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Found 10 papers, 8 papers with code
Diverse and Admissible Trajectory Prediction through Multimodal Context Understanding
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making.
TrustSQL: A Reliability Benchmark for Text-to-SQL Models with Diverse Unanswerable Questions
To explore this aspect, we introduce TrustSQL, a new benchmark designed to assess the reliability of text-to-SQL models in both single-database and cross-database settings.
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
To develop our dataset, we first construct two uni-modal resources: 1) The MIMIC-CXR-VQA dataset, our newly created medical visual question answering (VQA) benchmark, specifically designed to augment the imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of a previously established table-based EHR QA dataset.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs.
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll.
Exploration into Translation-Equivariant Image Quantization
This is an exploratory study that discovers the current image quantization (vector quantization) do not satisfy translation equivariance in the quantized space due to aliasing.
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel
To address this, we introduce a new neural stochastic processes, Decoupled Kernel Neural Processes (DKNPs), which explicitly learn a separate mean and kernel function to directly model the covariance between output variables in a data-driven manner.
Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction
Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems.
Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making.
