On Differentiable Interpreters

Neural networks have transformed the fields of Machine Learning and Artificial Intelligence with the ability to model complex features and behaviours from raw data. They quickly became instrumental models, achieving numerous state-of-the-art performances across many tasks and domains. Yet the successes of these models often rely on large amounts of data. When data is scarce, resourceful ways of using background knowledge often help. However, though different types of background knowledge can be used to bias the model, it is not clear how one can use algorithmic knowledge to that extent. In this thesis, we present differentiable interpreters as an effective framework for utilising algorithmic background knowledge as architectural inductive biases of neural networks. By continuously approximating discrete elements of traditional program interpreters, we create differentiable interpreters that, due to the continuous nature of their execution, are amenable to optimisation with gradient descent methods. This enables us to write code mixed with parametric functions, where the code strongly biases the behaviour of the model while enabling the training of parameters and/or input representations from data. We investigate two such differentiable interpreters and their use cases in this thesis. First, we present a detailed construction of ∂4, a differentiable interpreter for the programming language FORTH. We demonstrate the ability of ∂4 to strongly bias neural models with incomplete programs of variable complexity while learning missing pieces of the program with parametrised neural networks. Such models can learn to solve tasks and strongly generalise to out-of-distribution data from small datasets. Second, we present greedy Neural Theorem Provers (gNTPs), a significant improvement of a differentiable Datalog interpreter NTP. gNTPs ameliorate the large computational cost of recursive differentiable interpretation, achieving drastic time and memory speedups while introducing soft reasoning over logic knowledge and natural language

Programming with a Differentiable Forth Interpreter

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.Comment: 34th International Conference on Machine Learning (ICML 2017

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret

Influence of design and construction on building durability and maintenance

Uzročnik nevaljalog stanja objekta i gradiva nije jedino starost objekta. Postoje drugi čimbenici odnosno pogreške koji uvjetuju nevaljalo stanje objekta i potrebu za dodatnim mjerama održavanja i poboljšanja. Ovi čimbenici izravno su povezani s načinom projektiranja i izvedbe odnosno pogreškama koje pri tome nastaju, a čiji uzročnici su sudionici u gradnji. Ove pogreške vidljive su u obliku pukotina, promjene boje i sastava gradiva te samog urušavanja, vrlo brzo nakon završetka objekta. U radu su prikazani primjeri izbora statičkog sustava objekta s ciljem umanjenja mogućnosti nastanka pogreške u završenom objektu. Dodatno, navedene su i objašnjene najčešće vrste istih te su dane smjernice za njihovo sprječavanjeThe time is not the only factor influencing the poor condition of the building and built in materials. There are other factors or flaws that condition the poor building state and the necessity for increased measures for maintenance and rehabilitation. Those factors are related to design and construction process of the building i. e. to flaws made in it, and they are caused by participants in construction. In a very short time after the building is constructed, those flaws are observed in the form of cracks, changed material structure and color, up to very collapse itself. In this paper an example is given about the proper selection of the structural system with aim to reduce the probability of flaws appearance in complete building. Additionally, the most common types of flaws are stated

A Strong Lexical Matching Method for the Machine Comprehension Test

Abstract Machine comprehension of text is the overarching goal of a great deal of research in natural language processing. The Machine Comprehension Tes

Jack the Reader - A Machine Reading Framework

Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (Jack), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. Jack is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse.Comment: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2018), System Demonstration

A Generalist Neural Algorithmic Learner

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner -- a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry. We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by "incorporating" knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art. We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models.Comment: 20 pages, 10 figure

Multiple object tracking via multiple hypothesis method

Ovaj rad prikazuje probleme i izazove praćenja više objekata te predstavlja tri glavne skupine metoda za praćenje više objekata. Prikazan je i rad Kalmanovog filtra koji se koristi u tim metodama za predstavljanje stanja objekata koji se prate i nji- hovo propagiranje u buduće vremenske korake. Evaluirane su sposobnosti jedne od tih metoda, praćenje više objekata metodom višestrukih hipoteza. Evaluirana je njena izvedenica, TOMHT, odnosno implemntacija TOMHT sustava u programskom jeziku Pythonu na generiranim i vanjskim podacima. Implementacija i postavke Kalmano- vog filtra su prilagod̄ene obradi podataka sa javnog izazova za praćenje više objekata MOTChallenge te su rezultati prikazani odgovarajućim metrikama.This thesis presents the problems and challenges of tracking multiple objects and introduces three main groups of methods for tracking multiple objects. The operation of the Kalman filter used in these methods for representing the states of the objects be- ing tracked and their propagation into future time steps is also presented. The capabili- ties of one of these methods, tracking multiple objects using the Multiple Hypotheses Tracking method, were evaluated. Its derivative, TOMHT, the implementation of the TOMHT system in the Python programming language on generated and external data was evaluated. The implementation and settings of the Kalman filter have been adap- ted for processing data from the public MOTChallenge, and the results are presented with the corresponding metrics