- 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)

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- Read more about Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks
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- Read more about Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients
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Segmenting a document image into text-lines and words finds applications in many research areas of DIA(Document Image Analysis) such as OCR, Word Spotting, and document retrieval. However, carrying out segmentation operation directly in the compressed document images is still an unexplored and challenging research area. Since JPEG is most widely accepted compression algorithm, this research paper attempts to segment a JPEG compressed printed text document image into text-lines and words, without fully decompressing the image.

## dccv.pdf

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- Read more about Improved Subspace K-Means Performance via a Randomized Matrix Decomposition
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Subspace clustering algorithms provide the capability

to project a dataset onto bases that facilitate clustering.

Proposed in 2017, the subspace k-means algorithm simultaneously

performs clustering and dimensionality reduction with the goal

of finding the optimal subspace for the cluster structure; this

is accomplished by incorporating a trade-off between cluster

and noise subspaces in the objective function. In this study,

we improve subspace k-means by estimating a critical transformation

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- Read more about Poster: Generative-Discriminative Crop Type Identification using Satellite Images
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Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images are good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop phenology, multi-temporal images are stacked to extract the growth pattern of varied crops.

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- Read more about A deep network for single-snapshot direction of arrival estimation
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This paper examines a deep feedforward network for beamforming with the single--snapshot Sample Covariance Matrix (SCM). The Conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed--forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example.

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- Read more about Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks
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We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized.

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- Read more about DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS
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- Read more about Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis
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Semi-continuous data have a point mass at zero and are continuous with positive support. Such data arise naturally in several real-life situations like signals in a blind source separation problem, daily rainfall at a location, sales of durable goods among many others. Therefore, efficient estimation of the underlying probability density function is of significant interest.

## MLSP_2019.pdf

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- Read more about VISUALIZING HIGH DIMENSIONAL DYNAMICAL PROCESSES
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