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Found 12 papers, 7 papers with code
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (ANN) solutions, particularly locality-sensitive hashing.
Context Meta-Reinforcement Learning via Neuromodulation
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments.
Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks
A wide range of techniques can be considered for segmentation of images of nanostructured surfaces.
Fast and automated biomarker detection in breath samples with machine learning
Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics.
Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations
Deep neural networks suffer from the inability to preserve the learned data representation (i. e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training.
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning.
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA).
A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs
Few attempts have been made for urban topography which is typically an integration of complex man-made and natural features.
A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation
The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution.
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment.
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function.
Short-term plasticity as cause-effect hypothesis testing in distal reward learning
Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related.
