Search Results for author:
Found 10 papers, 5 papers with code
Grounding Language Models for Visual Entity Recognition
We introduce AutoVER, an Autoregressive model for Visual Entity Recognition.
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare.
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions.
Instructed Language Models with Retrievers Are Powerful Entity Linkers
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving
Bayesian Neural Networks (BNNs) that possess a property of uncertainty estimation have been increasingly adopted in a wide range of safety-critical AI applications which demand reliable and robust decision making, e. g., self-driving, rescue robots, medical image diagnosis.
Randomness In Neural Network Training: Characterizing The Impact of Tooling
However, we also find that the cost of ensuring determinism varies dramatically between neural network architectures and hardware types, e. g., with overhead up to $746\%$, $241\%$, and $196\%$ on a spectrum of widely used GPU accelerator architectures, relative to non-deterministic training.
A Graph Representation of Semi-structured Data for Web Question Answering
The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA).
DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction
To address these challenges, we proposed DeepEnroll, a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference.
Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design
In recent years, the CNNs have achieved great successes in the image processing tasks, e. g., image recognition and object detection.
