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Found 7 papers, 2 papers with code
Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset
To construct the ChatCoach system, we developed a dataset and integrated Large Language Models such as ChatGPT and Llama2, aiming to assess their effectiveness in communicative medical coaching tasks.
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees
In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain.
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
While deep learning models have achieved remarkable success across a range of medical image analysis tasks, deployment of these models in real clinical contexts requires that they be robust to variability in the acquired images.
Advancing Test-Time Adaptation for Acoustic Foundation Models in Open-World Shifts
However, while acoustic models face similar challenges due to distribution shifts in test-time speech, TTA techniques specifically designed for acoustic modeling in the context of open-world data shifts remain scarce.
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN
However, it is challenging for GANs to model distributions of separate non-i. i. d.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
