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2014


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Advanced Structured Prediction

Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H.

Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

Abstract
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

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publisher link (url) [BibTex]

2014


publisher link (url) [BibTex]


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

ei

DOI [BibTex]

DOI [BibTex]


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Exploring complex diseases with intelligent systems

Borgwardt, K.

2014 (mpi_year_book)

Abstract
Physicians are collecting an ever increasing amount of data describing the health state of their patients. Is new knowledge about diseases hidden in this data, which could lead to better therapies? The field of Machine Learning in Biomedicine is concerned with the development of approaches which help to gain such insights from massive biomedical data.

link (url) [BibTex]


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The cellular life-death decision – how mitochondrial membrane proteins can determine cell fate

García-Sáez, Ana J.

2014 (mpi_year_book)

Abstract
Living organisms have a very effective method for eliminating cells that are no longer needed: programmed death. Researchers in the group of Ana García Sáez work with a protein called Bax, a key regulator of apoptosis that creates pores with a flexible diameter inside the outer mitochondrial membrane. This step inevitably triggers the final death of the cell. These insights into the role of important key enzymes in setting off apoptosis could provide useful for developing drugs that can directly influence apoptosis.

link (url) [BibTex]

2011


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Optimization for Machine Learning

Sra, S., Nowozin, S., Wright, S.

pages: 494, Neural information processing series, MIT Press, Cambridge, MA, USA, December 2011 (book)

Abstract
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

ei

Web [BibTex]

2011


Web [BibTex]


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Bayesian Time Series Models

Barber, D., Cemgil, A., Chiappa, S.

pages: 432, Cambridge University Press, Cambridge, UK, August 2011 (book)

ei

[BibTex]

[BibTex]


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Handbook of Statistical Bioinformatics

Lu, H., Schölkopf, B., Zhao, H.

pages: 627, Springer Handbooks of Computational Statistics, Springer, Berlin, Germany, 2011 (book)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Preparation of high-efficiency nanostructures of crystalline silicon at low temperatures, as catalyzed by metals: The decisive role of interface thermodynamics

Wang, Zumin, Jeurgens, Lars P. H., Mittemeijer, Eric J.

2011 (mpi_year_book)

Abstract
Metals may help to convert semiconductors from a disordered (amorphous) to an ordered (crystalline) form at low temperatures. A general, quantitative model description has been developed on the basis of interface thermodynamics, which provides fundamental understanding of such so-called metal-induced crystallization (MIC) of amorphous semiconductors. This fundamental understanding can allow the low-temperature (< 200 ºC) manufacturing of high-efficiency solar cells and crystalline-Si-based nanostructures on cheap and flexible substrates such as glasses, plastics and possibly even papers.

link (url) [BibTex]


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The sweet coat of living cells – from supramolecular organization and dynamics to biological function

Richter, Ralf

2011 (mpi_year_book)

Abstract
Many biological cells endow themselves with a sugar-rich coat that plays a key role in the protection of the cell and in structuring and communicating with its environment. An outstanding property of these pericellular coats is their dynamic self-organization into strongly hydrated and gel-like meshworks. Tailor-made model systems that are constructed from the molecular building blocks of pericellular coats can help to understand how the coats function.

link (url) [BibTex]