Papers Unity
“Unity” 태그가 달린 논문 378편 · 필터 해제
A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlyin…
regressionUnityRobust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image
Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whol…
Cell SegmentationDecoderSegmentationSemantic Segmentation+1End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation environment of a highway scene was set up in …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1One-shot Distributed Ridge Regression in High Dimensions
To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental problem in this area: How to do ridge regression in a distributed computing environment? We st…
Distributed ComputingregressionUnityVocal Bursts Intensity PredictionUnity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to …
DiversityExtractive SummarizationExtractive Text SummarizationNatural Language Understanding+3Simulating Content Consistent Vehicle Datasets with Attribute Descent
This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the la…
AttributeData AugmentationDomain AdaptationPerson Re-Identification+2Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each boundi…
Box-supervised Instance SegmentationInstance SegmentationMultiple Instance LearningSegmentation+3Extensions of the Deep Galerkin Method
We extend the Deep Galerkin Method (DGM) introduced in Sirignano and Spiliopoulos (2018)} to solve a number of partial differential equations (PDEs) that arise in the context of optimal stochastic control and mean field …
UnityMean Shift Rejection: Training Deep Neural Networks Without Minibatch Statistics or Normalization
Deep convolutional neural networks are known to be unstable during training at high learning rate unless normalization techniques are employed. Normalizing weights or activations allows the use of higher learning rates, …
Data AugmentationUnityAn Unethical Optimization Principle
If an artificial intelligence aims to maximise risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective function allows sufficiently for this ris…
UnitySIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform
As digital virtual assistants become ubiquitous, it becomes increasingly important to understand the situated behaviour of users as they interact with these assistants. To this end, we introduce SIMMC, an extension to Pa…
AI AgentUnityIntuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a Modified Kalman Filter
Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over…
UnityMaking sense of sensory input
This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that b…
Inductive BiasProgram SynthesisUnityEnvironmental drivers of systematicity and generalization in a situated agent
The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample gen…
UnitySelf-driving scale car trained by Deep reinforcement learning
The self-driving based on deep reinforcement learning, as the most important application of artificial intelligence, has become a popular topic. Most of the current self-driving methods focus on how to directly learn end…
Autonomous DrivingDeep Reinforcement LearningQ-Learningreinforcement-learning+3Bayesian posterior repartitioning for nested sampling
Priors in Bayesian analyses often encode informative domain knowledge that can be useful in making the inference process more efficient. Occasionally, however, priors may be unrepresentative of the parameter values for a…
Bayesian InferenceUnityLearning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach
In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipp…
Deep Reinforcement LearningPositionreinforcement-learningReinforcement Learning+2On the probability of strain invasion in endemic settings: accounting for individual heterogeneity and control in multi-strain dynamics
The rise of antimicrobial drug resistance is an imminent threat to global health that has warranted, and duly received, considerable attention within the medical, microbiological and modelling communities. Outbreaks of d…
UnityBrain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
The fully automated and relatively accurate method of brain tissues segmentation on T2-weighted magnetic resonance perfusion images is proposed. Segmentation with this method provides a possibility to obtain perfusion re…
AnatomySegmentationUnityA PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameters Based Unsupervised Classification Scheme Using a Geodesic Distance
We propose a generic Scattering Power Factorization Framework (SPFF) for Polarimetric Synthetic Aperture Radar (PolSAR) data to directly obtain $N$ scattering power components along with a residue power component for eac…
General ClassificationUnity