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Found 10 papers, 3 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, necessitating robustness in MedVLP models to diverse prompt styles.
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer
Recent deep multi-view stereo (MVS) methods have widely incorporated transformers into cascade network for high-resolution depth estimation, achieving impressive results.
Understanding Bugs in Multi-Language Deep Learning Frameworks
Third, we found that 28. 6%, 31. 4%, and 16. 0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs.
Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity
For example, our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.
CERT: Contrastive Self-supervised Learning for Language Understanding
We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks.
MedDialog: Two Large-scale Medical Dialogue Datasets
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
Segmentation-Aware Image Denoising without Knowing True Segmentation
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step.
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms.
