Search Results for author:
Found 27 papers, 6 papers with code
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e. g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation.
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
Especially for the question that require a multi-hop reasoning path, frequent calls to LLM will consume a lot of computing power.
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models.
Unleashing the Potential of Large Language Models for Predictive Tabular Tasks in Data Science
Despite their proficiency in comprehending natural language, LLMs fall short in dealing with structured tabular data.
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving
In autonomous driving, predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to better plan their actions, enhancing safety and efficiency on the road.
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth
We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR).
Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making.
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
After a number of extensive experiments, the experimental results show that the proposed model achieves 99. 06% accuracy in the two-class classification task of tooth-marked tongue identification and 80. 02%.
Transferable Adversarial Attack on Image Tampering Localization
The black-box attack is achieved by relying on the transferability of such adversarial examples to different localizers.
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
This research primarily centers on classification and regression tasks involving tabular data, and conducts rigorous experimental testing and analyses to validate the effectiveness of our methodology.
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding.
Weakly Supervised 3D Object Detection with Multi-Stage Generalization
We devise the DoubleClustering algorithm to obtain object clusters from reconstructed scene-level points, and further enhance the model's detection capabilities by developing three stages of generalization: progressing from complete to partial, static to dynamic, and close to distant.
Tracking Objects with 3D Representation from Videos
In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.
Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation
Spleen volumetry is primarily associated with patients suffering from chronic liver disease and portal hypertension, as they often have spleens with abnormal shapes and sizes.
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection
The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection.
Hyperuniform disordered parametric loudspeaker array
A steerable parametric loudspeaker array is known for its directivity and narrow beam width.
4D Unsupervised Object Discovery
In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information.
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
A Robust Ensemble Model for Patasitic Egg Detection and Classification
Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method.
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Extensive experiments conducted on the MINIST and CIFAR-10 datasets show that our adversarial learning with second-order adversarial examples outperforms other fisrt-order methods, which can improve the model robustness against a wide range of attacks.
Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks
Moreover, through experiments we show that discrete language representation has several advantages compared with continuous feature representation, from the aspects of interpretability, generalization, and robustness.
RNAS: Robust Network Architecture Search beyond DARTS
The vulnerability of Deep Neural Networks (DNNs) (i. e., susceptibility to adversarial attacks) severely limits the application of DNNs.
Validate and Enable Machine Learning in Industrial AI
However, manufacturers and solution partners need to understand how to implement and integrate an AI model into the existing industrial control system.
