Adaptive Neural Compilation

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.Comment: Submitted to NIPS 2016, code and supplementary materials will be available on author's pag

Efficient Linear Programming for Dense CRFs

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels; and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs.Comment: 24 pages, 10 figures and 4 table

Evaluation of the usefulness of carcass-weight, meat-percentage or identity of pig-producer in future-risk-based meat inspection

In the search for new and risk-based ways of conducting meat inspection, a pilot study was conducted with the amin of investigating whether carcass weight in combination with meat percentage, or producer-identity could be used as indicators for rejection of finisher pig carcasses

Ranking of food safety risks in pork from organic and free-range production systems

The objectives of this semi-quantitative risk assessment were to identify, assess and rank food safety risks in outdoor pig production (organic and free-range) compared to indoor pig production (conventional) in Denmark. In addition, high-risk pork products would be identified. Finally, risk-reducing strategies for handling the identified agents would be suggested. Data were obtained from the literature as well as in-house statistics. Data describing tetracycline-resistant E. coli in outdoor pigs were available from the Qualysafe project. The OIE framework for risk assessment was applied

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a labelling that captures finer detail than their sparse counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of the solution. A question naturally arises as to whether it is possible to obtain a maximum a posteriori (MAP) estimate of a dense CRF using a principled method. Within this paper, we show that this is indeed possible. We will show that, by using a filter-based method, continuous relaxations of the MAP problem can be optimised efficiently using state-of-the-art algorithms. Specifically, we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher-order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The presented algorithms can be applied to any labelling problem using a dense CRF with sparse higher-order potentials. In this paper, we use semantic segmentation as an example application as it demonstrates the ability of the algorithm to scale to dense CRFs with large dimensions. We perform experiments on the Pascal dataset to indicate that the presented algorithms are able to attain lower energies than the mean-field inference method

Microbiological criteria - Danish experience with use of the food safety criteria on minced meat and meat preparations

The recently introduced EU Commission regulation 2073/2005 on microbiological criteria for foodstuffs sets food safety criteria on Salmonella in minced meat and meat preparations. Products intended to be eaten cooked are to be sampled weekly by five samples of 10g each. If Salmonella is found and the product is on the market, a recall will take place. Data from several EU countries in 2005 show a Salmonella prevalence varying from 0-8% in minced pork and 0-4% in minced beef. In Denmark, a total of 32 recalls were performed in 2006. This is costly, and it is questionably whether it has any impact on food safety, since the meat is supposed to be heat-treated prior to consumption

Assessment of human risk for yersinosis after consumption of Danish produced non-heat-treated ready-to-eat pork products

The objectives were to evaluate the risk of obtaining an infective dose of Yersinia enterocolitica (Y. enterocolitica) after consuming fermented sausages (made in a controlled process) and smoked filet made of Danish pork. For fermented sausages it was estimated that a maximum of two bacteria would be present in a serving of up to 40 g. However, most likely only one bacterium would be present per serving (4,000 times of one million simulations of 40 g serving’s = 0.4 %)