2,790 research outputs found
A critical examination of compound stability predictions from machine-learned formation energies
Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests
Feature Reinforcement Learning: Part I: Unstructured MDPs
General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the MDP framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into one learning algorithm.
Extensions to more realistic dynamic Bayesian networks are developed in Part
II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram
Meta-Learning for Symbolic Hyperparameter Defaults
Hyperparameter optimization in machine learning (ML) deals with the problem
of empirically learning an optimal algorithm configuration from data, usually
formulated as a black-box optimization problem. In this work, we propose a
zero-shot method to meta-learn symbolic default hyperparameter configurations
that are expressed in terms of the properties of the dataset. This enables a
much faster, but still data-dependent, configuration of the ML algorithm,
compared to standard hyperparameter optimization approaches. In the past,
symbolic and static default values have usually been obtained as hand-crafted
heuristics. We propose an approach of learning such symbolic configurations as
formulas of dataset properties from a large set of prior evaluations on
multiple datasets by optimizing over a grammar of expressions using an
evolutionary algorithm. We evaluate our method on surrogate empirical
performance models as well as on real data across 6 ML algorithms on more than
100 datasets and demonstrate that our method indeed finds viable symbolic
defaults.Comment: Pieter Gijsbers and Florian Pfisterer contributed equally to the
paper. V1: Two page GECCO poster paper accepted at GECCO 2021. V2: The
original full length paper (8 pages) with appendi
Feature Dynamic Bayesian Networks
Feature Markov Decision Processes (PhiMDPs) are well-suited for learning
agents in general environments. Nevertheless, unstructured (Phi)MDPs are
limited to relatively simple environments. Structured MDPs like Dynamic
Bayesian Networks (DBNs) are used for large-scale real-world problems. In this
article I extend PhiMDP to PhiDBN. The primary contribution is to derive a cost
criterion that allows to automatically extract the most relevant features from
the environment, leading to the "best" DBN representation. I discuss all
building blocks required for a complete general learning algorithm.Comment: 7 page
On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms
In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate.
This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests
Classification and Scoring of Protein Complexes
Proteins interactions mediate all biological systems in a cell; understanding their interactions
means understanding the processes responsible for human life. Their structure can
be obtained experimentally, but such processes frequently fail at determining structures
of protein complexes. To address the issue, computational methods have been developed
that attempt to predict the structure of a protein complex, using information of its constituents.
These methods, known as docking, generate thousands of possible poses for
each complex, and require effective and reliable ways to quickly discriminate the correct
pose among the set of incorrect ones. In this thesis, a new scoring function was developed
that uses machine learning techniques and features extracted from the structure of the
interacting proteins, to correctly classify and rank the putative poses. The developed
function has shown to be competitive with current state-of-the-art solutions
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