Computational Engineering Division Student Intern
Multiple Lecturer/Senior Lecturer (equivalent to U.S. Assistant Professor tenure track) positions in machine learning and computer vision at the University of Melbourne’s School of Computing and Information Systems.
MS/PhD/Visiting scholar positions for Deep RL Ford-WVU
PhD Positions for Robot Learning Uni Freiburg
2 PhD positions available – University College Cork (UCC)
Machine Learning Researcher, UK
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
RL/LfD research positions (including interns) at Bosch / UT Austin, focusing on autonomous vehicles
#Job
🔭 @DeepGravity
Multiple Lecturer/Senior Lecturer (equivalent to U.S. Assistant Professor tenure track) positions in machine learning and computer vision at the University of Melbourne’s School of Computing and Information Systems.
MS/PhD/Visiting scholar positions for Deep RL Ford-WVU
PhD Positions for Robot Learning Uni Freiburg
2 PhD positions available – University College Cork (UCC)
Machine Learning Researcher, UK
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
RL/LfD research positions (including interns) at Bosch / UT Austin, focusing on autonomous vehicles
#Job
🔭 @DeepGravity
Introducing neuromodulation in deep neural networks to learn adaptive behaviours
Abstract
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation, the biological mechanism that dynamically controls intrinsic properties of neurons and their response to external stimuli in a context-dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-reinforcement learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.
Paper
🔭 @DeepGravity
Abstract
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation, the biological mechanism that dynamically controls intrinsic properties of neurons and their response to external stimuli in a context-dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-reinforcement learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.
Paper
🔭 @DeepGravity
journals.plos.org
Introducing neuromodulation in deep neural networks to learn adaptive behaviours
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an…
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise
Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.
Paper
🔭 @DeepGravity
Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.
Paper
🔭 @DeepGravity
Nature
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise
Nature Machine Intelligence - Applications of machine learning in the life sciences and medicine require expertise in computational methods and in scientific subject matter. The authors surveyed...