L0L_0-ARM: Network Sparsification via Stochastic Binary Optimization

We consider network sparsification as an $L_0$-norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attached with a stochastic binary gate, whose parameters are jointly optimized with original network parameters. The Augment-Reinforce-Merge (ARM), a recently proposed unbiased gradient estimator, is investigated for this binary optimization problem. Compared to the hard concrete gradient estimator from Louizos et al., ARM demonstrates superior performance of pruning network architectures while retaining almost the same accuracies of baseline methods. Similar to the hard concrete estimator, ARM also enables conditional computation during model training but with improved effectiveness due to the exact binary stochasticity. Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training. Extensive experiments on multiple public datasets demonstrate state-of-the-art pruning rates with almost the same accuracies of baseline methods. The resulting algorithm $L_0$-ARM sparsifies the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete estimator cannot. The code is public available at https://github.com/leo-yangli/l0-arm.Comment: Published as a conference paper at ECML 201

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that actor-critic algorithms are limited by their need for an on-policy critic. We propose Bootstrapped Dual Policy Iteration (BDPI), a novel model-free reinforcement-learning algorithm for continuous states and discrete actions, with an actor and several off-policy critics. Off-policy critics are compatible with experience replay, ensuring high sample-efficiency, without the need for off-policy corrections. The actor, by slowly imitating the average greedy policy of the critics, leads to high-quality and state-specific exploration, which we compare to Thompson sampling. Because the actor and critics are fully decoupled, BDPI is remarkably stable, and unusually robust to its hyper-parameters. BDPI is significantly more sample-efficient than Bootstrapped DQN, PPO, and ACKTR, on discrete, continuous and pixel-based tasks. Source code: https://github.com/vub-ai-lab/bdpi.Comment: Accepted at the European Conference on Machine Learning 2019 (ECML

Visual Rationalizations in Deep Reinforcement Learning for Atari Games

Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games. However, clearly explaining why a certain action is taken by the agent can be as important as the decision itself. Deep reinforcement learning models, as other deep learning models, tend to be opaque in their decision-making process. In this work, we propose to make deep reinforcement learning more transparent by visualizing the evidence on which the agent bases its decision. In this work, we emphasize the importance of producing a justification for an observed action, which could be applied to a black-box decision agent.Comment: presented as oral talk at BNAIC 201

An Application of Molecular Genotyping in Mice

Microsatellite markers are simple sequence repeats within the mammalian genome that can be used for identifying disease loci, mapping genes of interest as well as studying segregation patterns related to meiotic nondisjunction. Different strains of mice have variable CA repeat lengths and PCR based methods can be used to identify them, thus allowing for specific genotypes to be assigned. Molecular genotyping offers such identification at any developmental stage, which allows for a broad range of anomalies to be studied. We studied chromosomal segregation in relation to nondisjunction in early-gestation mouse embryos using molecular genotyping. Information on the parental origin as well as the number of chromosomes a given progeny carried was obtained in our analysis

Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control

In this work we introduce the application of black-box quantum control as an interesting rein- forcement learning problem to the machine learning community. We analyze the structure of the reinforcement learning problems arising in quantum physics and argue that agents parameterized by long short-term memory (LSTM) networks trained via stochastic policy gradients yield a general method to solving them. In this context we introduce a variant of the proximal policy optimization (PPO) algorithm called the memory proximal policy optimization (MPPO) which is based on this analysis. We then show how it can be applied to specific learning tasks and present results of nu- merical experiments showing that our method achieves state-of-the-art results for several learning tasks in quantum control with discrete and continouous control parameters

Dysregulation of DGCR6 and DGCR6L: psychopathological outcomes in chromosome 22q11.2 deletion syndrome

Chromosome 22q11.2 deletion syndrome (22q11DS) is the most common microdeletion syndrome in humans. It is typified by highly variable symptoms, which might be explained by epigenetic regulation of genes in the interval. Using computational algorithms, our laboratory previously predicted that DiGeorge critical region 6 (DGCR6), which lies within the deletion interval, is imprinted in humans. Expression and epigenetic regulation of this gene have not, however, been examined in 22q11DS subjects. The purpose of this study was to determine if the expression levels of DGCR6 and its duplicate copy DGCR6L in 22q11DS subjects are associated with the parent-of-origin of the deletion and childhood psychopathologies. Our investigation showed no evidence of parent-of-origin-related differences in expression of both DGCR6 and DGCR6L. However, we found that the variability in DGCR6 expression was significantly greater in 22q11DS children than in age and gender-matched control individuals. Children with 22q11DS who had anxiety disorders had significantly lower DGCR6 expression, especially in subjects with the deletion on the maternal chromosome, despite the lack of imprinting. Our findings indicate that epigenetic mechanisms other than imprinting contribute to the dysregulation of these genes and the associated childhood psychopathologies observed in individuals with 22q11DS. Further studies are now needed to test the usefulness of DGCR6 and DGCR6L expression and alterations in the epigenome at these loci in predicting childhood anxiety and associated adult-onset pathologies in 22q11DS subjects

Quality and Safety Aspects of Infant Nutrition

Quality and safety aspects of infant nutrition are of key importance for child health, but oftentimes they do not get much attention by health care professionals whose interest tends to focus on functional benefits of early nutrition. Unbalanced diets and harmful food components induce particularly high risks for untoward effects in infants because of their rapid growth, high nutrient needs, and their typical dependence on only one or few foods during the first months of life. The concepts, standards and practices that relate to infant food quality and safety were discussed at a scientific workshop organized by the Child Health Foundation and the Early Nutrition Academy jointly with the European Society for Paediatric Gastroenterology, Hepatology and Nutrition, and a summary is provided here. The participants reviewed past and current issues on quality and safety, the role of different stakeholders, and recommendations to avert future issues. It was concluded that a high level of quality and safety is currently achieved, but this is no reason for complacency. The food industry carries the primary responsibility for the safety and suitability of their products, including the quality of composition, raw materials and production processes. Introduction of new or modified products should be preceded by a thorough science based review of suitability and safety by an independent authority. Food safety events should be managed on an international basis. Global collaboration of food producers, food-safety authorities, paediatricians and scientists is needed to efficiently exchange information and to best protect public health. Copyright (C) 2012 S. Karger AG, Base

Transplantation of canine olfactory ensheathing cells producing chondroitinase ABC promotes chondroitin sulphate proteoglycan digestion and axonal sprouting following spinal cord injury

Olfactory ensheathing cell (OEC) transplantation is a promising strategy for treating spinal cord injury (SCI), as has been demonstrated in experimental SCI models and naturally occurring SCI in dogs. However, the presence of chondroitin sulphate proteoglycans within the extracellular matrix of the glial scar can inhibit efficient axonal repair and limit the therapeutic potential of OECs. Here we have used lentiviral vectors to genetically modify canine OECs to continuously deliver mammalian chondroitinase ABC at the lesion site in order to degrade the inhibitory chondroitin sulphate proteoglycans in a rodent model of spinal cord injury. We demonstrate that these chondroitinase producing canine OECs survived at 4 weeks following transplantation into the spinal cord lesion and effectively digested chondroitin sulphate proteoglycans at the site of injury. There was evidence of sprouting within the corticospinal tract rostral to the lesion and an increase in the number of corticospinal axons caudal to the lesion, suggestive of axonal regeneration. Our results indicate that delivery of the chondroitinase enzyme can be achieved with the genetically modified OECs to increase axon growth following SCI. The combination of these two promising approaches is a potential strategy for promoting neural regeneration following SCI in veterinary practice and human patients