Wake-sleep Bayesian program synthesis applications in bioinformatics

Program synthesis is the process of learning mappings between sets of inputs and outputs in a way that generalizes to new inputs. Contrary to deep learning in the gradient descent sense, the goal of program induction is not to 'converge' to a correct solution by performing gradient descent on millions of parameters - rather it is to generate and search for discrete programs that are expressed as combinations of a library of known 'concepts' that will solve the given problem. The goal of this thesis is to explore the portability of program induction onto the bioinformatics domain – specifically the problem of tumor grade prediction. Programs enumerated to predict tumor grade from a data set of colon cancer were up to 76% accurate when the library of primitives was limited to arithmetic, exponential and logarithmic operations. Further work will involve building in models for solving differential equations (another success was to induce Dreamcoder to discover the forward Euler method for solving PDEs), as well as building conceptual representations of n-dimensional spatial data such as images. data such as images.Ope

Cooperative Multi-Agent Reinforcement Learning for Inventory Management

With Reinforcement Learning (RL) for inventory management (IM) being a nascent field of research, approaches tend to be limited to simple, linear environments with implementations that are minor modifications of off-the-shelf RL algorithms. Scaling these simplistic environments to a real-world supply chain comes with a few challenges such as: minimizing the computational requirements of the environment, specifying agent configurations that are representative of dynamics at real world stores and warehouses, and specifying a reward framework that encourages desirable behavior across the whole supply chain. In this work, we present a system with a custom GPU-parallelized environment that consists of one warehouse and multiple stores, a novel architecture for agent-environment dynamics incorporating enhanced state and action spaces, and a shared reward specification that seeks to optimize for a large retailer's supply chain needs. Each vertex in the supply chain graph is an independent agent that, based on its own inventory, able to place replenishment orders to the vertex upstream. The warehouse agent, aside from placing orders from the supplier, has the special property of also being able to constrain replenishment to stores downstream, which results in it learning an additional allocation sub-policy. We achieve a system that outperforms standard inventory control policies such as a base-stock policy and other RL-based specifications for 1 product, and lay out a future direction of work for multiple products.Comment: 14 pages, 5 figure

Hazardous Waste Removal In Chemical Industries

<p>This chapter has been published in Edited Book "Climate Change and Environment"</p><p>ISBN: 978-93-93166-53-1</p><p>For verification of this chapter, please visit on https://www.socialresearchfoundation.com/new/publish-book.php?editID=7542</p><p>Publisher : Social Research Foundation, Kanpur (SRF International)</p&gt