40 research outputs found
Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT
ACKNOWLEDGMENT For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPostprin
Domain-adapted driving scene understanding with uncertainty-aware and diversified generative adversarial networks
Funding Information: This work was supported by Fisheries Innovation & Sustainability (FIS) and the U.K. Department for Environment, Food & Rural Affairs (DEFRA) under grant number FIS039 and FIS045A.Peer reviewedPublisher PD
Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation
The author wishes to extend sincere appreciation to Professor Lin Shi for the generous provision of equipment support, which significantly aided in the successful completion of this research. Furthermore, the author expresses gratitude to Associate Professor Ning Li and Teacher Wei Guan for their invaluable academic guidance and unwavering support. Their expertise and advice played a crucial role in shaping the direction and quality of this research.Peer reviewe
Fine-grained RNN with Transfer Learning for Energy Consumption Estimation on EVs
This work is supported by the Engineering and Physical Sciences Research Council, under PETRAS SRF grant MAGIC (EP/S035362/1) and the University of Glasgow Impact Acceleration Account.Peer reviewedPostprin
Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection
ACKNOWLEDGMENT For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. This work was supported by Cancer Research UK (CRUK) under Grant EDDPJT-May23/100001Peer reviewedPostprin
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
Integrating large language models (LLMs) into healthcare presents potential
but faces challenges. Directly pre-training LLMs for domains like medicine is
resource-heavy and sometimes unfeasible. Sole reliance on Supervised
Fine-tuning (SFT) can result in overconfident predictions and may not tap into
domain specific insights. Addressing these challenges, we present a multi-stage
training method combining Domain-specific Continued Pre-training (DCPT), SFT,
and Direct Preference Optimization (DPO). A notable contribution of our study
is the introduction of a 3Gb Chinese Medicine (ChiMed) dataset, encompassing
medical question answering, plain texts, knowledge graphs, and dialogues,
segmented into three training stages. The medical LLM trained with our
pipeline, Qilin-Med, exhibits significant performance boosts. In the CPT and
SFT phases, it achieves 38.4% and 40.0% accuracy on the CMExam, surpassing
Baichuan-7B's 33.5%. In the DPO phase, on the Huatuo-26M test set, it scores
16.66 in BLEU-1 and 27.44 in ROUGE1, outperforming the SFT's 12.69 and 24.21.
This highlights the strength of our training approach in refining LLMs for
medical applications
Improving Synthetic to Realistic Semantic Segmentation with Parallel Generative Ensembles for Autonomous Urban Driving
Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Existing research efforts for multi-interest candidate matching in
recommender systems mainly focus on improving model architecture or
incorporating additional information, neglecting the importance of training
schemes. This work revisits the training framework and uncovers two major
problems hindering the expressiveness of learned multi-interest
representations. First, the current training objective (i.e., uniformly sampled
softmax) fails to effectively train discriminative representations in a
multi-interest learning scenario due to the severe increase in easy negative
samples. Second, a routing collapse problem is observed where each learned
interest may collapse to express information only from a single item, resulting
in information loss. To address these issues, we propose the REMI framework,
consisting of an Interest-aware Hard Negative mining strategy (IHN) and a
Routing Regularization (RR) method. IHN emphasizes interest-aware hard
negatives by proposing an ideal sampling distribution and developing a
Monte-Carlo strategy for efficient approximation. RR prevents routing collapse
by introducing a novel regularization term on the item-to-interest routing
matrices. These two components enhance the learned multi-interest
representations from both the optimization objective and the composition
information. REMI is a general framework that can be readily applied to various
existing multi-interest candidate matching methods. Experiments on three
real-world datasets show our method can significantly improve state-of-the-art
methods with easy implementation and negligible computational overhead. The
source code will be released.Comment: RecSys 202
Equivariant Contrastive Learning for Sequential Recommendation
Contrastive learning (CL) benefits the training of sequential recommendation
models with informative self-supervision signals. Existing solutions apply
general sequential data augmentation strategies to generate positive pairs and
encourage their representations to be invariant. However, due to the inherent
properties of user behavior sequences, some augmentation strategies, such as
item substitution, can lead to changes in user intent. Learning
indiscriminately invariant representations for all augmentation strategies
might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for
Sequential Recommendation (ECL-SR), which endows SR models with great
discriminative power, making the learned user behavior representations
sensitive to invasive augmentations (e.g., item substitution) and insensitive
to mild augmentations (e.g., featurelevel dropout masking). In detail, we use
the conditional discriminator to capture differences in behavior due to item
substitution, which encourages the user behavior encoder to be equivariant to
invasive augmentations. Comprehensive experiments on four benchmark datasets
show that the proposed ECL-SR framework achieves competitive performance
compared to state-of-the-art SR models. The source code is available at
https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202
Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning
The utilization of social media in epidemic surveillance has been well
established. Nonetheless, bias is often introduced when pre-defined lexicons
are used to retrieve relevant corpus. This study introduces a framework aimed
at curating extensive dictionaries of medical colloquialisms and Unified
Medical Language System (UMLS) concepts. The framework comprises three modules:
a BERT-based Named Entity Recognition (NER) model that identifies medical
entities from social media content, a deep-learning powered normalization
module that standardizes the extracted entities, and a semi-supervised
clustering module that assigns the most probable UMLS concept to each
standardized entity. We applied this framework to COVID-19-related tweets from
February 1, 2020, to April 30, 2022, generating a symptom dictionary (available
at https://github.com/ningkko/UMLS_colloquialism/) composed of 9,249
standardized entities mapped to 876 UMLS concepts and 38,175 colloquial
expressions. This framework demonstrates encouraging potential in addressing
the constraints of keyword matching information retrieval in social media-based
public health research.Comment: Accepted to ICHI 2023 (The 11th IEEE International Conference on
Healthcare Informatics) as a poster presentatio