However the resulting models are specialized to a single very specific task and domain. Adapting the learned classification to new domains is a hard problem due to at least three reasons: (1) the new domains and the tasks might be drastically different; (2) there might be very limited ...
Learning is performed through the open-source RL library smarties54. The library leverages efficiently the computing resources by separating the task of updating the policy parameters from the task of collecting interaction data. The flow simulations are distributed across workers who collect, for each...
A possible solution is to learn both tasks together using a multi-task approach. Some current methods address this problem by learning semantic segmentation and monocular depth together. However, monocular depth estimation from single images is an ill-posed problem. A better solution is to estimate...
(incremental learning). Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference). In this paper, we show that both can be achieved simply by reparameterizing the convolutions of ...
We consider a task as a multi-agent MDP, which consists of a series of actions, states, a bounded reward function, a transition function, etc. For a model-free reinforcement learning problem, the transition and reward functions can be reconstructed from a sequence of unordered transitions. It...
Machine LearningCaffe - A fast framework for neural networks. [BSD] CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library. [BSD] darknet - Open source neural network framework written in C and CUDA. [PublicDomain] website Dlib ⚡ - A modern C++11 machine ...
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING A natural solution for one-shot learning is to augment training data to handle the data deficiency problem. However, directly augmenting in the image domain may not necessarily generate training data that sufficiently explore the intra...
DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network. Mathematics 2022, 10, 721. https://doi.org/10.3390/math10050721 AMA Style Xiao Y, Li C, Liu V. DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network. Mathematics. 2022; ...
The resulting features are weighted and combined with those from the time domain. The decoder processes the information from the encoder and ultimately completes the prediction task. Additionally, MFDnet utilizes the GRU-based Seq2Seq framework, which offers several advantages over other prediction ...
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. ...