exampleseach model has its own example Experiments Data: `ASSISTments2015`, train 80%, test 20%. Epoch: 5 Learning Rate: 0.001 Batch Size: 64 Sequence Type: LSTM, one layer Dim: 100 for all embedding layer Hidden Dim: 100 Optim: Adam Scheduler: StepLR, step size 1, gamma 0.9 ...
In this paper, a novel knowledge tracing model, which makes use of both DKT and knowledge graph embedding (KGE)[25-29], is proposed. DKT has the capacity of assessing users and has no extra requirements on the partition of skills. KGE can be used to make inferences on whether two given...
DASNet -> Dual attentive fully convolutional siamese networks for change detection of high-resolution satellite images Self-Attention for Raw Optical Satellite Time Series Classification planet-movement -> Find and process Planet image pairs to highlight object movement temporal-cluster-matching -> det...
A Deep Bag-of-Features Model for Music Auto-Tagging by Juhan Nam, Jorge Herrera, Kyogu Lee. A Deep Generative Deconvolutional Image Model by Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin. A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding by...
Additionally, the Separated Self-Attentive Neural Knowledge Tracing (SAINT) model adopts a Transformer-based architecture [25], where exercises are embedded in the encoder and student responses are predicted in the decoder, further advancing the capability of knowledge tracing models to handle complex ...
Similarity-aware deep attentive model for clickbait detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macau, China, 14–17 April 2019; Springer: Cham, Switzerland, 2019; pp. 56–69. [Google Scholar] Saleh, H.; Alharbi, A.; Alsamhi, S.H. ...
However, the literature has been less attentive to the way these negative experiences can in some cases function as a resource that enable whistleblowers to develop new careers after the organization. To explore this idea, the paper analyzes 11 high-profile whistleblower cases and asks: What ...
Trajectory-user linking with attentive recurrent network Mutual distillation learning network for trajectory-user linking Adversarial mobility learning for human trajectory classification Self-supervised human mobility learning for next location prediction and trajectory classification Other Perspectives Deep Learning...
A Structured Self-attentive Sentence Embedding [arXiv] Multi-step Reinforcement Learning: A Unifying Algorithm [arXiv] Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG [arXiv] FaSTrack: a Modular Framework for Fast and...
2023-07-25 Mini-PointNetPlus: a local feature descriptor in deep learning model for 3d environment perception Chuanyu Luo et.al. 2307.13300 null 2023-07-21 Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data Sahar Almahfouz Nasser ...