Related papers: Proceedings of NIPS 2017 Symposium on Interpretabl…

These proceedings include full papers and research-in-progress papers presented at the ACIS 2015 Conference in Adelaide, Australia from November 30th to December 4th, 2015.

This volume contains the post-proceedings of the 14th International Workshop on Quantitative Aspects of Programming Languages and Systems (QAPL), held as a satellite workshop of ETAPS 2016 in Eindhoven, The Netherlands, on 2-3 April 2016.

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. \cite{hermann2015teaching} therefore…

This is the Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, which was held in Seattle, WA, July 10-12, 1987

Here we present the results of the NSF-funded Workshop on Computational Topology, which met on June 11 and 12 in Miami Beach, Florida. This report identifies important problems involving both computation and topology.

A non-parametric interpretable texture synthesis method, called the NITES method, is proposed in this work. Although automatic synthesis of visually pleasant texture can be achieved by deep neural networks nowadays, the associated…

In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a…

A growing number of approaches exist to generate explanations for image classification. However, few of these approaches are subjected to human-subject evaluations, partly because it is challenging to design controlled experiments with…

How can we find interpretable, domain-appropriate models of natural phenomena given some complex, raw data such as images? Can we use such models to derive scientific insight from the data? In this paper, we propose some methods for…

Proceedings of the Workshop on High Performance Energy Efficient Embedded Systems (HIP3ES) 2016. Prague, January 18th. Collocated with HIPEAC 2016 Conference.

Learning from negative samples holds great promise for improving Large Language Model (LLM) reasoning capability, yet existing methods treat all incorrect responses as equally informative, overlooking the crucial role of sample quality. To…

The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, machine learning could replace human modeler and shift the focus of human…

This volume contains the joint proceedings of IMPEX 2017, the first workshop on Handling IMPlicit and EXplicit knowledge in formal system development and FM&MDD, the second workshop on Formal and Model-Driven Techniques for Developing…

Despite the great potential of large language models(LLMs) in machine comprehension, it is still disturbing to fully count on them in real-world scenarios. This is probably because there is no rational explanation for whether the…

This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high…

Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in modern models. Advances in interpretability techniques such as…

Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we…

We are proud to present the papers from the 17th Refinement Workshop, co-located with FM 2015 held in Oslo, Norway on June 22nd, 2015. Refinement is one of the cornerstones of a formal approach to software engineering: the process of…

Learning Progressions (LPs) can help adjust instruction to individual learners needs if the LPs reflect diverse ways of thinking about a construct being measured, and if the LP-aligned assessments meaningfully measure this diversity. The…

This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs…