EvoJAX: Hardware-Accelerated Neuroevolution

Tang, Yujin; Tian, Yingtao; Ha, David

doi:10.48550/arxiv.2202.05008

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Over the last few years, JAX (Bradbury et al, 2018) has seen increasing adoption by the research community Heek et al, 2020;Ro et al, 2021;Tang et al, 2022). The key difference between JAX and other popular deep learning frameworks like PyTorch and Tensorflow is the clear separation between functions and state.…”

Section: Why a New Sparsity Library In Jax?mentioning

confidence: 99%

Tcf7l2 in hepatocytes regulates de novo lipogenesis in diet-induced non-alcoholic fatty liver disease in mice

Lee

Kim

et al. 2023

Diabetologia

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Aims/hypothesis Non-alcoholic fatty liver disease (NAFLD) associated with type 2 diabetes may more easily progress towards severe forms of non-alcoholic steatohepatitis (NASH) and cirrhosis. Although the Wnt effector transcription factor 7-like 2 (TCF7L2) is closely associated with type 2 diabetes risk, the role of TCF7L2 in NAFLD development remains unclear. Here, we investigated how changes in TCF7L2 expression in the liver affects hepatic lipid metabolism based on the major risk factors of NAFLD development. Methods Tcf7l2 was selectively ablated in the liver of C57BL/6N mice by inducing the albumin (Alb) promoter to recombine Tcf7l2 alleles floxed at exon 5 (liver-specific Tcf7l2-knockout [KO] mice: Alb-Cre;Tcf7l2f/f). Alb-Cre;Tcf7l2f/f and their wild-type (Tcf7l2f/f) littermates were fed a high-fat diet (HFD) or a high-carbohydrate diet (HCD) for 22 weeks to reproduce NAFLD/NASH. Mice were refed a standard chow diet or an HCD to stimulate de novo lipogenesis (DNL) or fed an HFD to provide exogenous fatty acids. We analysed glucose and insulin sensitivity, metabolic respiration, mRNA expression profiles, hepatic triglyceride (TG), hepatic DNL, selected hepatic metabolites, selected plasma metabolites and liver histology. Results Alb-Cre;Tcf7l2f/f essentially exhibited increased lipogenic genes, but there were no changes in hepatic lipid content in mice fed a normal chow diet. However, following 22 weeks of diet-induced NAFLD/NASH conditions, liver steatosis was exacerbated owing to preferential metabolism of carbohydrate over fat. Indeed, hepatic Tcf7l2 deficiency enhanced liver lipid content in a manner that was dependent on the duration and amount of exposure to carbohydrates, owing to cell-autonomous increases in hepatic DNL. Mechanistically, TCF7L2 regulated the transcriptional activity of Mlxipl (also known as ChREBP) by modulating O-GlcNAcylation and protein content of carbohydrate response element binding protein (ChREBP), and targeted Srebf1 (also called SREBP1) via miRNA (miR)-33-5p in hepatocytes. Eventually, restoring TCF7L2 expression at the physiological level in the liver of Alb-Cre;Tcf7l2f/f mice alleviated liver steatosis without altering body composition under both acute and chronic HCD conditions. Conclusions/interpretation In mice, loss of hepatic Tcf7l2 contributes to liver steatosis by inducing preferential metabolism of carbohydrates via DNL activation. Therefore, TCF7L2 could be a promising regulator of the NAFLD associated with high-carbohydrate diets and diabetes since TCF7L2 deficiency may lead to development of NAFLD by promoting utilisation of excess glucose pools through activating DNL. Data availability RNA-sequencing data have been deposited into the NCBI GEO under the accession number GSE162449 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162449). Graphical abstract

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Section: Why a New Sparsity Library In Jax?mentioning

confidence: 99%

Tcf7l2 in hepatocytes regulates de novo lipogenesis in diet-induced non-alcoholic fatty liver disease in mice

Lee

Kim

et al. 2023

Diabetologia

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“…Recent advances in hardware acceleration have led to new QD libraries such as QDax [20] or EvoJax [21]. These tools rely on highly-parallelised simulators like Brax [22] that can run on accelerators (e.g., GPUs and TPUs) and thus target simulated domains, for example, robotics control, where they drastically reduce the evaluation time.…”

Section: B Hardware-accelerated Quality-diversitymentioning

confidence: 99%

“…Nonetheless, recent advances in computer systems enable the high-parallelisation of evaluations. Recent libraries such as QDax [20], or EvoJax [21] based on the Brax simulator [22] allowed to speedup computation by a large order of magnitude thanks to the high-parallelisation of evaluations. With such tools, we now have access to 10 or 100 times more evaluations per generation within the same amount of time.…”

Section: Introductionmentioning

confidence: 99%

Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains

Flageat

Chalumeau

Cully

2023

ACM Trans. Evol. Learn. Optim.

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Quality-Diversity algorithms, among which MAP-Elites, have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of diverse and high-performing solutions to an optimisation problem. However, they are often limited to low-dimensional search spaces and deterministic environments. The recently introduced Policy Gradient Assisted MAP-Elites (PGA-MAP-Elites) algorithm overcomes this limitation by pairing the traditional Genetic operator of MAP-Elites with a gradient-based operator inspired by Deep Reinforcement Learning. This new operator guides mutations toward high-performing solutions using policy-gradients. In this work, we propose an in-depth study of PGA-MAP-Elites. We demonstrate the benefits of policy-gradients on the performance of the algorithm and the reproducibility of the generated solutions when considering uncertain domains. We first prove that PGA-MAP-Elites is highly performant in both deterministic and uncertain high-dimensional environments, decorrelating the two challenges it tackles. Secondly, we show that in addition to outperforming all the considered baselines, the collections of solutions generated by PGA-MAP-Elites are highly reproducible in uncertain environments, approaching the reproducibility of solutions found by Quality-Diversity approaches built specifically for uncertain applications. Finally, we propose an ablation and in-depth analysis of the dynamic of the policy-gradients-based variation. We demonstrate that the policy-gradient variation operator is determinant to guarantee the performance of PGA-MAP-Elites but is only essential during the early stage of the process, where it finds high-performing regions of the search space.

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“…So unlike traditionally training a neural network to perform one task, where the weight parameters of neural networks are traditionally optimized with a gradient descent algorithm, or with evolution strategies (Tang et al 2022), the goal of meta-learning is to train a meta-learner (which can be another neural network-based system) to learn a learning algorithm. This is a particularly challenging task, with a long history see Schmidhuber (Schmidhuber, 2020) for a review).…”

Section: Meta-learningmentioning

confidence: 99%

Collective intelligence for deep learning: A survey of recent developments

Tang

2022

Collective Intelligence

Self Cite

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In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods. In this review, we will provide a historical context of neural network research’s involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.

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EvoJAX: Hardware-Accelerated Neuroevolution

Cited by 4 publications

References 18 publications

Tcf7l2 in hepatocytes regulates de novo lipogenesis in diet-induced non-alcoholic fatty liver disease in mice

Tcf7l2 in hepatocytes regulates de novo lipogenesis in diet-induced non-alcoholic fatty liver disease in mice

Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains

Collective intelligence for deep learning: A survey of recent developments

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