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Found 7 papers, 1 papers with code
Prompting Large Language Models for Clinical Temporal Relation Extraction
We explored four fine-tuning strategies for GatorTron-Base: (1) Standard Fine-Tuning, (2) Hard-Prompting with Unfrozen LLMs, (3) Soft-Prompting with Frozen LLMs, and (4) Low-Rank Adaptation (LoRA) with Frozen LLMs.
A Simple yet Effective Framework for Active Learning to Rank
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces
Policy evaluation with linear function approximation is an important problem in reinforcement learning.
Thompson Sampling for Budgeted Multi-armed Bandits
In this paper, we extend the Thompson sampling to Budgeted MAB, where there is random cost for pulling an arm and the total cost is constrained by a budget.
Generalization Analysis for Game-Theoretic Machine Learning
For Internet applications like sponsored search, cautions need to be taken when using machine learning to optimize their mechanisms (e. g., auction) since self-interested agents in these applications may change their behaviors (and thus the data distribution) in response to the mechanisms.
Agent Behavior Prediction and Its Generalization Analysis
Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model.
