Mechanistic Mode Connectivity

We study neural network loss landscapes through the lens of mode connectivity, the observation that minimizers of neural networks retrieved via training on a dataset are connected via simple paths of low loss. Specifically, we ask the following question: are minimizers that rely on different mechanisms for making their predictions connected via simple paths of low loss? We provide a definition of mechanistic similarity as shared invariances to input transformations and demonstrate that lack of linear connectivity between two models implies they use dissimilar mechanisms for making their predictions. Relevant to practice, this result helps us demonstrate that naive fine-tuning on a downstream dataset can fail to alter a model's mechanisms, e.g., fine-tuning can fail to eliminate a model's reliance on spurious attributes. Our analysis also motivates a method for targeted alteration of a model's mechanisms, named connectivity-based fine-tuning (CBFT), which we analyze using several synthetic datasets for the task of reducing a model's reliance on spurious attributes.Comment: Accepted at ICML, 202

Explaining cognitive behaviour : a neurocomputational perspective

While the search for models and explanations of cognitive phenomena is a growing area of research, there is no consensus on what counts as a good explanation in cognitive science. This Ph.D. thesis offers a philosophical exploration of the different frameworks adopted to explain cognitive behaviour. It then builds on this systematic exploration to offer a new understanding of the explanatory standards employed in the construction and justification of models and modelling frameworks in cognitive science. Sub-goals of the project include a better understanding of some theoretical terms adopted in cognitive science and a deep analysis of the role of representation in explanations of cognitive phenomena. Results of this project can advance the debate on issues in general philosophy of cognitive science and be valuable for guiding future scientific and cognitive research. In particular, the goals of the thesis are twofold: (i) to provide some necessary desiderata that genuine explanations in cognitive science need to meet; (ii) to identify the framework that is most apt to generate such good explanations. With reference to the first goal, I claim that a good explanation needs to provide predictions and descriptions of mechanisms. With regards to the second goal, I argue that the neurocomputational framework can meet these two desiderata. In order to articulate the first claim, I discuss various possible desiderata of good explanations and I motivate why the ability to predict and to identify mechanisms are necessary features of good explanations in cognitive science. In particular, I claim that a good explanation should advance our understanding of the cognitive phenomenon under study, together with providing a clear specification of the components and their interactions that regularly bring the phenomenon about. I motivate the second claim by examining various frameworks employed to explain cognitive phenomena: the folk-psychological, the anti-representational, the solely subpersonal and the neurocomputational frameworks. I criticise the folk-psychological framework for meeting only the predictive criterion and I stress the inadequacy of its account of cause and causal explanation by engaging with James Woodward’s manipulationist theory of causation and causal explanation. By examining the anti-representational framework, I claim that the notion of representation is necessary to predict and to generalise cognitive phenomena. I reach the same conclusion by engaging with William Ramsey (2007) and Jose Luis Bermudez (2003). I then analyse the solely subpersonal framework and I argue that certain personal-level concepts are indeed required to successfully explain cognitive behaviour. Finally, I introduce the neurocomputational framework as more promising than the alternatives in explaining cognitive behaviour. I support this claim by assessing the framework’s ability to: (i) meet the two necessary criteria for good explanations; (ii) overcome some of the other frameworks’ explanatory limits. In particular, via an analysis of one of its family of models — Bayesian models — I argue that the neurocomputational framework can suggest a more adequate notion of representation, shed new light on the problem of how to bridge personal and subpersonal explanations, successfully meet the prediction criterion (it values predictions as a means to evaluate the goodness of an explanation) and can meet the mechanistic criterion (its model-based methodology opens up the possibility to study the nature of internal and unobservable components of cognitive phenomena)

Interpretable machine learning for genomics

High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines