Results 91 to 100 of about 199,039 (272)

Deep Bayesian Reward Learning from Preferences

, 2019
Workshop on Safety and Robustness in Decision Making at the 33rd Conference on Neural Information Processing Systems (NeurIPS ...
Brown, Daniel S., Niekum, Scott
openaire   +2 more sources

Enabling Real‐Time Shape‐Sensing in Soft Robots via a Miniaturized, Single‐Signal, Color‐Tuned Soft Optical Sensor

Advanced Robotics Research, EarlyView.
A miniaturized soft optical sensor that uses thin film color tuning enables real‐time 3D shape‐sensing from a single red–green–blue (RGB) signal. When integrated into a soft robot, it enables closed‐loop control and autonomous navigation in a phantom lung environment without the need for onboard electronics, achieving sub‐millimeter accuracy through ...
Frank Juliá Wise, Yitong Lu, Daniel Van Lewen, Riley Applebaum, Bianca Andrada, Johnathan Reamer, Sheila Russo   +6 more
wiley   +1 more source

How to beat a Bayesian adversary

European Journal of Applied Mathematics
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in ...
Zihan Ding, Kexin Jin, Jonas Latz, Chenguang Liu   +3 more
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Bayesian Computation in Deep Learning

Bayesian methods have shown success in deep learning applications. For example, in predictive tasks, Bayesian neural networks leverage Bayesian reasoning of model uncertainty to improve the reliability and uncertainty awareness of deep neural networks. In generative modeling domain, many widely used deep generative models, such as deep latent variable ...
Chen, Wenlong, Li, Bolian, Zhang, Ruqi, Li, Yingzhen   +3 more
openaire   +2 more sources

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

EPJ Web of Conferences, 2017
Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular.
Chernoded Andrey, Dudko Lev, Myagkov Igor, Volkov Petr   +3 more
doaj   +1 more source

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Advanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li   +27 more
wiley   +1 more source

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

, 2017
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
Bartels, Simon, Falkner, Stefan, Hennig, Philipp, Hutter, Frank, Klein, Aaron   +4 more
core  

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source