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Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand-supply imbalance in healthcare.
Path Signatures for Diversity in Probabilistic Trajectory Optimisation
Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated.
Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models
The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support.
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis
However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection.
Real-time Aerial Detection and Reasoning on Embedded-UAVs
We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs.
A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding
In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map.
Discover Life Skills for Planning with Bandits via Observing and Learning How the World Works
We formulate skills as high-level abstract policies that propose action plans based on the current state.
Water and Sediment Analyse Using Predictive Models
The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation.
L4KDE: Learning for KinoDynamic Tree Expansion
We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints.
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making.
sbp-env: Sampling-based Motion Planners' Testing Environment
Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning.
Learning Efficient and Robust Ordinary Differential Equations via Diffeomorphisms
Consequently, by restricting the base ODE to be amenable to integration, we can speed up and improve the robustness of integrating trajectories from the learned system.
Parallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs).
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements.
Robust Hierarchical Planning with Policy Delegation
We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation.
Learning to Plan Optimally with Flow-based Motion Planner
The normalising flow based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based planners.
OCTNet: Trajectory Generation in New Environments from Past Experiences
This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.
Bayesian Local Sampling-based Planning
In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution.
Occ-Traj120: Occupancy Maps with Associated Trajectories
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour.
