Search Results for author:
Found 18 papers, 4 papers with code
Learning Multiple Sound Source 2D Localization
In this paper, we propose novel deep learning based algorithms for multiple sound source localization.
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts.
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources.
Q-learning with Language Model for Edit-based Unsupervised Summarization
Q-learning is leveraged to train the agent to produce proper edit actions.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos
In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles.
Spatially-weighted Anomaly Detection with Regression Model
Visual anomaly detection is common in several applications including medical screening and production quality check.
Spatially-weighted Anomaly Detection
Many types of anomaly detection methods have been proposed recently, and applied to a wide variety of fields including medical screening and production quality checking.
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods.
Constrained Exploration and Recovery from Experience Shaping
We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties.
Reinforcement Learning Testbed for Power-Consumption Optimization
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management.
Systems and Control
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series.
Internal Model from Observations for Reward Shaping
During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model.
Reward Estimation via State Prediction
We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.
Model-based imitation learning from state trajectories
We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories.
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World
While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment.
Deep Reinforcement Learning for High Precision Assembly Tasks
High precision assembly of mechanical parts requires accuracy exceeding the robot precision.
Conditional generation of multi-modal data using constrained embedding space mapping
We present a conditional generative model that maps low-dimensional embeddings of multiple modalities of data to a common latent space hence extracting semantic relationships between them.
