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Found 24 papers, 13 papers with code
Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
We demonstrate experimental results with LLMs that address robotics task planning problems.
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
Behavioral Cloning via Search in Video PreTraining Latent Space
Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs
We study a novel neural architecture and its training strategies of speaker encoder for speaker recognition without using any identity labels.
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating.
The Transitive Information Theory and its Application to Deep Generative Models
Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regularization coefficient for disentangling representation but ultimately generating blurry examples.
Optimizing Tandem Speaker Verification and Anti-Spoofing Systems
As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve security.
Improving Behavioural Cloning with Human-Driven Dynamic Dataset Augmentation
Behavioural cloning has been extensively used to train agents and is recognized as a fast and solid approach to teach general behaviours based on expert trajectories.
Self-supervised Speaker Recognition with Loss-gated Learning
In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals.
Distilling Reinforcement Learning Tricks for Video Games
Reinforcement learning (RL) research focuses on general solutions that can be applied across different domains.
Multi-task Learning with Attention for End-to-end Autonomous Driving
This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.
Cost Sensitive Optimization of Deepfake Detector
Since the invention of cinema, the manipulated videos have existed.
General Characterization of Agents by States they Visit
Behavioural characterizations (BCs) of decision-making agents, or their policies, are used to study outcomes of training algorithms and as part of the algorithms themselves to encourage unique policies, match expert policy or restrict changes to policy per update.
Playing Minecraft with Behavioural Cloning
MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment.
Action Space Shaping in Deep Reinforcement Learning
In this work, we aim to gain insight on these action space modifications by conducting extensive experiments in video-game environments.
Benchmarking End-to-End Behavioural Cloning on Video Games
We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data.
An initial investigation on optimizing tandem speaker verification and countermeasure systems using reinforcement learning
The spoofing countermeasure (CM) systems in automatic speaker verification (ASV) are not typically used in isolation of each other.
Towards Debugging Deep Neural Networks by Generating Speech Utterances
One such debugging method used with image classification DNNs is activation maximization, which generates example-images that are classified as one of the classes.
SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data
In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model.
From Video Game to Real Robot: The Transfer between Action Spaces
Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training.
Who Do I Sound Like? Showcasing Speaker Recognition Technology by YouTube Voice Search
The popularization of science can often be disregarded by scientists as it may be challenging to put highly sophisticated research into words that general public can understand.
Audio and Speech Processing Sound
ToriLLE: Learning Environment for Hand-to-Hand Combat
We present Toribash Learning Environment (ToriLLE), a learning environment for machine learning agents based on the video game Toribash.
Staircase Network: structural language identification via hierarchical attentive units
Language recognition system is typically trained directly to optimize classification error on the target language labels, without using the external, or meta-information in the estimation of the model parameters.
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).
