- Bayesian learning; Bayesian signal processing (MLR-BAYL)
- Bounds on performance (MLR-PERF)
- Applications in Systems Biology (MLR-SYSB)
- Applications in Music and Audio Processing (MLR-MUSI)
- Applications in Data Fusion (MLR-FUSI)
- Cognitive information processing (MLR-COGP)
- Distributed and Cooperative Learning (MLR-DIST)
- Learning theory and algorithms (MLR-LEAR)
- Neural network learning (MLR-NNLR)
- Information-theoretic learning (MLR-INFO)
- Independent component analysis (MLR-ICAN)
- Graphical and kernel methods (MLR-GRKN)
- Other applications of machine learning (MLR-APPL)
- Pattern recognition and classification (MLR-PATT)
- Source separation (MLR-SSEP)
- Sequential learning; sequential decision methods (MLR-SLER)
Sparse Modeling in Image Processing and Deep LearningSparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.
Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a regression problem. Incorporating SQD-LSTM can circumvent the typical CNNs' inherent difficulty of learning global spatial dependencies which are vital when recovering the true phase. Furthermore, we employ a problem specific composite loss function to train this network.
Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a generative process by learning a latent representation of the input. In this paper, we propose a novel VAE defined in the quaternion domain, which exploits the properties of quaternion algebra to improve performance while significantly reducing the number of parameters required by the network.
Saliency-driven image and video coding for humans has gained importance in the recent past. In this paper, we propose such a saliency-driven coding framework for the video coding for machines task using the latest video coding standard Versatile Video Coding (VVC). To determine the salient regions before encoding, we employ the real-time-capable object detection network You Only Look Once (YOLO) in combination with a novel decision criterion. To measure the coding quality for a machine, the state-of-the-art object segmentation network Mask R-CNN was applied to the decoded frame.
In this paper, we consider the problem of Federated Learning (FL) under non-i.i.d data setting. We provide an improved estimate of the empirical loss at each node by using a weighted average of losses across nodes with a penalty term. These uneven weights to different nodes are assigned by taking a novel Bayesian approach to the problem where the problem of learning for each device/node is cast as maximizing the likelihood of a joint distribution. This joint distribution is for losses of nodes obtained by using data across devices for a given neural network of a node.
Intelligent traffic signal control is crucial for efficient
transportation systems. Recent studies use reinforcement
learning (RL) to coordinate traffic signals and improve traffic
signal cooperation. However, they either design the state of
agents in a heuristic manner or model traffic dynamics in a deterministic way. This work presents a variational graph learning model TSC-GNN (Traffic Signal Control via probabilistic
Graph Neural Networks) to learn the latent representations of
agents and generate Q-value while taking traffic uncertainty