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Found 17 papers, 3 papers with code
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective.
Aspect-Based Sentiment Analysis
Emotion Recognition in Conversation
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Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the probability distribution predicted by the context model as the loss function.
Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction
The proposed module can predict the number of occupied child nodes and map it into an 8- dimensional vector to assist the context model in predicting the probability distribution of the occupancy of the current node for efficient entropy coding.
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
While conditional models incorporating class labels, segmentation masks and image translations are prevalent, there is a gap in utilizing prior clinical knowledge and patient-specific context, suggesting a need for more personalized synthesis approaches and emphasizing the importance of tailoring generative approaches to the unique characteristics of medical data.
Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
Model size and inference speed at deployment time, are major challenges in many deep learning applications.
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition
Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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KDSM: An uplift modeling framework based on knowledge distillation and sample matching
The teacher model is the uplift decision tree (UpliftDT), whose structure is exploited to construct counterfactual sample pairs, and the pairwise incremental prediction is treated as another objective for the student model.
TAPS Responsibility Matrix: A tool for responsible data science by design
However, guidance on the responsibilities of the data scientists and the other involved actors in a data science project is typically missing.
Physical Logic Enhanced Network for Small-Sample Bi-Layer Metallic Tubes Bending Springback Prediction
In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function.
Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL
To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed.
Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANs
Finally, we present the balance between data utility and privacy in synthetic data generation considering the different data structure and characteristics of real-world datasets such as imbalance variables, abnormal distributions, and sparsity of data.
A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction
After the restored Radon data is reconstructed to an image, the image is sent into the second CAGAN trained for recovering the details, so that a high-quality image is obtained.
Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information
In stage one, to better utilize prior information in the Radon domain, we design an adversarial autoencoder to complement the Radon data.
A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction
Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations.
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.
Deep reinforcement learning for optical systems: A case study of mode-locked lasers
We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems.
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent
We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.
