Search Results for author:
Found 40 papers, 15 papers with code
PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets
This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.
Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems
Testing and evaluation is an important step before the large-scale application of the autonomous driving systems (ADSs).
SkyServe: Serving AI Models across Regions and Clouds with Spot Instances
While spot instances have long been offered with a large discount, spot preemptions have discouraged users from using them to host model replicas when serving AI models.
Robust image representations with counterfactual contrastive learning
Notably, counterfactual contrastive learning achieves superior downstream performance on both in-distribution and on external datasets, especially for images acquired with scanners under-represented in the training set.
Latent 3D Brain MRI Counterfactual
To address these challenges, we propose a two-stage method that constructs a Structural Causal Model (SCM) within the latent space.
Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates
We develop a framework to evaluate, compare, and visualize the reliability and alignment of LLM judges to provide informative observations that help choose LLM judges for alignment tasks.
Mitigating attribute amplification in counterfactual image generation
Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries.
Counterfactual contrastive learning: robust representations via causal image synthesis
Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings.
UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control
Video Diffusion Models have been developed for video generation, usually integrating text and image conditioning to enhance control over the generated content.
Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method
Bargaining is an important and unique part of negotiation between humans.
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging and identifying safety risks given agent interaction records.
High Fidelity Image Counterfactuals with Probabilistic Causal Models
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models.
Penalty Gradient Normalization for Generative Adversarial Networks
In this paper, we propose a novel normalization method called penalty gradient normalization (PGN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space.
Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail
With the development of Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple practical scenarios, including electricity transactions.
Causal Machine Learning for Healthcare and Precision Medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.
Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces
This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization
Disease is one of the fundamental entities in biomedical research.
Deep Multi-attribute Graph Representation Learning on Protein Structures
To address the above challenges, we propose a new graph neural network architecture to represent the proteins as 3D graphs and predict both distance geometric graph representation and dihedral geometric graph representation together.
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy.
AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel.
Learning to synthesise the ageing brain without longitudinal data
Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable).
A Quality-Diversity Controllable GAN for Text Generation
Text generation is a critical and difficult natural language processing task.
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net
In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control.
Plackett-Luce model for learning-to-rank task
List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based.
A simple discriminative training method for machine translation with large-scale features
Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation.
Analysis of Regression Tree Fitting Algorithms in Learning to Rank
In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle.
RGB-T Image Saliency Detection via Collaborative Graph Learning
In this paper, we propose an effective approach for RGB-T image saliency detection.
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Pseudo healthy synthesis, i. e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation.
Hybrid Policies Using Inverse Rewards for Reinforcement Learning
This paper puts forward a broad-spectrum improvement for reinforcement learning algorithms, which combines the policies using original rewards and inverse (negative) rewards.
Multi-View 3D Object Detection Network for Autonomous Driving
We encode the sparse 3D point cloud with a compact multi-view representation.
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Convolutional network techniques have recently achieved great success in vision based detection tasks.
Ranked #4 on
Object Detection
on KITTI Cars Moderate
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM).
Fully Convolutional Attention Networks for Fine-Grained Recognition
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses.
Une m\'ethode discriminant formation simple pour la traduction automatique avec Grands Caract\'eristiques
Marge infus{\'e} algorithmes d{\'e}tendus (MIRAS) dominent mod{\`e}le de tuning dans la traduction automatique statistique dans le cas des grandes caract{\'e}ristiques de l{'}{\'e}chelle, mais ils sont {\'e}galement c{\'e}l{\`e}bres pour la complexit{\'e} de mise en {\oe}uvre.
Learning From Massive Noisy Labeled Data for Image Classification
To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels.
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training examples; once the training classification error is reduced to a local coordinatewise minimum, DirectBoost runs a greedy coordinate ascent algorithm that continuously adds weak classifiers to maximize any targeted arbitrarily defined margins until reaching a local coordinatewise maximum of the margins in a certain sense.
