Search Results for author:
Found 22 papers, 3 papers with code
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision.
Applying QNLP to sentiment analysis in finance
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage.
Weight Re-Mapping for Variational Quantum Algorithms
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications.
Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time.
Visual Transformers for Primates Classification and Covid Detection
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings.
Stochastic Market Games
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals.
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.
BioSimulators: a central registry of simulation engines and services for recommending specific tools
To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.
Meta Learning MDPs with Linear Transition Models
We study meta-learning in Markov Decision Processes (MDP) with linear transition models in the undiscounted episodic setting.
Quantifying Multimodality in World Models
In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models.
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Analysis of Feature Representations for Anomalous Sound Detection
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection.
Acoustic Leak Detection in Water Networks
In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment.
Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms
In many fields of research, labeled datasets are hard to acquire.
Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents
Meta-learning is a paradigm whereby an agent is trained with the specific goal of fast adaptation.
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
In industrial applications, the early detection of malfunctioning factory machinery is crucial.
Policy Entropy for Out-of-Distribution Classification
One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained.
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
The difficulty of mountainbike downhill trails is a subjective perception.
Soccer Team Vectors
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning.
Deep Neural Baselines for Computational Paralinguistics
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).
