[PDF] Top 20 Neural Learning Methods for Human-Computer Interaction
Has 10000 "Neural Learning Methods for Human-Computer Interaction" found on our website. Below are the top 20 most common "Neural Learning Methods for Human-Computer Interaction".
Neural Learning Methods for Human-Computer Interaction
... Title: Neural Learning Methods for Human-Machine Interaction Keywords: Freehand Gestures, Machine Learning, Human-Machine Interac- tion Abstract: Touch-controlled ... Voir le document complet
149
Towards next generation human-computer interaction -- brain-computer interfaces: applications and challenges
... solution for general end-users. Although several methods are proposed to remove the artifacts, they are rarely fully automatic and online pro- cessing methods ...removal methods still need to ... Voir le document complet
3
Online Language Learning to Perform and Describe Actions for Human-Robot Interaction
... The neural model processes grammatical constructions where semantic words (e.g. put, grasp, toy, left, right) are replaced by a common marker. This is done with only a predefined set of grammatical words (after, ... Voir le document complet
2
Frequency Domain Forecasting Approach for Latency Reduction in Direct Human-Computer Interaction
... prediction methods. Particularly, trajectory prediction using Kalman filter for a chain of integrators was proposed in [9], and a method based on the first-order Taylor series was used in [10], where the ... Voir le document complet
7
A Multimodal Dataset for Object Model Learning from Natural Human-Robot Interaction
... strategies for target object segmentation thanks to a simple initial interaction ...these methods are trained on annotated data from our ...enable learning from noisy object ... Voir le document complet
9
Human-Computer interaction to learn scenarios from ICU multivariate time series
... environment for an in-depth exploration by the clinician of ...such human-computer collaboration could help with the definition of signatures representative of specific ... Voir le document complet
6
Vision-based human gestures recognition for human-robot interaction
... Convolutional Neural Network for Hand Gestures Detection 27 and indoor architecture images (explained in Section ...opted for transfer learning for gesture recognition, exploiting ... Voir le document complet
123
Learning Users' and Personality-Gender Preferences in Close Human-Robot Interaction
... II. METHODS The high level framework used here was introduced in a previous work [4], where the OCC Model was used to generate predefined behaviors based on the performance of the ...values for each ... Voir le document complet
9
Neural Methods for Event Extraction
... relation-specific human input [ Banko et ...distinguish for characterizing existing approaches refers to the way the extraction model is defined: either automati- cally from a set of annotations, which ... Voir le document complet
153
Optimizing the use of SSVEP-based brain-computer interfaces for human-computer interaction
... classification rate of 50.4% (SD = 16.8%), which is above chance level (33%) but still a bit lower than what has been observed in other SSVEP experiments with similar settings [Lalor et al., 2005, Legény et al., 2013, ... Voir le document complet
177
Human or neural translation?
... between human and neural machine translations at the sentence ...deep learning methods, investigating the impact of text domains and MT systems (in-house neural engines, Google ... Voir le document complet
13
Research methods from the social sciences in human computer interaction
... Archives des publications du CNRC For the publisher’s version, please access the DOI link below./ Pour consulter la version de l’éditeur, utilisez le lien DOI ci-dessous. Access and use of this website and the ... Voir le document complet
18
Human or neural translation?
... between human and neural machine translations at the sentence ...deep learning methods, investigating the impact of text domains and MT systems (in-house neural engines, Google ... Voir le document complet
12
Perception and human interaction for developmental learning of objects and affordances
... the computer vision ...body, human hands, colored or textured objects) and it should perform online and incremental learning of new objects in an open-ended ...tailored for specific tasks in ... Voir le document complet
8
Statistical Methods for Neural Network Prediction Models
... addressed. For example, it is crucial to distinguish the positive features, ...reduction methods may also have effects on the generalization properties of the models ... Voir le document complet
55
Assembly output codes for learning neural networks
... Deep Learning; Coding theory; Classification ...Automatic learning systems are different from storing systems in that they aim at generalizing to unknown ...suited for the task at hand, ... Voir le document complet
6
Planning for human robot interaction
... reactive methods, such as turning towards a goal and moving ...Reactive methods fail in environments that have sufficiently constrain- ing obstacles like walls that also form dead ... Voir le document complet
240
Pragmatic Frames for Teaching and Learning in Human–Robot interaction: Review and Challenges
... from human users that are inex- perienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in ...efficient learning and teaching in ... Voir le document complet
21
Learning Obstacle Representations for Neural Motion Planning
... details for our method and for other methods used for com- ...reinforcement learning, we use Soft Actor Critic (SAC) [ 18 ] with automatic entropy tuning, a learning rate of ... Voir le document complet
11
On Tree-based Methods for Similarity Learning
... similarity learning has been recently expressed as a pairwise bipartite ranking problem in [21], the task consisting in learning a similarity function that ranks the elements of a database by decreasing ... Voir le document complet
18
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