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Found 14 papers, 4 papers with code
Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language
Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks.
Are LLM-based Evaluators Confusing NLG Quality Criteria?
Some prior work has shown that LLMs perform well in NLG evaluation for different tasks.
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC).
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering
To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets.
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation
In the second stage, we distill the knowledge from the existing teacher adapters into the student adapter to help its inference.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (LLMs), function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning.
Learning-Initialized Trajectory Planning in Unknown Environments
Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima.
On-the-Fly Guidance Training for Medical Image Registration
Weakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy.
Ranked #1 on
Medical Image Registration
on OASIS
Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent
The significant practical advantages of the HB method for learning problems are well known, but the question of reducing communications has not been addressed.
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting
Compared to the classical ADMM, a key feature of OADMM is that transmissions are ordered among workers at each iteration such that a worker with the most informative data broadcasts its local variable to neighbors first, and neighbors who have not transmitted yet can update their local variables based on that received transmission.
Distributed Learning With Sparsified Gradient Differences
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning
Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers.
Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model
The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic.
