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Found 19 papers, 3 papers with code
LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
We present LLM-ABR, the first system that utilizes the generative capabilities of large language models (LLMs) to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics.
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
This study proposes a novel evaluation framework using large language models (LLMs) to compare radiology reports for assessment.
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning
Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts.
Toward Open-ended Embodied Tasks Solving
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years.
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.
Unified Medical Image Pre-training in Language-Guided Common Semantic Space
To overcome the aforementioned challenges, we propose an Unified Medical Image Pre-training framework, namely UniMedI, which utilizes diagnostic reports as common semantic space to create unified representations for diverse modalities of medical images (especially for 2D and 3D images).
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges.
Is Risk-Sensitive Reinforcement Learning Properly Resolved?
Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction.
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.
Bootstrapped Transformer for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment.
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks.
Deep Ensemble Policy Learning
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
Adaptive Q-learning for Interaction-Limited Reinforcement Learning
Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, i. e., a pessimistic update strategy for the offline dataset and a greedy or no pessimistic update scheme for the online dataset.
Variational oracle guiding for reinforcement learning
How to make intelligent decisions is a central problem in machine learning and cognitive science.
P-BN: Towards Effective Batch Normalization in the Path Space
Hence, some new algorithms that conduct optimizations directly in the path space (the path space is proven to be PSI) were developed, such as Stochastic Gradient Descent (SGD) in the path space, and it was shown that SGD in the path space is superior to that in the weight space.
Expressiveness in Deep Reinforcement Learning
Based on our observations, we formally define expressiveness of the state extractor as the rank of the matrix composed by representations.
