Search Results for author:
Found 13 papers, 8 papers with code
PAT: Pixel-wise Adaptive Training for Long-tailed Segmentation
First, the class-wise gradient magnitude homogenization helps alleviate the imbalance among label masks by ensuring equal consideration of the class-wise impact on model updates.
Revisiting LARS for Large Batch Training Generalization of Neural Networks
This paper explores Large Batch Training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings, uncovering insights.
TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction
While the Automatic Identification System (AIS) offers a rich source of information to address this task, forecasting vessel trajectory using AIS data remains challenging, even for modern machine learning techniques, because of the inherent heterogeneous and multimodal nature of motion data.
Structured Dropout Variational Inference for Bayesian Neural Networks
Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability.
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention.
Neuroevolution of Self-Interpretable Agents
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight.
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness.
Learning Latent Dynamics for Partially-Observed Chaotic Systems
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i. e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
Ranked #2 on
Acoustic Novelty Detection
on A3Lab PASCAL CHiME
BiasedWalk: Biased Sampling for Representation Learning on Graphs
We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks.
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
In a world of global trading, maritime safety, security and efficiency are crucial issues.
