Abstract: Bees have a diverse instinctual repertoire that exceeds in complexity that of most vertebrates. This repertoire allows the social organisation of such feats as the construction of precisely hexagonal honeycombs, an exact climate control system inside their home, the provision of the hive with commodities that must be harvested over a large territory (nectar,
Abstract: Some of my past and current research looks at "decisions from experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information,
Estimates from large-scale replication projects in psychology suggest that the majority of studies from top journals do not replicate. Using commonly accepted research methods, several academic fields amassed prolific, seemingly coherent literatures on phenomena that do not exist, such as extrasensory perception and depression candidate genes. Throughout the biomedical and life sciences, data detectives keep finding highly cited
What's on a mouse's mind? Behavioral measures to understand experiences and needs of an animal Lars Lewejohann, Freie Universität Berlin, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R) Mice, as all other living creatures, have adapted to specific living conditions in the course of evolution. From a
Abstract: The fields of neuroscience and AI have a long and intertwined history. From the study of simple and complex cells in visual areas of the brain to the recent success of convolution neural networks in many real-world applications, experimental and theoretical neuroscience has contributed significantly to designing smarter machines. In turn, AI models help us better understand
"Harnessing machine learning to model biological systems" Abstract: Classically, models of biological systems follow two different approaches. In bottom-up approaches, biological data are used to constrain a phenomenological model of the system in question, and the model is the studied to identify potential functions or potential consequences of the observations that flow into the model.
In this presentation, I will report on the results of my work so far on the concept of intelligence, summarising some of the main points and proposals made, and opening the floor for open discussion about the topic. The Zoom Link will be sent the day before the lecture.
Abstract: Self-supervised learning (SSL) consists in training neural network systems without using any human annotations. Typically, neural networks require large amounts of annotated data, which have limited their applications in fields where accessing these annotations is expensive or difficult. Moreover, manual annotations are biased towards a specific task and towards the annotator's own biases, which
Abstract Machine learning models have achieved stunning successes in the IID setting. Yet, beyond this setting, existing models still suffer from two grand challenges: brittle under covariate shift and inefficient for knowledge transfer. In this talk, I will introduce three approaches to tackle these challenges, namely self-supervised learning, causal representation learning, and test-time training. More
One fundamental difference between human and non-human animals is the ability of humans to instantaneously implement instructed behaviour. While other animals acquire new behaviour via effortful trial-and-error learning or extensive practice, humans can implement novel behaviour based on instructions. This ability is presumably a key aspect of cultural learning. In my talk, I will discuss
Abstract: What is perception? The most intuitive and influential answer to this question has long been the one given by David Marr: To see the world is “to know what is where by looking” - to transform light into representations of objects and their features, located somewhere ins pace. But is this all that perception
Abstract: The brain performs critical calculations on visual information as we swiftly, yet effortlessly, navigate around objects and obstacles in our cluttered environment. Perhaps one of the most important calculations is for the perception of depth using the apparent relative motion of objects in the environment created by our own translation known as motion parallax.
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