability that high-stakes domains need.
Explicit Feature Interaction-aware Graph Neural Networks
However, existing graph neural networks only learn higher-order feature interactions implicitly.
Towards a Rigorous Evaluation of Time-series Anomaly Detection
Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden.
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.
Ranked #17 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation
Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found.
PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation
We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images.
PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation
The cover image with the secret message is called a stego image.
Pivot Correlational Neural Network for Multimodal Video Categorization
The architecture is trained to maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific stream in the network.
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation.
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction
Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology.
