6,995 research outputs found
Bio-inspired analysis of deep learning on not-so-big data using data-prototypes
International audienceDeep artificial neural networks are feed-forward architectures capable of very impressive performances in diverse domains. Indeed stacking multiple layers allows a hierarchical composition of local functions, providing efficient compact mappings. Compared to the brain, however, such architectures are closer to a single pipeline and require huge amounts of data, while concrete cases for either human or machine learning systems are often restricted to not-so-big data sets.Furthermore, interpretability of the obtained results is a key issue: since deep learning applications are increasingly present in society,it is important that the underlying processes be accessible and understandable to every one.In order to target these challenges, in this contribution we analyze how considering prototypes in a rather generalized sense (with respect to the state of the art)allows to reasonably work with small data sets while providing an interpretable view of the obtained results.Some mathematical interpretation of this proposal is discussed.Sensitivity to hyperparameters is a key issue for reproducible deep learning results, and is carefully considered in our methodology.Performances and limitations of the proposed setup are explored in details, under different hyperparameters sets, in an analogous way as biological experiments are conducted.We obtain a rather simple architecture, easy to explain, and which allows, combined with a standard method, to target both performances and interpretability
Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems
In this paper I will present an analysis of the impact that the notion of “bounded rationality”,
introduced by Herbert Simon in his book “Administrative Behavior”, produced in the
field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated
Decision Making (ADM), I will show how the introduction of the cognitive dimension into
the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the
development of a line of research aiming at the realisation of artificial systems whose decisions
are based on the adoption of powerful shortcut strategies (known as heuristics) based
on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the
“heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial
complexity in real-life situations and still represents an important strategy for the design
and implementation of intelligent systems
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Approximate and Situated Causality in Deep Learning
Altres ajuts: ICREA Academia 2019, and "AppPhil: Applied Philosophy for the Value-Design of Social Networks Apps" project, funded by Caixabank in Recercaixa2017.Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality
How to Place Your Apps in the Fog -- State of the Art and Open Challenges
Fog computing aims at extending the Cloud towards the IoT so to achieve
improved QoS and to empower latency-sensitive and bandwidth-hungry
applications. The Fog calls for novel models and algorithms to distribute
multi-service applications in such a way that data processing occurs wherever
it is best-placed, based on both functional and non-functional requirements.
This survey reviews the existing methodologies to solve the application
placement problem in the Fog, while pursuing three main objectives. First, it
offers a comprehensive overview on the currently employed algorithms, on the
availability of open-source prototypes, and on the size of test use cases.
Second, it classifies the literature based on the application and Fog
infrastructure characteristics that are captured by available models, with a
focus on the considered constraints and the optimised metrics. Finally, it
identifies some open challenges in application placement in the Fog
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