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Found 14 papers, 5 papers with code
Baichuan-M1: Pushing the Medical Capability of Large Language Models
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like medicine, remain relatively scarce.
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms
However, reliable BCG-based sleep staging is challenging due to the limited sleep monitoring data available for BCG.
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification.
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions.
PowerPM: Foundation Model for Power Systems
PowerPM consists of a temporal encoder and a hierarchical encoder.
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs).
Full-ECE: A Metric For Token-level Calibration on Large Language Models
Deep Neural Networks (DNNs) excel in various domains but face challenges in providing accurate uncertainty estimates, which are crucial for high-stakes applications.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs.
Ranked #3 on
Knowledge Base Question Answering
on WebQuestionsSP
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e. g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i. e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design.
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period
Every financial crisis has caused a dual shock to the global economy.
PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs).
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary.
