Learning with Algebraic Invariances, and the Invariant Kernel Trick

When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances. This paper contributes by a novel framework for incorporating algebraic invariance structure into kernels. In particular, we show that algebraic properties such as sign symmetries in data, phase independence, scaling etc. can be included easily by essentially performing the kernel trick twice. We demonstrate the usefulness of our theory in simulations on selected applications such as sign-invariant spectral clustering and underdetermined ICA

Structure and dynamics in glass-formers: predictability at large length scales

Dynamic heterogeneity in glass-formers has been related to their static structure using the concept of dynamic propensity. We re-examine this relationship by analyzing dynamical fluctuations in two atomistic glass-formers and two theoretical models. We introduce quantitative statistical indicators which show that the dynamics of individual particles cannot be predicted on the basis of the propensity, nor by any structural indicator. However, the spatial structure of the propensity field does have predictive power for the spatial correlations associated with dynamic heterogeneity. Our results suggest that the quest for a connection between static and dynamic properties of glass-formers at the particle level is vain, but they demonstrate that such connection does exist on larger length scales.Comment: 7 pages; 4 figs - Extended, clarified versio

Im Rhein, im heiligen Strome (c1860)

Robert Franz Im Rhein, im heiligen Strome (c1860) text by Heinrich Heine, Poem 11 of Lyrisches Intermezzo (Buch der Lieder

Bayesian Learning of Sum-Product Networks

Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them are somewhat ad-hoc and based on intuition rather than a clear learning principle. In this paper, we introduce a well-principled Bayesian framework for SPN structure learning. First, we decompose the problem into i) laying out a computational graph, and ii) learning the so-called scope function over the graph. The first is rather unproblematic and akin to neural network architecture validation. The second represents the effective structure of the SPN and needs to respect the usual structural constraints in SPN, i.e. completeness and decomposability. While representing and learning the scope function is somewhat involved in general, in this paper, we propose a natural parametrisation for an important and widely used special case of SPNs. These structural parameters are incorporated into a Bayesian model, such that simultaneous structure and parameter learning is cast into monolithic Bayesian posterior inference. In various experiments, our Bayesian SPNs often improve test likelihoods over greedy SPN learners. Further, since the Bayesian framework protects against overfitting, we can evaluate hyper-parameters directly on the Bayesian model score, waiving the need for a separate validation set, which is especially beneficial in low data regimes. Bayesian SPNs can be applied to heterogeneous domains and can easily be extended to nonparametric formulations. Moreover, our Bayesian approach is the first, which consistently and robustly learns SPN structures under missing data.Comment: NeurIPS 2019; See conference page for supplemen

Corn stover densification using an auger compactor

Biomass densification is a necessary process for the bio-energy industry. Densities of 224 - 256.3 kg/m3 are needed to minimize transportation costs. Experiments on corn stover densification were completed using an auger compactor. The compaction pressure in the collection tube was varied to establish the pressure\u27s relationship with density and specific energy. Constrained densities of 171 - 343 kg/m3 and unconstrained densities of 131 - 235 kg/m3 were achieved using the system. A power relationship was established between the compaction pressure and the density. A liner relationship was established between the compaction pressure and the specific energy. The minimum bulk densities to optimize transportation fall within the attainable ranges and require less energy than high density processes such as pelletizing and briquetting. Auger compaction appears to be a promising low energy technology for commercial production of densified biomass