Learning to reason over visual objects

A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated by the goal of designing AI systems with this capacity, recent work has focused on evaluating whether neural networks can learn to solve RPM-like problems. Previous work has generally found that strong performance on these problems requires the incorporation of inductive biases that are specific to the RPM problem format, raising the question of whether such models might be more broadly useful. Here, we investigated the extent to which a general-purpose mechanism for processing visual scenes in terms of objects might help promote abstract visual reasoning. We found that a simple model, consisting only of an object-centric encoder and a transformer reasoning module, achieved state-of-the-art results on both of two challenging RPM-like benchmarks (PGM and I-RAVEN), as well as a novel benchmark with greater visual complexity (CLEVR-Matrices). These results suggest that an inductive bias for object-centric processing may be a key component of abstract visual reasoning, obviating the need for problem-specific inductive biases.Comment: ICLR 202

Ministry emphasises quality of medical training

One proposed mechanism of tumour escape from immune surveillance is tumour up-regulation of the cell surface ligan FasL, whichcan lead to apoptosis of Fas receptor (Fas) positive lymphocytes. Based upon this `coun-- rattack', we have developed a mathematical model inelAin tumour cell--lymphocyte ineA-- ction cell surface expression of Fas/FasL,an d their secreted soluble forms. The model predicts that (a) the production of soluble forms of Fas an d FasL will lead to thedown regulation of theimmun respon --fi (b) matrix metallopr otein se (MMP)ink'PTfiA ion should lead toin'x# sed membran FasLan result in a higher rate of Fas-mediated apoptosis for lymphocytesthan for tumour cells. Recen studieson can--# patient len support for theseprediction s. TheclinP-- l implication are two-fold. Firstly, the use of broad spectrum MMPin'#x tors asan`fi-- n`fi--'` cagenP may be compromised by their adverse e#ecton tumour FasL up-regulation Also, Fas/FasL insL action may havean impact on the outcome ofnA--x`#B onA in immunBB`fiAw`P-- ic trialssin` the finA common pathway of all these approaches is thetran - duction of deathsign-- swithin the tumour cell

Systematic Visual Reasoning through Object-Centric Relational Abstraction

Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays

Determinantal Point Process Attention Over Grid Codes Supports Out of Distribution Generalization

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization -- successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid-like representations (e.g., in entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over these grid representations using determinantal point process (DPP-A) -- a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in grid codes, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how grid codes in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.Comment: 24 pages (including Appendix), 19 figure

Lightning deaths in the UK: a 30-year analysis of the factors contributing to people being struck and killed

In the UK in the past 30 years (1987-2016), 58 people were known to have been killed by lightning, that is, on average, two people per year. The average annual risk of being struck and killed was one person in 33 million. If only the past ten years are considered, a period with fewer average lightning deaths, the risk was one person in 71 million. The likelihood of being killed by lightning is much less than it was a century ago when it was around one person in every two million per year. The current UK lightning risk is compared with USA risk. The risk of being killed by lightning in the UK differs by the activity being undertaken at the time. This paper groups activities into three broad types. During the past 30 years, work-related activities accounted for 15 per cent of all deaths, daily routine for 13 per cent, and outdoor leisure, recreation and sports pursuits for 72 per cent. Leisure walking on hills, mountains and cliff-tops together with participating in outdoor sports activities, notably cricket, fishing, football, golf, rugby and watersports, gave rise to around half of all leisure, recreation and sports activity deaths. The highest number of deaths occurred amongst the 20-29 year-age-range. Men accounted for 83 per cent of all lightning deaths reflecting the higher proportion of male participation in outdoor work-related activities and specific outdoor leisure activities (hill and mountain walking) and sports activities (cricket, fishing, football and golf). Sundays gave rise to 26 per cent of all deaths reflecting this is a day when large numbers of people participate in higher lightning risk leisure activities. The four months from May to August accounted for 80 per cent of all deaths. A specific study is conducted of the synoptic and weather situations during days when thunderstorms developed and resulted in deaths amongst people undertaking leisure walking activities. Overall, this paper highlights the factors that should help to lessen the risk of being killed by lightning in the future

Learning Representations that Support Extrapolation

Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, temporal context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.Comment: ICML 202

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain